How Crosswalk Affordances Influence Pedestrian Vigilance and Safety Perception

How Crosswalk Affordances Influence Pedestrian Vigilance and Safety Perception | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article How Crosswalk Affordances Influence Pedestrian Vigilance and Safety Perception Hyunjoo Eom, Jinho Won, Gi-Hyoug Cho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7979619/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines how pedestrian behavior and risk perception are shaped by crosswalk infrastructure through two distinct pathways: direct environmental affordances and perception-mediated associations. Using immersive virtual reality (VR) simulations of urban crossings, participants completed multiple trials measuring head scanning, yielding, crossing speed, and perceived safety and collision risk. Linear mixed-effects models and path-decomposition analyses were employed to assess whether behavioral differences across crosswalk types were consistent with perception-mediated processes. Results indicate that zebra and interactive crosswalks significantly increased perceived safety and reduced perceived collision risk. However, behavioral adaptations—reduced scanning, lower yielding, and slower speed—were largely independent of these perceptual shifts. The decomposition analysis revealed a suppression pattern: safety perception encouraged scanning, while crosswalk affordances directly reduced vigilance, suggesting that implicit cues outweighed conscious appraisal. These findings imply that pedestrian behavior is primarily guided by automatic affordance-based mechanisms rather than deliberate perception, highlighting a potential false-safety effect in crosswalk design. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Despite continuous efforts to improve pedestrian infrastructure and enforce crossing regulations, pedestrian fatalities remain high. Approximately one-third of crosswalk-related accidents are linked to pedestrian inattention, such as unsafe gap acceptance or failure to monitor approaching vehicles (Ko et al. 2015). These persistent risks suggest that physical design improvements alone are insufficient if they do not account for how pedestrians perceive and respond to their environments. Previous studies have shown that decision-making and behavioral adaptation play a critical role in pedestrian safety, particularly at intersections where pedestrian–vehicle interactions are complex (Hamann et al., 2017; Herrero-Fernández et al., 2016 ; Zhou & Horrey, 2010). However, it is still unclear whether these behavioral adaptations arise from conscious risk evaluation or from automatic, environment-driven responses. Much of the existing research has framed pedestrian decision-making through the lens of cognitive theories, particularly Endsley’s ( 1995 ) model of situation awareness (SA), which emphasizes how individuals perceive, comprehend, and project environmental information to guide behavior. Yet this model primarily explains the processing of information rather than the translation of awareness into action. In contrast, ecological theories such as Gibson’s ( 1977 ) affordance framework argue that environmental structures directly elicit behavioral responses, often bypassing conscious reasoning. For instance, road geometry, markings, or lighting conditions can shape movement patterns automatically, without deliberate evaluation of risk. This theoretical tension—between cognition-based and affordance-based perspectives—has significant implications for pedestrian safety interventions. If pedestrian behavior is largely guided by implicit affordances, design improvements that focus only on altering perceived safety may fail to produce the intended behavioral outcomes. Bendak et al. ( 2021 ) found that crosswalk markings and signal timing at signalized intersections significantly affected pedestrian compliance and waiting behavior, while Li et al. ( 2022 ) demonstrated that enforcement cameras at unsignalized crossings not only improved driver yielding but also altered pedestrians’ risk perception. These findings suggest that both conscious perception and automatic affordance cues shape crossing behavior, underscoring the need to examine their relative influence. Accordingly, this study investigates how crosswalk infrastructure shapes pedestrian behavior through two distinct pathways: a perception-mediated process, where conscious risk and safety evaluations influence actions, and a direct affordance-based process, where environmental cues trigger automatic responses. Using immersive VR experiments, we examine whether pedestrian micro-behaviors—head scanning, yielding, and crossing speed—are driven by perceived safety and collision risk or by implicit affordance cues embedded in crosswalk design. By integrating situation awareness and affordance theories, this study contributes to a more comprehensive understanding of pedestrian decision-making and offers insights for designing crossings that enhance both physical protection and behavioral vigilance. 2. Literature Review 2.1 Cognitive Foundations of Pedestrian Decision-Making Understanding pedestrian behavior requires examining how individuals perceive and interpret environmental risk cues before acting. Early safety research, initially focused on drivers, evolved to acknowledge pedestrians as active decision-makers who consciously assess their surroundings and adjust behavior accordingly (Cohen et al. 1955; Kastenbaum and Briscoe 1975; Repetto-Wright 1977 ). Subsequent studies confirmed that pedestrians evaluate vehicle speed, distance, and signal timing when determining when to cross, and that these judgments vary by cognitive ability, age, and experience (Keegan and O’Mahony 2003; Ishaque and Noland 2008; Dommes and Cavallo 2011; Herrero-Fernández et al. 2016 ; Havard and Willis 2012). These findings underscore that perception and decision-making are interdependent, not only a matter of compliance or infrastructure. Endsley’s ( 1995 ) Situation Awareness (SA) theory remains central to explaining how these cognitive mechanisms unfold. SA describes three progressive stages—perception, comprehension, and projection—through which individuals extract and interpret environmental information to anticipate events and guide behavior. In pedestrian contexts, these processes determine when individuals initiate crossing, adjust speed, or abort movement. For instance, pedestrians who perceive high collision risk often wait longer or cross more cautiously, reflecting deliberate risk management (Cœugnet et al. 2019; Butler et al. 2016 ). Yet, the predictive power of risk perception varies: contextual influences such as traffic density, social norms, and habitual exposure can override deliberate cognition (Liu et al. 2021 ). These mixed findings reveal both the utility and the limitations of cognitive approaches. While perception and reasoning underpin many safety behaviors, real-world decision-making is rarely purely rational. Rapid, habitual responses often occur before full cognitive evaluation. This recognition provides a conceptual bridge to ecological perspectives, where behavior is viewed as the product of continuous interaction between perception, affordances, and environmental feedback rather than a sequence of detached mental calculations. 2.2 Ecological and Behavioral Adaptation Perspectives While cognitive theories emphasize deliberate information processing, ecological perspectives highlight the spontaneous, often unconscious, nature of human-environment interaction. Gibson’s ( 1977 ) affordance theory argues that environmental structures directly specify possible actions, allowing people to respond automatically to cues in their surroundings. For pedestrians, elements such as crosswalk markings, lane width, medians, and refuge islands act as affordances that communicate where movement is safe or restricted. These cues guide locomotion not through deliberate reflection but through immediate perceptual pickup—linking perception directly to behavior. A complementary framework, Wilde’s ( 1982 ) risk homeostasis theory, extends this ecological logic by suggesting that individuals regulate their behavior to maintain a preferred level of perceived risk. When safety measures reduce perceived danger—through brighter lighting, enhanced markings, or signalization—people may unconsciously take compensatory risks, offsetting the intended benefits of design interventions. Empirical studies support this adaptive tendency. Koh et al. ( 2014 ) found that safety improvements, such as wider medians or shorter crossing lengths, encouraged some pedestrians to accept smaller gaps or violate signals, consistent with risk homeostasis principles. This evidence highlights that safer environments can paradoxically diminish vigilance if they over‑stabilize expectations of safety. Recent ecological research also demonstrates that affordance cues can work beneficially when they clearly articulate movement possibilities. For instance, well‑demarcated crosswalks, textured pavements, and refuge spaces enhance predictability for both drivers and pedestrians, reducing ambiguity and conflict. These features do not simply increase subjective feelings of safety; they recalibrate behavior by restructuring how pedestrians perceive the flow of vehicles and space available for movement. In this sense, behavioral adaptation is not purely compensatory—it can be constructive when affordances reinforce appropriate actions. Understanding this balance between adaptive risk‑taking and guidance‑driven adjustment is central to designing crossings that encourage active attention rather than passive reliance on infrastructure. 2.3 Integrating Perception and Affordance: Evidence from Experiments and Simulations A growing body of experimental and simulation-based studies has advanced understanding of how perception and affordance jointly shape pedestrian behavior. Virtual reality (VR) experiments, in particular, have provided a means to manipulate environmental conditions with high precision while maintaining ecological realism. This methodology allows researchers to investigate how pedestrians respond to specific design features—such as crosswalk markings, lighting, traffic speed, or driver type—while observing detailed indicators of decision-making such as waiting time, head scanning, and crossing speed. Findings from these VR studies demonstrate that pedestrians continuously adapt their behavior to changing risk contexts through both conscious and automatic processes. Luu et al. ( 2022 ) found that participants crossing narrow streets in VR displayed longer hesitation and more extensive visual scanning when the environment appeared risky, indicating that conscious risk appraisal guided cautious strategies. Kwon et al. ( 2022 ) expanded on this by showing that risk perception influences two behavioral stages differently: it increases caution during the decision phase but triggers urgency once the crossing begins, resulting in faster movement and less stable gait. This stage-dependent response supports the idea that pedestrian behavior reflects both cognitive assessment and reactive adaptation. Beyond risk perception, VR studies have also highlighted the role of spatial affordances. Joo et al. ( 2023 ) observed that the inclusion of medians and refuge islands improved crossing success rates and reduced collisions without significant changes in perceived safety, implying that the environment itself can guide safer actions through affordance mechanisms. Similarly, Yang et al. ( 2024 ) found that head movements toward oncoming autonomous vehicles functioned as implicit communication cues, enabling coordination even when explicit perception of danger was low. Together, these studies demonstrate that pedestrian behavior arises from the dynamic interplay between conscious perception and implicit affordance cues. Cognitive appraisal helps pedestrians anticipate risk, while affordances embedded in infrastructure elicit immediate adjustments in movement and vigilance. However, despite the growing evidence base, few studies have quantitatively compared the relative influence of these mechanisms. The present study addresses this gap using a multilevel path-decomposition framework to distinguish between direct environmental effects and perception-mediated associations, offering a more integrated understanding of how crosswalk design shapes pedestrian awareness, confidence, and safety performance. Pedestrian crossing behavior is shaped by both deliberate cognitive assessments and automatic environmental responses. While cognitive models explain conscious evaluations of risk, ecological approaches capture implicit, affordance-driven adjustments. The present study integrates these perspectives within a unified experimental framework to examine how crosswalk infrastructure influences both perceptual and behavioral dimensions of pedestrian safety. 3. Methods 3.1 Participants Forty participants (25 men, 15 women; M = 22.2 years, SD = 3.3, range = 18–31) were recruited from a university campus through bulletin boards and social media postings. Eligibility criteria required participants to (1) be aged 19 years or older, (2) have normal or corrected-to-normal vision, and (3) report no prior adverse reactions to virtual-reality (VR) exposure. Sixty percent had previous VR experience, 60 % held a driver’s license, and 10 % reported prior involvement in a pedestrian-related traffic incident. Participants received USD 25 for approximately 45 minutes of participation (including setup, practice, trials, and debriefing). The study protocol was reviewed and approved by the Institutional Review Board of the Ulsan National Institute of Science and Technology (UNISTIRB-23-042-A). All participants provided written informed consent prior to participation. 3.2 Apparatus The study was conducted using an HTC Vive Pro head-mounted display (2,448 × 2,448 pixels per eye; 120 Hz; 120° field of view) equipped with VIVE Tracker 3.0 sensors attached to participants’ ankles and waist to capture full-body motion. The virtual environment was built in Unity 3D (version 2021.3 LTS) using the SteamVR SDK and deployed within a 10 m × 10 m tracked area, allowing participants to move naturally. Custom C# scripts continuously recorded head position, rotation, pedestrian coordinates, and timestamped behavioral events. Participants physically crossed the virtual road whenever they judged it safe to do so. Following each trial, they rated their perceived safety and collision risk using an in-VR handheld interface. Integrated spatial audio reproduced realistic traffic sounds and ambient noise, while all sessions were video-recorded for post-hoc data validation and quality assurance. 3.3 Virtual Reality Setup and Experimental Design The virtual environment simulated a two-lane urban road (7 m wide, 3.5 m per lane) bordered by sidewalks, bus stops, and commercial buildings to enhance realism (Figure 1). The experiment followed a fully within-subject factorial design comprising five manipulated factors: (1) crosswalk type (unmarked, zebra, interactive LED), (2) driver type (automated vs. human-controlled), (3) vehicle speed (30 vs. 50 km/h), (4) lighting condition (day vs. night), and (5) time-to-collision (TTC) auditory prompt (2 s vs. 5 s). Each participant completed 20 randomized trials that counterbalanced all experimental conditions to mitigate order effects. The interactive LED crosswalk featured embedded lighting that dynamically responded to traffic and pedestrian proximity. As shown in Figure 2, the LEDs displayed three colors: yellow when a vehicle approached within 10 m, green when the pedestrian entered the crosswalk (indicating right-of-way), and red when a vehicle’s TTC fell below one second, signaling danger. This responsive design simulated emerging smart crosswalk systems and operationalized the concept of environmental affordance by providing perceptual cues that guided crossing behavior. Two vehicle types were implemented: automated and human-driven vehicles. Automated vehicles were programmed with consistent yielding behavior, set to decelerate when detecting pedestrians within 10 meters and fully yield when pedestrians initiate crossing. Human-driven vehicles were controlled by recruited drivers connected to the VR environment in real-time, providing naturalistic and variable responses to pedestrian behavior. Same visual appearance to participants. This approach ensured that participants experienced both predictable automated vehicle interactions and the unpredictability characteristic of human drivers, enhancing the ecological validity of the crossing scenarios. Environmental visibility conditions alternated between daytime and nighttime scenarios with appropriate lighting adjustments. Traffic speed varied between residential zones (30 km/h) and arterial road speeds (50 km/h). Additionally, auditory crossing signals were implemented based on time-to-collision (TTC) values, with a beep sound prompting pedestrians to cross at either 2 seconds or 5 seconds TTC, simulating different crossing opportunity windows. This signal manipulation allowed examination of how temporal pressure influences crossing decisions and safety perceptions. 3.4 Procedure Upon arrival, participants were provided with an overview of the experiment process. After reviewing and signing the informed consent form, participants completed a demographic and screening form to confirm eligibility, including questions about age, gender, driving experience, VR experience, and any history of motion sickness or visual impairments. Following the pre-experiment questionnaires, participants were fitted with the HTC Vive Pro headset and VIVE trackers on their ankles and waist. Before the main experiment, participants completed two practice trials to familiarize themselves with the VR environment, crossing mechanics, and in-VR questionnaire system. The experimenter confirmed participants' comfort with the VR system before proceeding. The main experiment consisted of 20 randomized crossing trials: 10 with automated vehicles and 10 with human-controlled vehicles. Pedestrians were instructed to approach and cross the road when they felt it was safe to cross, with no time constraints. After each crossing, participants remained in VR to complete ratings of perceived environmental safety and collision risk using the handheld controller. Between trials, participants returned to the starting position while the next scenario loaded. To ensure participant comfort and minimize VR-induced fatigue, the experimenter asked participants to verbally confirm their readiness before initiating each new trial. After completing all trials, participants removed the VR equipment and completed post-experiment questionnaires asking their attitude toward crossing, including risk-taking and safety consciousness, and overall experience with the VR system. The session concluded with a debriefing where participants could ask questions and were informed about the study's objectives. Each experimental session lasted approximately 30 minutes. 3.5 Measures To examine the sequential relationship between environment, perception, and behavior, data were collected across perceptual and behavioral dimensions in each trial. The environment–perception–behavior framework followed Endsley’s (1995) model of situation awareness, where environmental features influence perception and comprehension of risk, which in turn guide decisions and actions. 3.5.1 Perceptual measures Perceptual variables were obtained through two in-VR questionnaire items administered after each crossing: (1) perceived safety of the road environment at the time of crossing (0 = "not safe at all" to 10 = "very safe"), and (2) perceived collision risk with vehicles at the time of crossing (0 = "not risky at all" to 10 = "very risky"). These ratings captured participants’ conscious evaluation of the crossing environment and vehicle behavior. Collecting these measures post-trial preserved immersion and prevented interruption of natural decision-making. Participants were instructed to base their responses on their immediate experience during the crossing rather than retrospective judgment. 3.5.2 Behavioral measures Behavioral indicators were derived directly from motion-tracking data. Head movement angle (horizontal rotation, in degrees) quantified scanning activity, with larger values indicating broader visual monitoring. Yielding behavior represented binary crossing decisions: waiting for vehicles to pass (coded 1) or crossing first (coded 0). Crossing speed was calculated as road width divided by crossing duration, measured from step initiation to reaching the opposite curb. These variables provided objective metrics of pedestrian vigilance, assertiveness, and efficiency. 3.5.3 Individual attitude measures Post-experiment questionnaires assessed individual differences in crossing attitudes. Principal component analysis of six items identified two distinct dimensions explaining 67.4% of total variance. The first component, Risk-Taking Tendency (46.4% variance), included items related to jaywalking and crossing outside designated zones. The second, Safety Consciousness (21.0% variance), reflected cautiousness and accident avoidance. These factor scores served as covariates in the subsequent analyses to account for stable inter-individual traits influencing both perception and behavior. Table 1: Principal Component Analysis of Pedestrian Attitude Measures Variable Comp1 Comp2 Risk-Taking Tendency I don't use crosswalks when in a hurry 0.53 -0.05 I cross without crosswalks when no cars 0.48 0.1 I often jaywalk 0.54 -0.1 I take risky actions to save time 0.39 -0.34 Safety Conscious I'm less likely to be involved in accidents 0.1 0.71 I'm more cautious than others 0.17 0.6 Component Summary Eigenvalue 2.78 1.26 % of total variance explained 46.40% 21.00% 3.6 Analytical Framework This study employed a path-decomposition analytical framework to examine how crosswalk design influences pedestrian behavior both directly and indirectly through perception of safety and collision risk. The conceptual model (Figure 3) draws from Endsley's (1995) situation awareness theory, viewing pedestrian decision-making as a sequential process in which environmental features shape perceived risk and safety, which in turn are associated with behavioral responses. Rather than implying strict causal mediation, this framework identifies associational pathways that are statistically consistent with perception-mediated effects. The model decomposes the total effect of crosswalk design into three pathways: (a) the effect of environment on perception, representing how different crosswalk types influence perceived safety and collision risk; (b) the effect of perception on behavior, capturing how risk assessment translates into crossing actions; and direct effect (c') the effect of the direct environment on behavior, reflecting behavioral differences that persist after accounting for perception. The indirect effect (a × b) represents the degree to which variations in behavior are consistent with perception-mediated mechanisms. This approach quantifies the relative contributions of perception and affordance in shaping pedestrian actions within the VR environment. Because perceptual measures were collected immediately after each crossing, results should be interpreted as cross-sectional associations rather than causal mediation. However, the repeated within-subject design enhances internal consistency and allows for robust comparisons of perceptual and behavioral patterns across environmental conditions. 3.7. Statistical Analysis Linear mixed-effects models were employed to examine whether crosswalk design influences pedestrian behavior directly or indirectly through risk perception, while accounting for repeated observations within participants. Three models were estimated: Model 1 (Environment → Perception): Perceptionᵢⱼ = β₀ + β₁(Crosswalk typeᵢⱼ) + Controls + uᵢ + εᵢⱼ Model 2 (Environment → Behavior): Behaviorᵢⱼ = γ₀ + γ₁(Crosswalk typeᵢⱼ) + Controls + vᵢ + εᵢⱼ Model 3 (Perception + Environment → Behavior): Behaviorᵢⱼ = δ₀ + δ₁(Crosswalk typeᵢⱼ) + δ₂(Perceptionᵢⱼ) + Controls + wᵢ + εᵢⱼ where uᵢ, vᵢ, and wᵢ ~ N(0, σ²ᵤ) are random intercepts for participant i, εᵢⱼ represents the residual error, and crosswalk type was treated as a categorical variable (0 = no crosswalk, 1 = zebra crossing, 2 = interactive crossing). Control variables included demographic factors (age, gender, driver's license status, VR experience, crash history) and individual attitude components (risk-taking tendency (PC1), and safety consciousness (PC2)). Interaction terms between crosswalk type and attitude factors (Crosswalk × PC1, Crosswalk × PC2) were tested to assess heterogeneity in perceptual and behavioral responses. Indirect (a × b) effects were estimated using the Monte Carlo method for multilevel data, providing confidence intervals around the decomposed effects. All analyses were conducted in a mixed-modeling framework to handle unbalanced repeated measures and correlated residuals. To further validate interpretability, sensitivity analyses were performed to ensure that inclusion or exclusion of perceptual covariates did not alter the direction or significance of the main behavioral effects. The results were thus interpreted as evidence of statistical consistency with perception-mediated processes, rather than definitive causal mediation. 4. Results 4.1. Descriptive results Table 2 summarizes participant characteristics and study variables. The study included 40 participants and 777 observations (97.1% of the original 800 trials). Since the experiment was conducted within a campus, participants were predominantly young adults, with approximately two-thirds being male and holding driver's licenses. Most had prior VR experience, while only 10.1% reported previous traffic accident experience. Regarding perceptual measures, participants reported moderate levels of perceived safety (M = 6.09, SD = 2.73) and perceived collision risk (M = 3.18, SD = 2.51) on 0–10 scales, indicating generally favorable safety perceptions across experimental conditions. Individual difference measures revealed substantial variation in pedestrian attitudes. Risk-taking tendency (PC1) showed the widest spread (SD = 1.67, range: -3.10 to 3.74), while safety consciousness (PC2) demonstrated more moderate variation (SD = 1.11, range: -2.44 to 2.04). Both principal component scores were standardized with means of zero. To examine whether perceptions and behaviors differed across crosswalk type, one-way ANOVAs were conducted. In Table 3 , safety perceptions at zebra crossings were rated as safest (M = 6.69), followed by interactive crossings (M = 6.15), with unmarked locations perceived as least safe (M = 5.41, p < .001). Collision risk perception differed significantly across conditions (p < .001), all values remained relatively low on the 0–10 scale, with unmarked locations rated highest (M = 3.65) and zebra crossings lowest (M = 2.75). This suggests that participants generally perceived the VR environment as relatively safe across all conditions, though unmarked locations were seen as comparatively riskier. Pre-crossing behaviors also varied significantly by crosswalk type. Head rotation angles were greatest at unmarked locations (M = 41.06), indicating more extensive visual scanning when crossing infrastructure was absent, compared to zebra (M = 35.90) or interactive crossings (M = 38.16, p < .001). Yielding rates were highest at unmarked locations (87.7%) and lowest at zebra crossings (75.7%, p < .01), suggesting that marked crosswalks increased pedestrians' crossing assertiveness. Crossing speeds were fastest at unmarked locations compared to both zebra and interactive crossings. Taken together, these descriptive results suggest that crosswalk infrastructure shapes both perceptions and pre-crossing behaviors: when infrastructure conveys greater safety, pedestrians reduce scanning, yield less frequently, and walk more slowly. Table 2 Participant Characteristics Variable Unit Mean Std. Dev. Min Max Perception Perceived safety of environment 0–10 scales 6.089 2.733 0 10 Perceived collision risk 0–10 scales 3.182 2.514 0 10 Pedestrian Behavior Head rotation angle Degrees 38.462 11.788 2.231 77.761 Pedestrian yield Binary 0.820 0.385 0 1 Crossing Speed km/h 4.708 1.077 1.858 10.26 Attitudes Risk-Taking Tendency (PC1) Factor score 0.008 1.669 -3.102 3.743 Safety Conscious (PC2) Factor score 0.000 1.109 -2.442 2.040 Demographic characteristics Age (years) Years 22.212 3.318 18 31 Gender (male) Binary (1 = yes) 0.630 0.483 0 1 Driver's license Binary (1 = yes) 0.606 0.489 0 1 Prior VR experience Binary (1 = yes) 0.594 0.491 0 1 Accident experience Binary (1 = yes) 0.101 0.301 0 1 We next estimated linear mixed-effects models to account for within-participant correlations across multiple crossings and to control for demographic characteristics and crossing-related attitudes. The following sections present results from sequential mixed-effects models and a mediation analysis, which together assess whether crosswalk design influences pedestrian behavior directly or indirectly through perceptions of safety and collision risk. Table 3 Difference of perception and behaviors across crosswalk type (ANOVA) No crosswalk Zebra crosswalk Interactive crosswalk P-value Perception Perceived safety of environment 5.41 6.69 6.15 *** Perceived collision risk 3.65 2.75 3.12 *** Behavior before crossing Head rotation angle 41.06 35.90 38.16 *** Pedestrian yield .877 .757 .813 ** Crossing Speed 4.89 4.60 4.64 ** Note: ***p < 0.001; **p < 0.01; *p < 0.05 4.2. Effect on perception (Psychological Responses) Table 4 presents results from Model 1, examining the effects of crosswalk type, individual attitudes, and experimental design variables on pedestrian safety and collision risk perceptions. Both zebra and interactive crosswalks significantly increased perceived safety compared to unmarked roads (β = 1.426 and 1.127, respectively, both p < .001) and decreased perceived collision risk (β = -1.112 and − 0.735, both p < .001). These findings indicate that crosswalk infrastructure consistently improves pedestrian safety perceptions and reduces perceived risk level. Table 4 Effects on perception Perceived safety of environment Perceived collision risk Coef. S.E. Coef. Std. Crosswalk Type Zebra crosswalk (reference = no crosswalk) 1.426 *** 0.153 -1.112 *** 0.157 Interactive crosswalk (reference = no crosswalk) 1.127 *** 0.157 -0.735 *** 0.161 Attitude PC1 (Risk-taking) 0.283 0.204 -0.532 *** 0.164 PC2 (Safety-conscious) -0.222 0.300 -0.399 + 0.242 Interaction (Crosswalk Type # Attitude) Zebra # PC1 (Risk-taking) -0.591 *** 0.091 0.402 *** 0.093 Interactive # PC1 (Risk-taking) -0.427 *** 0.095 0.21 * 0.097 Zebra # PC2 (Safety-conscious) 0.684 *** 0.136 0.146 0.140 Interactive # PC2 (Safety-conscious) 0.523 *** 0.141 0.265 + 0.144 Individual Characteristics Age 0.14 0.101 -0.118 0.080 Gender (male = 1) 0.225 0.645 -0.402 0.508 Driver's license (yes = 1) 0.116 0.633 -0.503 0.498 VR experience (yes = 1) -0.715 0.684 0.048 0.538 Crash experience (yes = 1) 1.533 1.091 -0.085 0.859 Experimental Design Driver type (0 = npc; 1 = human) 0.557 *** 0.126 -0.369 ** 0.129 Experiment number 0.029 0.022 -0.056 * 0.022 Night -0.483 *** 0.128 0.347 ** 0.131 Speed limit (0 = 30km/h, 1 = 50km/h) -0.278 * 0.127 0.431 *** 0.130 Singal to cross (0 = ttc 2s, 1 = ttc 5s) 0.16 0.128 -0.093 0.131 Intercept 2.062 2.119 7.096 *** 1.675 LR Test vs. Linear Model 415.65 (p < .001) 245.96 (p < .001) ICC 0.503 0.364 Marginal R² (fixed effects) 0.205 0.217 Conditional R² (total model) 0.605 0.503 Obs.(participants) 777 (40) 777 (40) Note: ***p < 0.001; **p < 0.01; *p < 0.05; +p < 0.1 Individual attitudes also showed higher risk-taking tendency (PC1) was associated with lower perceived collision risk (β = -0.532, p < .001), while safety consciousness (PC2) was marginally associated with lower collision risk perception (β = -0.399, p < .10). The results also show significant crosswalk and attitude interactions, indicating that the effects of crossing infrastructure on risk perception vary by individual differences. The positive effects of both zebra and interactive crosswalks on perceived safety were diminished among high risk-takers, but enhanced among safety-conscious individuals (β = 0.684 and 0.523, both p < .001). A similar pattern emerged for perceived collision risk. Risk-reducing effects of crosswalks were smaller for risk-taking individuals, while marginally stronger for safety-conscious pedestrians. These interactions suggest that risk-taking individuals derive less perceptual benefit from crossing infrastructure, while safety-conscious individuals show heightened sensitivity to environmental safety cues. Experimental design variables showed expected patterns. Human-controlled vehicles increased perceived safety while decreasing collision risk perception, likely because human drivers exhibited more predictable yielding behavior than non-player-controlled (programmed) vehicles. Nighttime conditions decreased perceived safety. Higher speed limits marginally decreased perceived safety (β = -0.278, p < .05) and significantly increased collision risk perception (β = 0.432, p < .001), indicating that participants were sensitive to the faster-approaching vehicles. Despite substantial between-person variation, demographic characteristics did not significantly predict risk perception. This suggests that unexplained heterogeneity may reflect unmeasured factors such as habitual behaviors, or prior experiences with traffic environments. Figure 4 illustrates the interaction effects between crosswalk type and individual differences on risk perception. For perceived safety (top panels), high risk-takers showed minimal differentiation across crosswalk types, while low risk-takers and safety-conscious individuals demonstrated greater sensitivity to crossing infrastructure, with marked improvements at zebra crossings. For collision risk perception (bottom panels), all groups perceived lower risk at crosswalks compared to unmarked locations, but the magnitude of this effect was attenuated for high risk-takers. Interaction effects between crosswalk type and individual differences are shown in Fig. 4 . These patterns suggest that crosswalk design interventions will have differential effectiveness across pedestrian populations, with safety infrastructure providing the greatest behavioral benefits for individuals already predisposed toward cautious behavior, while showing limited impact on risk-taking pedestrians who may rely more on direct environmental cues rather than infrastructure-based risk assessment. These findings indicate that crosswalk design benefits are not uniformly distributed across all pedestrian types, with safety-oriented individuals deriving the greatest perceptual benefits from enhanced crossing infrastructure. 4.3. Effect on behavior The results of the model examining the effects of perception on pedestrian behavior are presented in Table 5 . Both zebra crosswalks and interactive crosswalks significantly reduced head rotation angles compared to no crosswalk conditions, indicating that crosswalks lead to decreased visual scanning. These crosswalks also decreased the odds of pedestrians yielding to vehicles, with zebra crosswalks showing a 69% reduction in yielding (odds ratio of 0.306) and interactive crosswalks showing a 62% reduction (odds ratio of 0.385), while also reducing crossing speeds. Individual attitudes played important roles in shaping behavior. Safety-conscious individuals demonstrated increased head rotation and slower crossing speeds, while risk-taking tendency showed no significant main effects on any behavioral outcomes. However, interaction effects revealed that high risk-takers maintained better visual scanning at crosswalks, showing smaller reductions in head rotation at both zebra and interactive crosswalks compared to low risk-takers. Perceptual factors were also significant predictors of behavior. Higher safety perception was positively associated with head rotation, suggesting that pedestrians who feel safer actually engage in more vigilant scanning behavior. Collision risk perception increased yielding behavior, indicating that pedestrians who perceive higher collision risk are more likely to yield to approaching vehicles. These findings suggest that crosswalks create complex behavioral changes where pedestrians both reduce their vigilance (less head scanning), reduce their yielding to vehicles, and slower crossing speed. Table 5 Effects on behavior Head rotation angle Pedestrian yield Crossing speed Coef. S.E. Odds S.E. Coef. S.E. Environmental Characteristics Zebra crosswalk (reference = no crosswalk) -5.902 *** 0.772 0.306 *** 0.111 -0.260 *** 0.077 Interactive crosswalk (reference = no crosswalk) -3.304 *** 0.769 0.385 ** 0.141 -0.224 ** 0.077 Attitude Risk-taking tendency -0.664 0.752 1.01 0.311 0.098 0.076 Safety-conscious 2.293 * 1.102 0.963 0.458 -0.264 * 0.112 Interaction (Crosswalk Type # Attitude) Zebra # PC1 (Risk-taking) 0.891 * 0.443 1.202 0.217 -0.033 0.044 Interactive # PC1 (Risk-taking) 0.808 + 0.455 1.226 0.23 0.013 0.046 Zebra # PC2 (Safety-conscious) -0.466 0.657 1.703 + 0.539 0.061 0.066 Interactive # PC2 (Safety-conscious) -0.046 0.675 1.232 0.4 0.088 0.068 Perception Safety perception 0.391 * 0.171 1.05 0.083 -0.014 0.017 Collision risk perception 0.036 0.17 1.232 ** 0.088 -0.019 0.017 Individual Characteristics Age 0.111 0.363 1.138 0.174 0.031 0.037 Gender (male = 1) -1.742 2.302 1.108 1.028 -0.263 0.234 Driver's license (yes = 1) 4.502 * 2.258 0.712 0.665 0.038 0.23 VR experience (yes = 1) 1.81 2.442 2.658 2.594 -0.182 0.249 Crash experience (yes = 1) 5.921 3.901 0.277 0.441 -0.199 0.397 Experimental Conditions Driver type (0 = npc; 1 = human) 1.176 + 0.603 0.312 *** 0.086 -0.037 0.06 Experiment number 0.282 ** 0.104 1.05 0.048 0 0.01 Night 0.642 0.61 1.155 0.304 -0.034 0.061 Speed limit (0 = 30km/h, 1 = 50km/h) 0.606 0.607 1.058 0.276 -0.001 0.061 Singal to cross (0 = ttc 2s, 1 = ttc 5s) 7.366 *** 0.604 0.061 *** 0.021 0.045 0.061 Intercept 26.722 *** 7.705 3.795 12.224 4.619 *** 0.784 LR Test vs. Linear Model (Logistic Model for Yield) 231.65 (p < .001) 131.56 (p < .001) 241.72 (p < .001) ICC 0.356 0.638 0.364 Marginal R² (fixed effects) 0.259 0.281 0.091 Conditional R² (total model) 0.523 0.73 0.421 Obs.(participants) 777 777 777 4.4. Mediation analysis Table 6 presents the mediation analysis examining how crosswalk influences pedestrian behavior through risk and safety perception as mediating pathways. Path a (environment → perception) demonstrates that consistent with earlier results (Table 4 ), both zebra and interactive crosswalk types significantly increased safety perceptions compared to unmarked roads. Zebra crossings showed the strongest effects, increasing perceived safety (β = 1.426, p < .001) and reducing collision risk perception (β = -1.112, p < .001). Interactive crossings showed similar but slightly smaller effects on both perception measures. Path b (Perception → Behavior) reveals that higher perceived safety increased head rotation (β = 0.391, p < .05), indicating that increased safety perception led to more visual scanning. While this finding is counterintuitive to risk compensation theory (or behavioral adaptation theory, in which people maintain a target level of risk, so when they feel safer, they engage in riskier behaviors), increased head movement in safe environments may reflect exploratory information-seeking behavior or confirmatory scanning as a form of proactive engagement. Supporting this interpretation, Yang et al. ( 2024 ) found that pedestrians increase head-turns for a "last-second check" before crossing initiation. In safer environments, pedestrians have greater temporal and cognitive capacity, enabling more systematic and thorough visual exploration. Conversely, when feeling unsafe, pedestrians may employ "defensive non-looking" as a protective strategy, either to minimize anxiety from threatening stimuli or to signal non-crossing intentions to drivers. Thus, increased scanning in safer conditions reflects a shift from avoidance-motivated behavior to approach-motivated exploration aimed at enhancing situational awareness. Regarding the effect of perception on pedestrian yielding behavior, safety perception showed no significant effect, but collision risk perception significantly increased the odds of yielding to vehicles (odds ratio of 1.232). This indicates that pedestrians base their yielding decisions on risk assessment rather than environmental safety. Path c (Total Effects) shows that crosswalks significantly influence pedestrian behaviors. Zebra crossings significantly reduced head rotation and crossing speed, while reducing the odds of pedestrian yielding. Path c′ (Direct Effects ) reveals that these behavioral changes largely remain after controlling perception. This pattern reveals two opposing mechanisms that are simultaneously at play. The positive indirect path (perceived safety → increased head rotation) suggests that psychological safety promotes visual exploration. However, the stronger negative direct path indicates that physical infrastructure reduces scanning needs through environmental predictability, allowing cognitive resource conservation. In other words, while crosswalks do increase safety perception, and safety perception does encourage scanning behavior for confirmatory or proactive engagement, the direct affordance effect of crosswalk overwhelms this cognitive pathway. The presence of designated crosswalk reduces the need for extensive scanning, resulting in decreased head movement in marked crosswalks. When perception is held constant, pedestrians scan less in crosswalks. This reduction is not due to conscious cognitive perception of feeling safer, but rather by an implicit sense of security and predictability afforded by the crosswalk infrastructure itself, which reduces the need for vigilance. By contrast, when pedestrians perceive greater safety, they engage in more extensive visual exploration of the environment compared to when they feel less safe to cross. Table 6 Summary of Mediation Analysis Results Pathway Coef. S.E. Pathway Coef. S.E. Environment → Perception (a) (Direct Effects) Zebra → Safety perception 1.426*** 0.153 Zebra → Collision risk perception -1.112*** 0.157 Interactive → Safety perception 1.127*** 0.157 Interactive → Collision risk perception -0.735*** 0.161 Perception → Behavior (b) (Direct Effects) Safety perception → Head rotation angle 0.391* 0.171 Collision risk perception → Head rotation angle 0.036 0.170 Safety perception → Pedestrian yield 1.050 0.083 Collision risk perception → Pedestrian yield 1.232** 0.088 Safety perception → Crossing speed -0.014 0.017 Collision risk perception → Crossing speed -0.019 0.017 Environment → Behavior (c) (Total Effects) Zebra → Head rotation angle -5.386*** 0.726 Zebra → Pedestrian yield 0.292*** 0.096 Zebra → Crossing speed -0.259*** 0.073 Interactive → Head rotation angle -2.893*** 0.746 Interactive → Pedestrian yield 0.375** 0.128 Interactive → Crossing speed -0.226** 0.074 Environment → Behavior (c') (Direct effects after controlling for perception) Zebra → Head rotation angle -5.902*** 0.772 Zebra → Pedestrian yield 0.306*** 0.111 Zebra → Crossing speed -0.260*** 0.077 Interactive → Head rotation angle -3.304*** 0.769 Interactive → Pedestrian yield 0.385** 0.141 Interactive → Crossing speed -0.224** 0.077 Note: 40 participants and 777 observations. Coefficients for yielding behavior represent odds ratios as yielding is a binary outcome (0 = pedestrian crossed first, 1 = yielded to vehicle). All other behavioral outcomes are continuous measures. All models included control variables (demographics, attitudes, experimental conditions) and crosswalk × attitude interactions; only focal pathways are shown for clarity. ***p < 0.001; **p < 0.01; *p < 0.05. 5. Discussion Mediation analysis reveals that physical infrastructure exerts stronger influence on pedestrian behavior rather than conscious risk perception, supporting Gibson's affordance theory (1977). Our results support that crosswalks shape behavioral responses based on affordance of crossing, at a pre-cognitive level, leading to reduced vigilance and defensive behaviors regardless of their conscious risk evaluations. While crosswalks enhance perceived safety, their primary effect operates through direct environmental affordances rather than cognitive mediation. Crosswalks operate through three direct pathways—reducing head rotation, decreasing yielding to vehicles, and slowing crossing speeds—while the perceptual pathways work in opposite directions. Crosswalks enable behavioral efficiency by reducing head rotation, as the infrastructure provides clear crossing boundaries that reduce the need for extensive environmental scanning. Similarly, crossing speeds decrease not because pedestrians perceive less risk, but because the presence of crosswalks creates a protected corridor that pedestrians can navigate at a comfortable pace. The reduction in yielding reflects pedestrians' assertion of priority within designated crossing space. These behavioral adaptations occur independently of risk perception—the crosswalk itself structures behavior through physical design cues rather than through altered safety feelings. This dominance of direct environmental effects over perception-mediated pathways challenges traditional models that assume infrastructure works by making people feel safer, revealing instead that effective pedestrian facilities reshape behavior through immediate physical affordances. Our findings have several implications on the relationship between perception and behavior. Individual personality traits significantly moderate how pedestrians respond to crosswalk infrastructure. High risk-taking individuals demonstrate the most pronounced sensitivity to environmental safety cues, showing substantial reductions in collision risk perception when zebra crosswalks are present. Safety-conscious pedestrians exhibit heightened responsiveness to infrastructure improvements, achieving the highest safety perception. Crosswalk effectiveness is not uniform across the population Designated crosswalks significantly increase pedestrians’ safety perception. Interactive crosswalks that respond to pedestrian and vehicle positioning generate ambiguous environmental signals. Zebra crosswalks are perceived as safer due to their clear, static visual demarcation. Enhanced safety perception may facilitate adaptive protective behaviors through "safety verification" processes. Pedestrians with higher safety perception engage in more thorough confirmatory scanning before crossing. Psychological comfort can translate into behavioral vigilance through deliberate pre-crossing environmental assessment rather than attentional disengagement. In crosswalks, although pedestrians feel safer, their head scanning decreases. This likely stems from affordance effects wherein safer environmental cues foster perceptions of 'protected space' or 'pedestrian-prioritized infrastructure,' automatically diminishing defensive behaviors regardless of conscious safety awareness. 6. Conclusion This study examined how physical safety infrastructure, particularly crosswalk design, influences pedestrian behavior through both direct environmental mechanisms and perception-mediated processes. Using virtual reality simulations and a mediation framework, we explored whether infrastructure shapes pedestrian behavior through changes in perceived safety or through direct environmental affordances. Findings highlight the dominance of implicit behavioral pathways. Crosswalk infrastructure enhanced subjective perceptions of safety and reduced perceived collision risk, yet the main behavioral changes—reduced head scanning, decreased yielding, and slower crossing speeds—were largely independent of these perceptual shifts. Mediation analyses revealed suppression effects, indicating that environmental features guide pedestrian behavior through automatic, implicit processes that bypass deliberate cognitive appraisal. This reinforces the importance of distinguishing between direct, implicit responses and indirect, perception-mediated choices: pedestrians may feel safer, but their actions are more strongly driven by affordance cues than by conscious deliberation. In addition, study revealed that the safety benefits of pedestrian infrastructure are moderated by individual differences in attitudes toward risk and safety. Safety-conscious pedestrians exhibited heightened perceptual sensitivity and greater behavioral adjustment, while risk-taking individuals showed minimal response to crosswalk cues. These findings demonstrate that the safety benefits of pedestrian infrastructure are not uniformly distributed, pointing to the importance of tailoring interventions to diverse user profiles. The implications for urban planning are multifaceted. First, the finding that implicit pathways outweigh conscious deliberation suggests that investments in physical infrastructure may unintentionally reduce pedestrian vigilance and defensive behaviors despite enhancing subjective safety perceptions. Infrastructure design should carefully balance safety enhancement with maintaining appropriate pedestrian caution, potentially requiring complementary features that preserve vigilant behaviors even in protected crossing environments. This emphasizes the limitation of relying solely on perception-based safety assessments, as enhanced perceived safety may inadvertently suppress defensive behaviors. Second, safety designs should mitigate false safety effects wherein structured environments inadvertently suppress vigilance, creating inattentional blind spots. Strategic integration of visibility cues, tactile surfaces, or interactive displays may help maintain cognitive engagement without undermining the predictability that makes crosswalks effective. Third, results provide a nuanced relationship with risk homeostasis theory, which posits that increased perceived safety leads to riskier behavior. Our findings partially support this theory but clarify that the mechanism is not deliberate risk recalibration but automatic adjustment to environmental affordances. While crosswalks significantly increased perceived safety, they simultaneously reduced vigilant behaviors, suggesting that risk homeostasis operates below the threshold of conscious awareness. This distinction is crucial: pedestrians do not intentionally decide to take greater risks because they feel safer; instead, implicit design cues reduce vigilance by default. In conclusion, effective pedestrian infrastructure does not work primarily by changing perceptions but structuring behavioral responses through both conscious and implicit pathways. Urban design should therefore prioritize behavioral guidance over subjective safety enhancement, ensuring that infrastructure not only protects pedestrians physically but also sustains their active engagement with the environment. Crosswalks should function not just as protective boundaries but as behavioral interfaces that activate vigilance rather than passive reliance. Future interventions should incorporate action-oriented design features that sustain cognitive engagement, particularly for populations less responsive to traditional crosswalk infrastructure. By recognizing infrastructure as both a physical and psychological interface, planners can design urban environments that foster safety, trust, and efficient pedestrian movement. 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Yang, Yue, Yee Mun Lee, Ruth Madigan, Albert Solernou, and Natasha Merat. 2024. “Interpreting Pedestrians’ Head Movements When Encountering Automated Vehicles at a Virtual Crossroad.” Transportation Research Part F: Traffic Psychology and Behaviour 103 (May): 340–52. https://doi.org/10.1016/j.trf.2024.04.022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":196303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(Left) Screenshot of experimental setup. (Right) Participant equipped with HTC Vive Pro headset and body trackers.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7979619/v1/fa2dbb8d0c1e364b93ca056c.png"},{"id":95808401,"identity":"786d7461-ac56-49ab-bfa4-f1a11cc44661","added_by":"auto","created_at":"2025-11-13 08:49:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":327304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTypes of LED lighting under four conditions. Clockwise from top left: (a) No light, representing the baseline condition; (b) Yellow light, when car is approaching; (c) green light, indicating pedestrian priority; (d) red light, activated as vehicle is close to crosswalk.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7979619/v1/0f1ca0f49b5c547790f325cd.png"},{"id":95808285,"identity":"3d3fb657-8d47-4c1b-80af-d72e5a74a3ad","added_by":"auto","created_at":"2025-11-13 08:49:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation model of environment-perception-behavior relationships\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7979619/v1/71c9298069343307ac9e7549.png"},{"id":95808577,"identity":"272bcc06-b7dd-483d-b8be-010514a43ddc","added_by":"auto","created_at":"2025-11-13 08:49:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101663,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effects between crosswalk type and pedestrian attitudes on perceived safety and collision risk\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7979619/v1/06a62e5f07856644b99f6227.png"},{"id":95810645,"identity":"f3dd9b49-3550-4c09-88bf-4de6c72abe2d","added_by":"auto","created_at":"2025-11-13 08:53:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2555946,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7979619/v1/b924130f-1e0f-49a7-a53b-9442c22225c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Crosswalk Affordances Influence Pedestrian Vigilance and Safety Perception","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDespite continuous efforts to improve pedestrian infrastructure and enforce crossing regulations, pedestrian fatalities remain high. Approximately one-third of crosswalk-related accidents are linked to pedestrian inattention, such as unsafe gap acceptance or failure to monitor approaching vehicles (Ko et al. 2015). These persistent risks suggest that physical design improvements alone are insufficient if they do not account for how pedestrians perceive and respond to their environments. Previous studies have shown that decision-making and behavioral adaptation play a critical role in pedestrian safety, particularly at intersections where pedestrian\u0026ndash;vehicle interactions are complex (Hamann et al., 2017; Herrero-Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhou \u0026amp; Horrey, 2010). However, it is still unclear whether these behavioral adaptations arise from conscious risk evaluation or from automatic, environment-driven responses.\u003c/p\u003e\u003cp\u003eMuch of the existing research has framed pedestrian decision-making through the lens of cognitive theories, particularly Endsley\u0026rsquo;s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) model of situation awareness (SA), which emphasizes how individuals perceive, comprehend, and project environmental information to guide behavior. Yet this model primarily explains the processing of information rather than the translation of awareness into action. In contrast, ecological theories such as Gibson\u0026rsquo;s (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) affordance framework argue that environmental structures directly elicit behavioral responses, often bypassing conscious reasoning. For instance, road geometry, markings, or lighting conditions can shape movement patterns automatically, without deliberate evaluation of risk.\u003c/p\u003e\u003cp\u003eThis theoretical tension\u0026mdash;between cognition-based and affordance-based perspectives\u0026mdash;has significant implications for pedestrian safety interventions. If pedestrian behavior is largely guided by implicit affordances, design improvements that focus only on altering perceived safety may fail to produce the intended behavioral outcomes. Bendak et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that crosswalk markings and signal timing at signalized intersections significantly affected pedestrian compliance and waiting behavior, while Li et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that enforcement cameras at unsignalized crossings not only improved driver yielding but also altered pedestrians\u0026rsquo; risk perception. These findings suggest that both conscious perception and automatic affordance cues shape crossing behavior, underscoring the need to examine their relative influence.\u003c/p\u003e\u003cp\u003eAccordingly, this study investigates how crosswalk infrastructure shapes pedestrian behavior through two distinct pathways: a perception-mediated process, where conscious risk and safety evaluations influence actions, and a direct affordance-based process, where environmental cues trigger automatic responses. Using immersive VR experiments, we examine whether pedestrian micro-behaviors\u0026mdash;head scanning, yielding, and crossing speed\u0026mdash;are driven by perceived safety and collision risk or by implicit affordance cues embedded in crosswalk design. By integrating situation awareness and affordance theories, this study contributes to a more comprehensive understanding of pedestrian decision-making and offers insights for designing crossings that enhance both physical protection and behavioral vigilance.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Cognitive Foundations of Pedestrian Decision-Making\u003c/h2\u003e\u003cp\u003eUnderstanding pedestrian behavior requires examining how individuals perceive and interpret environmental risk cues before acting. Early safety research, initially focused on drivers, evolved to acknowledge pedestrians as active decision-makers who consciously assess their surroundings and adjust behavior accordingly (Cohen et al. 1955; Kastenbaum and Briscoe 1975; Repetto-Wright \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Subsequent studies confirmed that pedestrians evaluate vehicle speed, distance, and signal timing when determining when to cross, and that these judgments vary by cognitive ability, age, and experience (Keegan and O\u0026rsquo;Mahony 2003; Ishaque and Noland 2008; Dommes and Cavallo 2011; Herrero-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Havard and Willis 2012). These findings underscore that perception and decision-making are interdependent, not only a matter of compliance or infrastructure.\u003c/p\u003e\u003cp\u003eEndsley\u0026rsquo;s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) Situation Awareness (SA) theory remains central to explaining how these cognitive mechanisms unfold. SA describes three progressive stages\u0026mdash;perception, comprehension, and projection\u0026mdash;through which individuals extract and interpret environmental information to anticipate events and guide behavior. In pedestrian contexts, these processes determine when individuals initiate crossing, adjust speed, or abort movement. For instance, pedestrians who perceive high collision risk often wait longer or cross more cautiously, reflecting deliberate risk management (Cœugnet et al. 2019; Butler et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Yet, the predictive power of risk perception varies: contextual influences such as traffic density, social norms, and habitual exposure can override deliberate cognition (Liu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese mixed findings reveal both the utility and the limitations of cognitive approaches. While perception and reasoning underpin many safety behaviors, real-world decision-making is rarely purely rational. Rapid, habitual responses often occur before full cognitive evaluation. This recognition provides a conceptual bridge to ecological perspectives, where behavior is viewed as the product of continuous interaction between perception, affordances, and environmental feedback rather than a sequence of detached mental calculations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ecological and Behavioral Adaptation Perspectives\u003c/h2\u003e\u003cp\u003eWhile cognitive theories emphasize deliberate information processing, ecological perspectives highlight the spontaneous, often unconscious, nature of human-environment interaction. Gibson\u0026rsquo;s (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) affordance theory argues that environmental structures directly specify possible actions, allowing people to respond automatically to cues in their surroundings. For pedestrians, elements such as crosswalk markings, lane width, medians, and refuge islands act as affordances that communicate where movement is safe or restricted. These cues guide locomotion not through deliberate reflection but through immediate perceptual pickup\u0026mdash;linking perception directly to behavior.\u003c/p\u003e\u003cp\u003eA complementary framework, Wilde\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) risk homeostasis theory, extends this ecological logic by suggesting that individuals regulate their behavior to maintain a preferred level of perceived risk. When safety measures reduce perceived danger\u0026mdash;through brighter lighting, enhanced markings, or signalization\u0026mdash;people may unconsciously take compensatory risks, offsetting the intended benefits of design interventions. Empirical studies support this adaptive tendency. Koh et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that safety improvements, such as wider medians or shorter crossing lengths, encouraged some pedestrians to accept smaller gaps or violate signals, consistent with risk homeostasis principles. This evidence highlights that safer environments can paradoxically diminish vigilance if they over‑stabilize expectations of safety.\u003c/p\u003e\u003cp\u003eRecent ecological research also demonstrates that affordance cues can work beneficially when they clearly articulate movement possibilities. For instance, well‑demarcated crosswalks, textured pavements, and refuge spaces enhance predictability for both drivers and pedestrians, reducing ambiguity and conflict. These features do not simply increase subjective feelings of safety; they recalibrate behavior by restructuring how pedestrians perceive the flow of vehicles and space available for movement. In this sense, behavioral adaptation is not purely compensatory\u0026mdash;it can be constructive when affordances reinforce appropriate actions. Understanding this balance between adaptive risk‑taking and guidance‑driven adjustment is central to designing crossings that encourage active attention rather than passive reliance on infrastructure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Integrating Perception and Affordance: Evidence from Experiments and Simulations\u003c/h2\u003e\u003cp\u003eA growing body of experimental and simulation-based studies has advanced understanding of how perception and affordance jointly shape pedestrian behavior. Virtual reality (VR) experiments, in particular, have provided a means to manipulate environmental conditions with high precision while maintaining ecological realism. This methodology allows researchers to investigate how pedestrians respond to specific design features\u0026mdash;such as crosswalk markings, lighting, traffic speed, or driver type\u0026mdash;while observing detailed indicators of decision-making such as waiting time, head scanning, and crossing speed.\u003c/p\u003e\u003cp\u003eFindings from these VR studies demonstrate that pedestrians continuously adapt their behavior to changing risk contexts through both conscious and automatic processes. Luu et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that participants crossing narrow streets in VR displayed longer hesitation and more extensive visual scanning when the environment appeared risky, indicating that conscious risk appraisal guided cautious strategies. Kwon et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) expanded on this by showing that risk perception influences two behavioral stages differently: it increases caution during the decision phase but triggers urgency once the crossing begins, resulting in faster movement and less stable gait. This stage-dependent response supports the idea that pedestrian behavior reflects both cognitive assessment and reactive adaptation.\u003c/p\u003e\u003cp\u003eBeyond risk perception, VR studies have also highlighted the role of spatial affordances. Joo et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that the inclusion of medians and refuge islands improved crossing success rates and reduced collisions without significant changes in perceived safety, implying that the environment itself can guide safer actions through affordance mechanisms. Similarly, Yang et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that head movements toward oncoming autonomous vehicles functioned as implicit communication cues, enabling coordination even when explicit perception of danger was low.\u003c/p\u003e\u003cp\u003eTogether, these studies demonstrate that pedestrian behavior arises from the dynamic interplay between conscious perception and implicit affordance cues. Cognitive appraisal helps pedestrians anticipate risk, while affordances embedded in infrastructure elicit immediate adjustments in movement and vigilance. However, despite the growing evidence base, few studies have quantitatively compared the relative influence of these mechanisms. The present study addresses this gap using a multilevel path-decomposition framework to distinguish between direct environmental effects and perception-mediated associations, offering a more integrated understanding of how crosswalk design shapes pedestrian awareness, confidence, and safety performance.\u003c/p\u003e\u003cp\u003ePedestrian crossing behavior is shaped by both deliberate cognitive assessments and automatic environmental responses. While cognitive models explain conscious evaluations of risk, ecological approaches capture implicit, affordance-driven adjustments. The present study integrates these perspectives within a unified experimental framework to examine how crosswalk infrastructure influences both perceptual and behavioral dimensions of pedestrian safety.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cp\u003e\u003cstrong\u003e3.1 Participants\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eForty participants (25 men, 15 women; M = 22.2 years, SD = 3.3, range = 18\u0026ndash;31) were recruited from a university campus through bulletin boards and social media postings. Eligibility criteria required participants to (1) be aged 19 years or older, (2) have normal or corrected-to-normal vision, and (3) report no prior adverse reactions to virtual-reality (VR) exposure. Sixty percent had previous VR experience, 60 % held a driver\u0026rsquo;s license, and 10 % reported prior involvement in a pedestrian-related traffic incident. Participants received USD 25 for approximately 45 minutes of participation (including setup, practice, trials, and debriefing). The study protocol was reviewed and approved by the Institutional Review Board of the Ulsan National Institute of Science and Technology (UNISTIRB-23-042-A). All participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Apparatus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted using an HTC Vive Pro head-mounted display (2,448 \u0026times; 2,448 pixels per eye; 120 Hz; 120\u0026deg; field of view) equipped with VIVE Tracker 3.0 sensors attached to participants\u0026rsquo; ankles and waist to capture full-body motion. The virtual environment was built in Unity 3D (version 2021.3 LTS) using the SteamVR SDK and deployed within a 10 m \u0026times; 10 m tracked area, allowing participants to move naturally. Custom C# scripts continuously recorded head position, rotation, pedestrian coordinates, and timestamped behavioral events. Participants physically crossed the virtual road whenever they judged it safe to do so. Following each trial, they rated their perceived safety and collision risk using an in-VR handheld interface. Integrated spatial audio reproduced realistic traffic sounds and ambient noise, while all sessions were video-recorded for post-hoc data validation and quality assurance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Virtual Reality Setup and Experimental Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe virtual environment simulated a two-lane urban road (7 m wide, 3.5 m per lane) bordered by sidewalks, bus stops, and commercial buildings to enhance realism (Figure 1). The experiment followed a fully within-subject factorial design comprising five manipulated factors: (1) crosswalk type (unmarked, zebra, interactive LED), (2) driver type (automated vs. human-controlled), (3) vehicle speed (30 vs. 50 km/h), (4) lighting condition (day vs. night), and (5) time-to-collision (TTC) auditory prompt (2 s vs. 5 s). Each participant completed 20 randomized trials that counterbalanced all experimental conditions to mitigate order effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe interactive LED crosswalk featured embedded lighting that dynamically responded to traffic and pedestrian proximity. As shown in Figure 2, the LEDs displayed three colors: yellow when a vehicle approached within 10 m, green when the pedestrian entered the crosswalk (indicating right-of-way), and red when a vehicle\u0026rsquo;s TTC fell below one second, signaling danger. This responsive design simulated emerging smart crosswalk systems and operationalized the concept of environmental affordance by providing perceptual cues that guided crossing behavior.\u003c/p\u003e\n\u003cp\u003eTwo vehicle types were implemented: automated and human-driven vehicles. Automated vehicles were programmed with consistent yielding behavior, set to decelerate when detecting pedestrians within 10 meters and fully yield when pedestrians initiate crossing. Human-driven vehicles were controlled by recruited drivers connected to the VR environment in real-time, providing naturalistic and variable responses to pedestrian behavior. Same visual appearance to participants. This approach ensured that participants experienced both predictable automated vehicle interactions and the unpredictability characteristic of human drivers, enhancing the ecological validity of the crossing scenarios. Environmental visibility conditions alternated between daytime and nighttime scenarios with appropriate lighting adjustments. Traffic speed varied between residential zones (30 km/h) and arterial road speeds (50 km/h). Additionally, auditory crossing signals were implemented based on time-to-collision (TTC) values, with a beep sound prompting pedestrians to cross at either 2 seconds or 5 seconds TTC, simulating different crossing opportunity windows. This signal manipulation allowed examination of how temporal pressure influences crossing decisions and safety perceptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon arrival, participants were provided with an overview of the experiment process. After reviewing and signing the informed consent form, participants completed a demographic and screening form to confirm eligibility, including questions about age, gender, driving experience, VR experience, and any history of motion sickness or visual impairments. Following the pre-experiment questionnaires, participants were fitted with the HTC Vive Pro headset and VIVE trackers on their ankles and waist. Before the main experiment, participants completed two practice trials to familiarize themselves with the VR environment, crossing mechanics, and in-VR questionnaire system. The experimenter confirmed participants\u0026apos; comfort with the VR system before proceeding.\u003c/p\u003e\n\u003cp\u003eThe main experiment consisted of 20 randomized crossing trials: 10 with automated vehicles and 10 with human-controlled vehicles. Pedestrians were instructed to approach and cross the road when they felt it was safe to cross, with no time constraints. After each crossing, participants remained in VR to complete ratings of perceived environmental safety and collision risk using the handheld controller. Between trials, participants returned to the starting position while the next scenario loaded. To ensure participant comfort and minimize VR-induced fatigue, the experimenter asked participants to verbally confirm their readiness before initiating each new trial. After completing all trials, participants removed the VR equipment and completed post-experiment questionnaires asking their attitude toward crossing, including risk-taking and safety consciousness, and overall experience with the VR system. The session concluded with a debriefing where participants could ask questions and were informed about the study\u0026apos;s objectives. Each experimental session lasted approximately 30 minutes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the sequential relationship between environment, perception, and behavior, data were collected across perceptual and behavioral dimensions in each trial. The environment\u0026ndash;perception\u0026ndash;behavior framework followed Endsley\u0026rsquo;s (1995) model of situation awareness, where environmental features influence perception and comprehension of risk, which in turn guide decisions and actions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 Perceptual measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceptual variables were obtained through two in-VR questionnaire items administered after each crossing: (1) perceived safety of the road environment at the time of crossing (0 = \u0026quot;not safe at all\u0026quot; to 10 = \u0026quot;very safe\u0026quot;), and (2) perceived collision risk with vehicles at the time of crossing (0 = \u0026quot;not risky at all\u0026quot; to 10 = \u0026quot;very risky\u0026quot;). These ratings captured participants\u0026rsquo; conscious evaluation of the crossing environment and vehicle behavior. Collecting these measures post-trial preserved immersion and prevented interruption of natural decision-making. Participants were instructed to base their responses on their immediate experience during the crossing rather than retrospective judgment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 Behavioral measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBehavioral indicators were derived directly from motion-tracking data. Head movement angle (horizontal rotation, in degrees) quantified scanning activity, with larger values indicating broader visual monitoring. Yielding behavior represented binary crossing decisions: waiting for vehicles to pass (coded 1) or crossing first (coded 0). Crossing speed was calculated as road width divided by crossing duration, measured from step initiation to reaching the opposite curb. These variables provided objective metrics of pedestrian vigilance, assertiveness, and efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.3 Individual attitude measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-experiment questionnaires assessed individual differences in crossing attitudes. Principal component analysis of six items identified two distinct dimensions explaining 67.4% of total variance. The first component, Risk-Taking Tendency (46.4% variance), included items related to jaywalking and crossing outside designated zones. The second, Safety Consciousness (21.0% variance), reflected cautiousness and accident avoidance. These factor scores served as covariates in the subsequent analyses to account for stable inter-individual traits influencing both perception and behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Principal Component Analysis of Pedestrian Attitude Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComp1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComp2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk-Taking Tendency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eI don\u0026apos;t use crosswalks when in a hurry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eI cross without crosswalks when no cars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eI often jaywalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eI take risky actions to save time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafety Conscious\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eI\u0026apos;m less likely to be involved in accidents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eI\u0026apos;m more cautious than others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 332px;\"\u003e\n \u003cp\u003e% of total variance explained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e46.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e21.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Analytical Framework\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a path-decomposition analytical framework to examine how crosswalk design influences pedestrian behavior both directly and indirectly through perception of safety and collision risk. The conceptual model (Figure 3) draws from Endsley\u0026apos;s (1995) situation awareness theory, viewing pedestrian decision-making as a sequential process in which environmental features shape perceived risk and safety, which in turn are associated with behavioral responses. Rather than implying strict causal mediation, this framework identifies associational pathways that are statistically consistent with perception-mediated effects.\u003c/p\u003e\n\u003cp\u003eThe model decomposes the total effect of crosswalk design into three pathways: (a) the effect of environment on perception, representing how different crosswalk types influence perceived safety and collision risk; (b) the effect of perception on behavior, capturing how risk assessment translates into crossing actions; and direct effect (c\u0026apos;) the effect of the direct environment on behavior, reflecting behavioral differences that persist after accounting for perception. The indirect effect (a \u0026times; b) represents the degree to which variations in behavior are consistent with perception-mediated mechanisms. This approach quantifies the relative contributions of perception and affordance in shaping pedestrian actions within the VR environment.\u003c/p\u003e\n\u003cp\u003eBecause perceptual measures were collected immediately after each crossing, results should be interpreted as cross-sectional associations rather than causal mediation. However, the repeated within-subject design enhances internal consistency and allows for robust comparisons of perceptual and behavioral patterns across environmental conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models were employed to examine whether crosswalk design influences pedestrian behavior directly or indirectly through risk perception, while accounting for repeated observations within participants. Three models were estimated:\u003c/p\u003e\n\u003cp\u003eModel 1 (Environment\u0026nbsp;\u0026rarr;\u0026nbsp;Perception): Perceptionᵢⱼ = \u0026beta;₀ + \u0026beta;₁(Crosswalk typeᵢⱼ) + Controls + uᵢ + \u0026epsilon;ᵢⱼ\u003c/p\u003e\n\u003cp\u003eModel 2 (Environment\u0026nbsp;\u0026rarr;\u0026nbsp;Behavior): Behaviorᵢⱼ = \u0026gamma;₀ + \u0026gamma;₁(Crosswalk typeᵢⱼ) + Controls + vᵢ + \u0026epsilon;ᵢⱼ\u003c/p\u003e\n\u003cp\u003eModel 3 (Perception\u0026nbsp;+\u0026nbsp;Environment\u0026nbsp;\u0026rarr;\u0026nbsp;Behavior): Behaviorᵢⱼ = \u0026delta;₀ + \u0026delta;₁(Crosswalk typeᵢⱼ) + \u0026delta;₂(Perceptionᵢⱼ) + Controls + wᵢ + \u0026epsilon;ᵢⱼ\u003c/p\u003e\n\u003cp\u003ewhere uᵢ, vᵢ, and wᵢ ~ N(0, \u0026sigma;\u0026sup2;ᵤ) are random intercepts for participant i, \u0026epsilon;ᵢⱼ represents the residual error, and crosswalk type was treated as a categorical variable (0 = no crosswalk, 1 = zebra crossing, 2 = interactive crossing). Control variables included demographic factors (age, gender, driver\u0026apos;s license status, VR experience, crash history) and individual attitude components (risk-taking tendency (PC1), and safety consciousness (PC2)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInteraction terms between crosswalk type and attitude factors (Crosswalk\u0026nbsp;\u0026times;\u0026nbsp;PC1, Crosswalk\u0026nbsp;\u0026times;\u0026nbsp;PC2) were tested to assess heterogeneity in perceptual and behavioral responses. Indirect (a\u0026nbsp;\u0026times;\u0026nbsp;b) effects were estimated using the Monte\u0026nbsp;Carlo method for multilevel data, providing confidence intervals around the decomposed effects. All analyses were conducted in a mixed-modeling framework to handle unbalanced repeated measures and correlated residuals.\u003c/p\u003e\n\u003cp\u003eTo further validate interpretability, sensitivity analyses were performed to ensure that inclusion or exclusion of perceptual covariates did not alter the direction or significance of the main behavioral effects. The results were thus interpreted as evidence of statistical consistency with perception-mediated processes, rather than definitive causal mediation.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes participant characteristics and study variables. The study included 40 participants and 777 observations (97.1% of the original 800 trials). Since the experiment was conducted within a campus, participants were predominantly young adults, with approximately two-thirds being male and holding driver's licenses. Most had prior VR experience, while only 10.1% reported previous traffic accident experience. Regarding perceptual measures, participants reported moderate levels of perceived safety (M\u0026thinsp;=\u0026thinsp;6.09, SD\u0026thinsp;=\u0026thinsp;2.73) and perceived collision risk (M\u0026thinsp;=\u0026thinsp;3.18, SD\u0026thinsp;=\u0026thinsp;2.51) on 0\u0026ndash;10 scales, indicating generally favorable safety perceptions across experimental conditions. Individual difference measures revealed substantial variation in pedestrian attitudes. Risk-taking tendency (PC1) showed the widest spread (SD\u0026thinsp;=\u0026thinsp;1.67, range: -3.10 to 3.74), while safety consciousness (PC2) demonstrated more moderate variation (SD\u0026thinsp;=\u0026thinsp;1.11, range: -2.44 to 2.04). Both principal component scores were standardized with means of zero.\u003c/p\u003e\u003cp\u003eTo examine whether perceptions and behaviors differed across crosswalk type, one-way ANOVAs were conducted. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, safety perceptions at zebra crossings were rated as safest (M\u0026thinsp;=\u0026thinsp;6.69), followed by interactive crossings (M\u0026thinsp;=\u0026thinsp;6.15), with unmarked locations perceived as least safe (M\u0026thinsp;=\u0026thinsp;5.41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Collision risk perception differed significantly across conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), all values remained relatively low on the 0\u0026ndash;10 scale, with unmarked locations rated highest (M\u0026thinsp;=\u0026thinsp;3.65) and zebra crossings lowest (M\u0026thinsp;=\u0026thinsp;2.75). This suggests that participants generally perceived the VR environment as relatively safe across all conditions, though unmarked locations were seen as comparatively riskier.\u003c/p\u003e\u003cp\u003ePre-crossing behaviors also varied significantly by crosswalk type. Head rotation angles were greatest at unmarked locations (M\u0026thinsp;=\u0026thinsp;41.06), indicating more extensive visual scanning when crossing infrastructure was absent, compared to zebra (M\u0026thinsp;=\u0026thinsp;35.90) or interactive crossings (M\u0026thinsp;=\u0026thinsp;38.16, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Yielding rates were highest at unmarked locations (87.7%) and lowest at zebra crossings (75.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), suggesting that marked crosswalks increased pedestrians' crossing assertiveness. Crossing speeds were fastest at unmarked locations compared to both zebra and interactive crossings. Taken together, these descriptive results suggest that crosswalk infrastructure shapes both perceptions and pre-crossing behaviors: when infrastructure conveys greater safety, pedestrians reduce scanning, yield less frequently, and walk more slowly.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerception\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived safety of environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;10 scales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived collision risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;10 scales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePedestrian Behavior\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDegrees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrossing Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekm/h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttitudes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk-Taking Tendency (PC1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety Conscious (PC2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographic characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriver's license\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior VR experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccident experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe next estimated linear mixed-effects models to account for within-participant correlations across multiple crossings and to control for demographic characteristics and crossing-related attitudes. The following sections present results from sequential mixed-effects models and a mediation analysis, which together assess whether crosswalk design influences pedestrian behavior directly or indirectly through perceptions of safety and collision risk.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifference of perception and behaviors across crosswalk type (ANOVA)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo crosswalk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZebra crosswalk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInteractive crosswalk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerception\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived safety of environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived collision risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBehavior before crossing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrossing Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNote: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Effect on perception (Psychological Responses)\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents results from Model 1, examining the effects of crosswalk type, individual attitudes, and experimental design variables on pedestrian safety and collision risk perceptions. Both zebra and interactive crosswalks significantly increased perceived safety compared to unmarked roads (β\u0026thinsp;=\u0026thinsp;1.426 and 1.127, respectively, both p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and decreased perceived collision risk (β = -1.112 and \u0026minus;\u0026thinsp;0.735, both p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These findings indicate that crosswalk infrastructure consistently improves pedestrian safety perceptions and reduces perceived risk level.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects on perception\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePerceived safety of environment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePerceived collision risk\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCrosswalk Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra crosswalk (reference\u0026thinsp;=\u0026thinsp;no crosswalk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive crosswalk (reference\u0026thinsp;=\u0026thinsp;no crosswalk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttitude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC1 (Risk-taking)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC2 (Safety-conscious)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInteraction (Crosswalk Type # Attitude)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra # PC1 (Risk-taking)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive # PC1 (Risk-taking)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra # PC2 (Safety-conscious)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive # PC2 (Safety-conscious)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndividual Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriver's license (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVR experience (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrash experience (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExperimental Design\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriver type (0\u0026thinsp;=\u0026thinsp;npc; 1\u0026thinsp;=\u0026thinsp;human)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperiment number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeed limit (0\u0026thinsp;=\u0026thinsp;30km/h, 1\u0026thinsp;=\u0026thinsp;50km/h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingal to cross (0\u0026thinsp;=\u0026thinsp;ttc 2s, 1\u0026thinsp;=\u0026thinsp;ttc 5s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLR Test vs. Linear Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e415.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e245.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarginal R\u0026sup2; (fixed effects)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConditional R\u0026sup2; (total model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObs.(participants)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e777 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e777 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNote: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; +p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIndividual attitudes also showed higher risk-taking tendency (PC1) was associated with lower perceived collision risk (β = -0.532, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), while safety consciousness (PC2) was marginally associated with lower collision risk perception (β = -0.399, p\u0026thinsp;\u0026lt;\u0026thinsp;.10). The results also show significant crosswalk and attitude interactions, indicating that the effects of crossing infrastructure on risk perception vary by individual differences. The positive effects of both zebra and interactive crosswalks on perceived safety were diminished among high risk-takers, but enhanced among safety-conscious individuals (β\u0026thinsp;=\u0026thinsp;0.684 and 0.523, both p\u0026thinsp;\u0026lt;\u0026thinsp;.001). A similar pattern emerged for perceived collision risk. Risk-reducing effects of crosswalks were smaller for risk-taking individuals, while marginally stronger for safety-conscious pedestrians. These interactions suggest that risk-taking individuals derive less perceptual benefit from crossing infrastructure, while safety-conscious individuals show heightened sensitivity to environmental safety cues.\u003c/p\u003e\u003cp\u003eExperimental design variables showed expected patterns. Human-controlled vehicles increased perceived safety while decreasing collision risk perception, likely because human drivers exhibited more predictable yielding behavior than non-player-controlled (programmed) vehicles. Nighttime conditions decreased perceived safety. Higher speed limits marginally decreased perceived safety (β = -0.278, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and significantly increased collision risk perception (β\u0026thinsp;=\u0026thinsp;0.432, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that participants were sensitive to the faster-approaching vehicles. Despite substantial between-person variation, demographic characteristics did not significantly predict risk perception. This suggests that unexplained heterogeneity may reflect unmeasured factors such as habitual behaviors, or prior experiences with traffic environments.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the interaction effects between crosswalk type and individual differences on risk perception. For perceived safety (top panels), high risk-takers showed minimal differentiation across crosswalk types, while low risk-takers and safety-conscious individuals demonstrated greater sensitivity to crossing infrastructure, with marked improvements at zebra crossings. For collision risk perception (bottom panels), all groups perceived lower risk at crosswalks compared to unmarked locations, but the magnitude of this effect was attenuated for high risk-takers.\u003c/p\u003e\u003cp\u003eInteraction effects between crosswalk type and individual differences are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThese patterns suggest that crosswalk design interventions will have differential effectiveness across pedestrian populations, with safety infrastructure providing the greatest behavioral benefits for individuals already predisposed toward cautious behavior, while showing limited impact on risk-taking pedestrians who may rely more on direct environmental cues rather than infrastructure-based risk assessment. These findings indicate that crosswalk design benefits are not uniformly distributed across all pedestrian types, with safety-oriented individuals deriving the greatest perceptual benefits from enhanced crossing infrastructure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Effect on behavior\u003c/h2\u003e\u003cp\u003eThe results of the model examining the effects of perception on pedestrian behavior are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Both zebra crosswalks and interactive crosswalks significantly reduced head rotation angles compared to no crosswalk conditions, indicating that crosswalks lead to decreased visual scanning. These crosswalks also decreased the odds of pedestrians yielding to vehicles, with zebra crosswalks showing a 69% reduction in yielding (odds ratio of 0.306) and interactive crosswalks showing a 62% reduction (odds ratio of 0.385), while also reducing crossing speeds.\u003c/p\u003e\u003cp\u003eIndividual attitudes played important roles in shaping behavior. Safety-conscious individuals demonstrated increased head rotation and slower crossing speeds, while risk-taking tendency showed no significant main effects on any behavioral outcomes. However, interaction effects revealed that high risk-takers maintained better visual scanning at crosswalks, showing smaller reductions in head rotation at both zebra and interactive crosswalks compared to low risk-takers.\u003c/p\u003e\u003cp\u003ePerceptual factors were also significant predictors of behavior. Higher safety perception was positively associated with head rotation, suggesting that pedestrians who feel safer actually engage in more vigilant scanning behavior. Collision risk perception increased yielding behavior, indicating that pedestrians who perceive higher collision risk are more likely to yield to approaching vehicles. These findings suggest that crosswalks create complex behavioral changes where pedestrians both reduce their vigilance (less head scanning), reduce their yielding to vehicles, and slower crossing speed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects on behavior\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHead rotation angle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePedestrian yield\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eCrossing speed\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnvironmental Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra crosswalk (reference\u0026thinsp;=\u0026thinsp;no crosswalk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive crosswalk (reference\u0026thinsp;=\u0026thinsp;no crosswalk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttitude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk-taking tendency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety-conscious\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInteraction (Crosswalk Type # Attitude)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra # PC1 (Risk-taking)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive # PC1 (Risk-taking)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra # PC2 (Safety-conscious)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive # PC2 (Safety-conscious)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerception\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollision risk perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndividual Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriver's license (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVR experience (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrash experience (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExperimental Conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriver type (0\u0026thinsp;=\u0026thinsp;npc; 1\u0026thinsp;=\u0026thinsp;human)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperiment number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeed limit (0\u0026thinsp;=\u0026thinsp;30km/h, 1\u0026thinsp;=\u0026thinsp;50km/h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingal to cross (0\u0026thinsp;=\u0026thinsp;ttc 2s, 1\u0026thinsp;=\u0026thinsp;ttc 5s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLR Test vs. Linear Model (Logistic Model for Yield)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e131.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e241.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarginal R\u0026sup2; (fixed effects)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConditional R\u0026sup2; (total model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObs.(participants)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Mediation analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the mediation analysis examining how crosswalk influences pedestrian behavior through risk and safety perception as mediating pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePath a (environment \u0026rarr; perception)\u003c/b\u003e demonstrates that consistent with earlier results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), both zebra and interactive crosswalk types significantly increased safety perceptions compared to unmarked roads. Zebra crossings showed the strongest effects, increasing perceived safety (β\u0026thinsp;=\u0026thinsp;1.426, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and reducing collision risk perception (β = -1.112, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Interactive crossings showed similar but slightly smaller effects on both perception measures.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePath b (Perception \u0026rarr; Behavior)\u003c/b\u003e reveals that higher perceived safety increased head rotation (β\u0026thinsp;=\u0026thinsp;0.391, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), indicating that increased safety perception led to more visual scanning. While this finding is counterintuitive to risk compensation theory (or behavioral adaptation theory, in which people maintain a target level of risk, so when they feel safer, they engage in riskier behaviors), increased head movement in safe environments may reflect exploratory information-seeking behavior or confirmatory scanning as a form of proactive engagement. Supporting this interpretation, Yang et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that pedestrians increase head-turns for a \"last-second check\" before crossing initiation.\u003c/p\u003e\u003cp\u003eIn safer environments, pedestrians have greater temporal and cognitive capacity, enabling more systematic and thorough visual exploration. Conversely, when feeling unsafe, pedestrians may employ \"defensive non-looking\" as a protective strategy, either to minimize anxiety from threatening stimuli or to signal non-crossing intentions to drivers. Thus, increased scanning in safer conditions reflects a shift from avoidance-motivated behavior to approach-motivated exploration aimed at enhancing situational awareness. Regarding the effect of perception on pedestrian yielding behavior, safety perception showed no significant effect, but collision risk perception significantly increased the odds of yielding to vehicles (odds ratio of 1.232). This indicates that pedestrians base their yielding decisions on risk assessment rather than environmental safety.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePath c (Total Effects)\u003c/b\u003e shows that crosswalks significantly influence pedestrian behaviors. Zebra crossings significantly reduced head rotation and crossing speed, while reducing the odds of pedestrian yielding. \u003cb\u003ePath c\u0026prime; (Direct Effects\u003c/b\u003e) reveals that these behavioral changes largely remain after controlling perception. This pattern reveals two opposing mechanisms that are simultaneously at play. The positive indirect path (perceived safety \u0026rarr; increased head rotation) suggests that psychological safety promotes visual exploration. However, the stronger negative direct path indicates that physical infrastructure reduces scanning needs through environmental predictability, allowing cognitive resource conservation. In other words, while crosswalks do increase safety perception, and safety perception does encourage scanning behavior for confirmatory or proactive engagement, the direct affordance effect of crosswalk overwhelms this cognitive pathway. The presence of designated crosswalk reduces the need for extensive scanning, resulting in decreased head movement in marked crosswalks.\u003c/p\u003e\u003cp\u003eWhen perception is held constant, pedestrians scan less in crosswalks. This reduction is not due to conscious cognitive perception of feeling safer, but rather by an implicit sense of security and predictability afforded by the crosswalk infrastructure itself, which reduces the need for vigilance. By contrast, when pedestrians perceive greater safety, they engage in more extensive visual exploration of the environment compared to when they feel less safe to cross.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Mediation Analysis Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eEnvironment \u0026rarr; Perception (a) (Direct Effects)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZebra \u003c/p\u003e\u003cp\u003e\u0026rarr; Safety perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.426***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZebra \u003c/p\u003e\u003cp\u003e\u0026rarr; Collision risk perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.112***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteractive \u003c/p\u003e\u003cp\u003e\u0026rarr; Safety perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.127***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInteractive \u003c/p\u003e\u003cp\u003e\u0026rarr; Collision risk perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.735***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerception \u0026rarr; Behavior (b) (Direct Effects)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety perception \u003c/p\u003e\u003cp\u003e\u0026rarr; Head rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.391*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCollision risk perception \u003c/p\u003e\u003cp\u003e\u0026rarr; Head rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety perception \u003c/p\u003e\u003cp\u003e\u0026rarr; Pedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCollision risk perception \u003c/p\u003e\u003cp\u003e\u0026rarr; Pedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.232**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety perception \u003c/p\u003e\u003cp\u003e\u0026rarr; Crossing speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCollision risk perception \u003c/p\u003e\u003cp\u003e\u0026rarr; Crossing speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnvironment \u0026rarr; Behavior (c) (Total Effects)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eZebra \u0026rarr; Head rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.386***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eZebra \u0026rarr; Pedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.292***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eZebra \u0026rarr; Crossing speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.259***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eInteractive \u0026rarr; Head rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.893***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eInteractive \u0026rarr; Pedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.375**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eInteractive \u0026rarr; Crossing speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.226**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnvironment \u0026rarr; Behavior (c') (Direct effects after controlling for perception)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eZebra \u0026rarr; Head rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.902***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eZebra \u0026rarr; Pedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.306***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eZebra \u0026rarr; Crossing speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.260***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eInteractive \u0026rarr; Head rotation angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.304***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eInteractive \u0026rarr; Pedestrian yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.385**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eInteractive \u0026rarr; Crossing speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.224**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: 40 participants and 777 observations. Coefficients for yielding behavior represent odds ratios as yielding is a binary outcome (0\u0026thinsp;=\u0026thinsp;pedestrian crossed first, 1\u0026thinsp;=\u0026thinsp;yielded to vehicle). All other behavioral outcomes are continuous measures. All models included control variables (demographics, attitudes, experimental conditions) and crosswalk \u0026times; attitude interactions; only focal pathways are shown for clarity. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eMediation analysis reveals that physical infrastructure exerts stronger influence on pedestrian behavior rather than conscious risk perception, supporting Gibson's affordance theory (1977). Our results support that crosswalks shape behavioral responses based on affordance of crossing, at a pre-cognitive level, leading to reduced vigilance and defensive behaviors regardless of their conscious risk evaluations. While crosswalks enhance perceived safety, their primary effect operates through direct environmental affordances rather than cognitive mediation. Crosswalks operate through three direct pathways\u0026mdash;reducing head rotation, decreasing yielding to vehicles, and slowing crossing speeds\u0026mdash;while the perceptual pathways work in opposite directions. Crosswalks enable behavioral efficiency by reducing head rotation, as the infrastructure provides clear crossing boundaries that reduce the need for extensive environmental scanning. Similarly, crossing speeds decrease not because pedestrians perceive less risk, but because the presence of crosswalks creates a protected corridor that pedestrians can navigate at a comfortable pace. The reduction in yielding reflects pedestrians' assertion of priority within designated crossing space. These behavioral adaptations occur independently of risk perception\u0026mdash;the crosswalk itself structures behavior through physical design cues rather than through altered safety feelings. This dominance of direct environmental effects over perception-mediated pathways challenges traditional models that assume infrastructure works by making people feel safer, revealing instead that effective pedestrian facilities reshape behavior through immediate physical affordances.\u003c/p\u003e\u003cp\u003eOur findings have several implications on the relationship between perception and behavior.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIndividual personality traits significantly moderate how pedestrians respond to crosswalk infrastructure. High risk-taking individuals demonstrate the most pronounced sensitivity to environmental safety cues, showing substantial reductions in collision risk perception when zebra crosswalks are present. Safety-conscious pedestrians exhibit heightened responsiveness to infrastructure improvements, achieving the highest safety perception. Crosswalk effectiveness is not uniform across the population\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDesignated crosswalks significantly increase pedestrians\u0026rsquo; safety perception. Interactive crosswalks that respond to pedestrian and vehicle positioning generate ambiguous environmental signals. Zebra crosswalks are perceived as safer due to their clear, static visual demarcation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEnhanced safety perception may facilitate adaptive protective behaviors through \"safety verification\" processes. Pedestrians with higher safety perception engage in more thorough confirmatory scanning before crossing. Psychological comfort can translate into behavioral vigilance through deliberate pre-crossing environmental assessment rather than attentional disengagement.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIn crosswalks, although pedestrians feel safer, their head scanning decreases. This likely stems from affordance effects wherein safer environmental cues foster perceptions of 'protected space' or 'pedestrian-prioritized infrastructure,' automatically diminishing defensive behaviors regardless of conscious safety awareness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examined how physical safety infrastructure, particularly crosswalk design, influences pedestrian behavior through both direct environmental mechanisms and perception-mediated processes. Using virtual reality simulations and a mediation framework, we explored whether infrastructure shapes pedestrian behavior through changes in perceived safety or through direct environmental affordances.\u003c/p\u003e\u003cp\u003eFindings highlight the dominance of implicit behavioral pathways. Crosswalk infrastructure enhanced subjective perceptions of safety and reduced perceived collision risk, yet the main behavioral changes\u0026mdash;reduced head scanning, decreased yielding, and slower crossing speeds\u0026mdash;were largely independent of these perceptual shifts. Mediation analyses revealed suppression effects, indicating that environmental features guide pedestrian behavior through automatic, implicit processes that bypass deliberate cognitive appraisal. This reinforces the importance of distinguishing between direct, implicit responses and indirect, perception-mediated choices: pedestrians may feel safer, but their actions are more strongly driven by affordance cues than by conscious deliberation.\u003c/p\u003e\u003cp\u003eIn addition, study revealed that the safety benefits of pedestrian infrastructure are moderated by individual differences in attitudes toward risk and safety. Safety-conscious pedestrians exhibited heightened perceptual sensitivity and greater behavioral adjustment, while risk-taking individuals showed minimal response to crosswalk cues. These findings demonstrate that the safety benefits of pedestrian infrastructure are not uniformly distributed, pointing to the importance of tailoring interventions to diverse user profiles.\u003c/p\u003e\u003cp\u003eThe implications for urban planning are multifaceted.\u003c/p\u003e\u003cp\u003eFirst, the finding that implicit pathways outweigh conscious deliberation suggests that investments in physical infrastructure may unintentionally reduce pedestrian vigilance and defensive behaviors despite enhancing subjective safety perceptions. Infrastructure design should carefully balance safety enhancement with maintaining appropriate pedestrian caution, potentially requiring complementary features that preserve vigilant behaviors even in protected crossing environments. This emphasizes the limitation of relying solely on perception-based safety assessments, as enhanced perceived safety may inadvertently suppress defensive behaviors.\u003c/p\u003e\u003cp\u003eSecond, safety designs should mitigate false safety effects wherein structured environments inadvertently suppress vigilance, creating inattentional blind spots. Strategic integration of visibility cues, tactile surfaces, or interactive displays may help maintain cognitive engagement without undermining the predictability that makes crosswalks effective.\u003c/p\u003e\u003cp\u003eThird, results provide a nuanced relationship with risk homeostasis theory, which posits that increased perceived safety leads to riskier behavior. Our findings partially support this theory but clarify that the mechanism is not deliberate risk recalibration but automatic adjustment to environmental affordances. While crosswalks significantly increased perceived safety, they simultaneously reduced vigilant behaviors, suggesting that risk homeostasis operates below the threshold of conscious awareness. This distinction is crucial: pedestrians do not intentionally decide to take greater risks because they feel safer; instead, implicit design cues reduce vigilance by default.\u003c/p\u003e\u003cp\u003eIn conclusion, effective pedestrian infrastructure does not work primarily by changing perceptions but structuring behavioral responses through both conscious and implicit pathways. Urban design should therefore prioritize behavioral guidance over subjective safety enhancement, ensuring that infrastructure not only protects pedestrians physically but also sustains their active engagement with the environment. Crosswalks should function not just as protective boundaries but as behavioral interfaces that activate vigilance rather than passive reliance. Future interventions should incorporate action-oriented design features that sustain cognitive engagement, particularly for populations less responsive to traditional crosswalk infrastructure. By recognizing infrastructure as both a physical and psychological interface, planners can design urban environments that foster safety, trust, and efficient pedestrian movement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHyunjoo Eom: Conceptualization; Funding acquisition; Formal analysis; Writing\u0026mdash;original draftJinho Won: Formal analysis; Data curation; MethodologyGi-Hyoug Cho: Conceptualization; Funding acquisition; Supervision; Project administration; Writing\u0026mdash;review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: This work was supported by the National Research Foundation (NRF) grant funded by the Korea Government (MSIT) (RS-2021-NR059071 and RS-2022-NR072468)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Informed Consent\u003c/strong\u003e: This study was approved by the Institutional Review Board of UNIST (IRB No. UNISTIRB-23-043-A). All participants provided informed consent prior to participation in the experiment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBendak, Salaheddine, Asayel M. Alnaqbi, Muna Y. Alzarooni, Sara M. Aljanaahi, and Shaikha J. Alsuwaidi. 2021. \u0026ldquo;Factors Affecting Pedestrian Behaviors at Signalized Crosswalks: An Empirical Study.\u0026rdquo; \u003cem\u003eJournal of Safety Research\u003c/em\u003e 76 (February): 269\u0026ndash;75. https://doi.org/10.1016/j.jsr.2020.12.019.\u003c/li\u003e\n\u003cli\u003eButler, Annie A., Stephen R. Lord, and Richard C. 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Using immersive virtual reality (VR) simulations of urban crossings, participants completed multiple trials measuring head scanning, yielding, crossing speed, and perceived safety and collision risk. Linear mixed-effects models and path-decomposition analyses were employed to assess whether behavioral differences across crosswalk types were consistent with perception-mediated processes. Results indicate that zebra and interactive crosswalks significantly increased perceived safety and reduced perceived collision risk. However, behavioral adaptations—reduced scanning, lower yielding, and slower speed—were largely independent of these perceptual shifts. The decomposition analysis revealed a suppression pattern: safety perception encouraged scanning, while crosswalk affordances directly reduced vigilance, suggesting that implicit cues outweighed conscious appraisal. These findings imply that pedestrian behavior is primarily guided by automatic affordance-based mechanisms rather than deliberate perception, highlighting a potential false-safety effect in crosswalk design.","manuscriptTitle":"How Crosswalk Affordances Influence Pedestrian Vigilance and Safety Perception","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:17:21","doi":"10.21203/rs.3.rs-7979619/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e3152d7-2d99-40b3-9458-23b9d67c10ae","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T09:25:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:17:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7979619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7979619","identity":"rs-7979619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}