Application of Virtual Simulation Technology in the Measurement of Pedestrian Personality Traits

Application of Virtual Simulation Technology in the Measurement of Pedestrian Personality Traits | 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 Application of Virtual Simulation Technology in the Measurement of Pedestrian Personality Traits Zhefeng An, Long Li, Jingxuan Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7109404/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 Pedestrians' personality traits partially determine their walking styles. A deeper understanding of pedestrian walking styles holds significant implications for research in the field of traffic safety. However, current studies lack measurement tools with high ecological validity for assessing pedestrians' personality traits. Virtual simulation technology, as an innovative approach capable of realistically simulating real-world scenarios, offers novel methods and tools in the field of psychological measurement. This study utilized AnyLogic virtual simulation software to develop the "Pedestrian Walking Simulation Video Scale," consisting of 20 simulated pedestrian walking videos, and conducted experimental validation with 250 participants and 35 retest participants. Results indicated that the video scale demonstrated high test-retest reliability (Kappa values ranging from 0.70 to 0.78) and moderate criterion-related validity (Spearman correlation coefficient = 0.378). Future studies should further optimize the design of video scales to enhance ecological validity, thereby promoting broader applications of virtual simulation technology in psychological measurement and personality assessment research. virtual simulation technology AnyLogic psychological measurement personality assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In daily life, we encounter countless pedestrians every day. Have you ever wondered why some people walk quickly, while others prefer to walk slowly with their heads down? Some people tend to follow the crowd, while others prefer to walk alone. These different behavioral patterns may be closely related to individual personality traits(Satchell et al., 2017 ; Zheng et al., 2017 ). It has been shown that personality can influence various aspects of life, such as consumption habits, performance, interpersonal relationships, mental health, and even political stance(Cai and Liu, 2022 ). In previous personality measurement studies, researchers typically used various personality scales for assessment, such as the Big Five Personality Traits (Big-5), the Tridimensional Personality Questionnaire (TPQ), and the Eysenck Personality Questionnaire (EPQ). Although these scales provide detailed personality information and have high accuracy, they often require participants to complete a long self-report questionnaire within 5 to 15 minutes. During this process, participants may experience boredom, fatigue, and frustration, which can reduce their focus on the questionnaire content, potentially affecting the reliability and validity of the data(Credé et al., 2012 ; Hilliard et al., 2022 ). More importantly, when participants make complex judgments about specific tasks, they often overlook contextual factors, which play a crucial role in task-related judgments(Rejeski et al., 2010 ), thus impacting the ecological validity of the scales. With the rise of the digital age, technology may overcome these issues(Woods et al., 2020 ). Virtual simulation technology allows researchers to simulate different scenarios in controlled and safe environments and measure individual responses. For example, computer-based gamified assessments not only provide sufficient precision in psychological measurement but also encourage natural reactions from participants, reducing the risk of cheating during the assessment process(Melchers and Basch, 2022 ; Willis et al., 2021 ). However, in the current body of literature on psychological measurement and evaluation, research exploring the use of virtual simulation technology to simulate individual behavior patterns for psychological assessment remains scarce. Therefore, this paper aims to explore how AnyLogic virtual simulation software can be used to create pedestrian walking simulation videos in various scenarios to measure and analyze different personalities based on walking styles in different contexts. AnyLogic is a powerful modeling and simulation tool that can simulate complex dynamic systems and diverse behavioral patterns(Niu et al., 2023 ). By designing different pedestrian simulation scenarios, such as corners, head-to-head encounters, walking in the same direction, and narrow exits, we can observe and record the behavioral characteristics of participants in these virtual environments, thus assessing their personality traits. Specifically, this study designed four different virtual pedestrian scenarios, each presenting walking videos of pedestrians with five different personality traits. Participants were asked to select the video that best matched their own walking style in each scenario and then complete a standardized personality scale to assess their personality traits. Using this method, researchers will validate whether the personality traits reflected in the walking videos chosen by participants in virtual scenarios align with those measured by the personality scale. This approach not only provides intuitive and dynamic behavioral data but also enhances the fun of psychological measurement and evaluation using virtual simulation technology compared to traditional methods, reducing the subjectivity of self-reported measures. Through this study, we aim to reveal the potential of virtual simulation technology in psychological measurement, promoting innovation in this field, and providing new tools and methods for applications in education, psychological counseling, and human resource management. 2. Materials and methods This section provides an overview of the specific tools and methods used in this study. According to scholar Guy’s description of behavioral differences, he believes that the adjectives "aggressive," "impulsive," "confident," "shy," "nervous," and "active" comprehensively describe individual behavior within a group(Guy et al., 2011). However, when observing different pedestrian walking styles in real-life scenarios, it was found that "confident" and "active" traits are not easily distinguishable by walking styles. Therefore, in this study, these two traits were replaced with "steady," a common personality trait possessed by most people(Sun and Chen, 2023 ). Based on these five adjectives representing pedestrian walking styles, this study created corresponding simulation videos using AnyLogic software and compiled a personality trait scale. 2.1 Instruments and Materials 1. Virtual Simulation Software: In this study, AnyLogic Professional Edition 8.5 was used as the virtual simulation software tool. AnyLogic is a powerful multi-method simulation software suitable for simulating complex dynamic systems and diverse human behaviors. 2. Pedestrian Walking Style Questionnaire: The questionnaire consists of two parts: the first part is the "Pedestrian Walking Simulation Video Scale," and the second part is the "Pedestrian Walking Style Scale." The research population includes commuters, students, and other pedestrians who frequently take the subway. To ensure the validity of the data, the initial questionnaire screening set the following standards: a. The time taken to complete the questionnaire must not be less than 200 seconds. b. The questionnaire must be fully completed with no missing answers. c. There must be no obvious random response patterns in the answers. d. The results should clearly distinguish between different personality traits. Pedestrian Walking Simulation Video Scale: In this study, AnyLogic was used to create four different simulation scenarios: a corner, head-to-head encounter, walking in the same direction, and narrow exits (see Figs. 1 – 4 ). In each virtual scenario, videos of pedestrians with five different personality traits were created, each with a length of 8 seconds, totaling 20 videos. In each video, a black virtual pedestrian represents the experimental subject. Participants were asked to observe the walking style of the black virtual pedestrian in each scenario and select the video that most closely matched their own walking style. These videos displayed typical behavioral patterns of different personality traits in specific scenarios, such as walking speed, path choice, and interaction with other pedestrians. Based on observations of real-world pedestrian behavior and references to descriptions of pedestrian personality traits from related literature(Guy et al., 2011), the aggressive personality walks a relatively straight path, doesn’t mind bumping into others, and usually does not yield when encountering others; the impulsive personality also walks straight, at a faster pace, often overtaking other pedestrians but without collisions; the shy personality prefers less crowded areas and likes to walk alone or with a few others; the nervous personality tends to follow the crowd and is easily influenced by others; the steady personality walks in their own way without being influenced by others. Pedestrian Walking Style Scale: The scale used in this study combined traditional personality test questionnaires with self-developed items. Traditional personality tests are mostly based on psychological theories like the Big Five Personality Traits and the Eysenck Personality Model, providing comprehensive assessments of individual psychological traits. These standardized measurements usually include objective questions to understand participants' emotional preferences, behavioral responses, or views in specific situations. By including such questions, researchers can effectively identify and measure participants' underlying personality factors, thus providing a basis for studying individual behavior traits. In addition, based on observed differences in real-world pedestrian behavior, self-developed questions such as "I like walking alone or with a few people" and "Others usually yield to me when encountering me" were included to more accurately judge participants' psychological tendencies during walking. The "Pedestrian Walking Style Scale" contains five dimensions: aggression, impulsivity, shyness, nervousness, and steadiness. The scale has 41 items, scored on a Likert scale with five options: "Very true," "Somewhat true," "Neutral," "Somewhat untrue," and "Not true". 2.2 Participants To facilitate sampling and statistical analysis, we distributed the questionnaires using an online survey platform. A total of 290 participants completed the survey, and after excluding 40 invalid questionnaires based on the above criteria, we obtained 250 valid responses, with an effective response rate of 86%. Additionally, we selected 35 participants to undergo a repeat measurement of the "Pedestrian Walking Simulation Video Scale" 7–10 days later. This study was reviewed and approved by the Ethics Committee of Beijing University of Technology (Certificate Number: BJUTCOM-202503-001). The research involving human participants was conducted in accordance with internationally recognized ethical standards, including the Declaration of Helsinki, and followed the relevant national regulations on research ethics in China. All participants provided written informed consent prior to participation. 2.3 Methodology In the "Pedestrian Walking Simulation Video Scale," each scenario contained five different pedestrian walking videos, each representing a distinct personality trait. Participants were asked, "Which video best represents your walking style?" to force them to choose one video that most closely matched their own walking style. If participants chose the same personality video in at least two or more scenarios, they were categorized into the corresponding personality type. In the "Pedestrian Walking Style Scale," the average scores across the five dimensions were compared, and participants were categorized into the personality type with the highest score. Data analysis was conducted using SPSS 25.0 statistical software. The data from the "Pedestrian Walking Style Scale" were subjected to item analysis, validity analysis, and reliability analysis. The data from the "Pedestrian Walking Simulation Video Scale" were examined for test-retest reliability and criterion-related validity to verify the reliability and validity of the research findings. 3. Results 3.1 Pedestrian Walking Style Scale 3.1.1 Item analysis The item analyses were conducted using the critical ratio method. The critical ratio method was employed by calculating the total scores of the scale entries of the samples and sorting them from high to low according to the total scores. The samples with the top 27% and the bottom 27% of the total scores were selected as the high and low subgroups, respectively. Subsequently, the scores of each entry in the high and low subgroups were compared through independent samples t-tests, and a statistically significant difference was observed ( p < 0.05). This indicated that the entries exhibited a notable degree of differentiation and were therefore retained, whereas those with lesser differentiation were excluded. The formula is as follows: , D represents the degree of differentiation, X 1 denotes the average score of the high grouping, X 2 signifies the average score of the low grouping, and N refers to the number of individuals in each group. The results demonstrated that there were no statistically significant differences ( p > 0.05) between the high and low subgroups for questions 4, 13, 16, 20, 30, 31, 35, 36, and 40 on all items. Consequently, these questions were excluded. 3.1.2 Validity analysis The suitability of the data for exploratory factor analysis was confirmed by the KMO value (0.89) and the significance of Bartlett's test of sphericity (p < 0.001). The data were subjected to principal component analysis using the varimax rotation method. The results indicated that five factors were optimal for extraction. Therefore, the number of factors was restricted to five. According to the factor analysis results, items with high and similar factor coefficients or with loadings below 0.40 were excluded. Ultimately, 32 items were retained, representing five dimensions—aggression, impulsivity, shyness, nervousness, and steadiness—with a cumulative explained variance of 66.30%, as shown in Table 1 . Table 1 Factor loadings for each dimension steadiness impulsivity shyness aggression nervousness item loading item loading item loading item loading item loading 5 0.845 2 0.825 8 0.804 3 0.774 1 0.793 17 0.877 6 0.772 9 0.810 14 0.786 10 0.773 19 0.845 7 0.856 11 0.796 15 0.808 12 0.788 41 0.862 22 0.783 18 0.797 23 0.770 26 0.797 24 0.811 21 0.778 25 0.682 27 0.768 28 0.837 34 0.761 29 0.834 33 0.809 32 0.765 37 0.791 39 0.835 38 0.816 3.1.3 Reliability analysis The Conbach's alpha coefficients for the overall scale and the five sub-dimensions of steadiness, impulsivity, shyness, aggression, and tension were 0.823, 0.892, 0.937, 0.862, 0.865, and 0.918, respectively. According to Wu Minglong's criteria for integrating various scholars, the overall Cronbach's alpha coefficients of the scale should be 0.80 or above, while the Cronbach's alpha coefficient of each subscale should be 0.70 or above. They have also established that the Cronbach's alpha coefficients of each subscale should be between 0.60 and 0.70, which is an acceptable range. As evidenced by the results, the Cronbach's alpha for each dimension of the Pedestrian Walking Style Scale is in accordance with the requisite standards for the study, thereby indicating that the scale exhibits satisfactory internal consistency reliability. 3.2 Measurement Metrics of the Pedestrian Walking Simulation Video Scale After determining the items for the Pedestrian Walking Simulation Video Scale, we tested its validity and reliability. First, a chi-square goodness-of-fit test was conducted on the number of participants who selected each personality video in the four scenarios to examine the randomness of their choices. The test results were as follows:In the "corner" scenario, = 22.92, P < 0.001.In the "head-on encounter" scenario, =21.52, P < 0.001.In the "same-direction walking" scenario, =30.12, P < 0.001.In the "narrow exit" scenario, =14.80, P < 0.005.These results indicate that, at a 95% confidence level, participants' choices of the five personality videos in the four scenarios were not random. Based on this, the Pedestrian Walking Style Scale was used as the criterion, and Spearman correlation analysis was conducted to compare the personality traits measured by the two scales among the 250 participants. The results showed that the overall Spearman correlation coefficient between the Pedestrian Walking Simulation Video Scale and the Pedestrian Walking Style Scale was r = 0.378, P < 0.01, indicating a moderate positive correlation between the two. Additionally, the Spearman correlation coefficients between each scenario of the Pedestrian Walking Simulation Video Scale (corner, head-on encounter, same-direction walking, and narrow exit) and the Pedestrian Walking Style Scale were r = 0.303 (P < 0.01), r = 0.352 (P < 0.01), r = 0.322 (P < 0.01), and r = 0.187 (P < 0.01), respectively. Except for the "narrow exit" scenario, the other scenarios showed moderate correlations with the Pedestrian Walking Style Scale. We also conducted a test-retest reliability check on the Pedestrian Walking Simulation Video Scale for 35 participants, with a retest interval of 7–10 days. The Kappa consistency coefficients for each scenario were as follows: Corner: K = 0.73, Head-to-head encounter: K = 0.779, Walking in the same direction: K = 0.73, Narrow exit: K = 0.70. The Kappa coefficients, which range from 0 to 1, suggest that higher values indicate greater consistency. The results show that the Pedestrian Walking Simulation Video Scale has high stability across measurements. Furthermore, to better support the validity of the Pedestrian Walking Simulation Video Scale, we conducted a Kappa consistency check between the two parts of the Pedestrian Walking Style Scale to determine if both tools produced consistent results for the same group of participants. The Kappa coefficient was K = 0.343, P < 0.01, indicating a moderate level of agreement between the two methods in classifying personality traits. 3.3 Descriptive Analysis of the Pedestrian Walking Style Questionnaire A descriptive statistical analysis of the Pedestrian Walking Simulation Video Scale revealed the following distribution of personality types among the participants: 81 (32.40%) were classified as steady, 52 (20.80%) as impulsive, 43 (17.20%) as shy, 36 (14.40%) as aggressive, and 38 (15.20%) as nervous. The detailed distribution of video selections across the four simulated scenarios is summarized in Table 2 . Table 2 Number of Participants Who Chose Each Video for Each Scenario in the Pedestrian Walking Simulation Video Scale video scene Corners Head-to-head encounter Walking in the same direction Narrow exits 1(Steady type) 78 71 78 55 2(Impulsive type) 51 58 60 59 3(Shy type) 44 49 47 26 4(Aggressive type) 44 31 32 57 5(Nervous type) 33 39 33 53 Total 250 250 250 250 As illustrated in Fig. 5 , the descriptive analysis of the Pedestrian Walking Style Scale showed that the majority of participants (40.40%) were identified as having a steady personality. The remaining distribution included impulsive (16.40%), shy (17.60%), aggressive (9.60%), and nervous (16.00%) personality types. 4. Discussion In this study, we used virtual simulation technology, particularly AnyLogic software, to construct various pedestrian simulation scenarios to measure individual personality traits. By conducting repeat measurements on 35 participants and comparing the results of the "Pedestrian Walking Simulation Video Scale" and "Pedestrian Walking Style Scale" for 250 participants, we tested the reliability and validity of the "Pedestrian Walking Simulation Video Scale." The high test-retest reliability and moderate criterion-related validity provide strong preliminary support for the reliability of the video scale. On the basis of verifying reliability and validity, we discovered several interesting phenomena. Among the various pedestrian simulation scenarios, the "same-direction walking" scenario showed the highest correlation with the "Pedestrian Walking Style Scale," while the "narrow exit" scenario showed the lowest correlation. The research found that, in the "narrow exit" scenario, the number of participants choosing the "aggressive" video increased significantly. This may be because narrow exits increase the likelihood of collisions, and previous research by Aghabayk has shown that collision experiences can increase the likelihood of pedestrians expressing anger(Aghabayk et al., 2022 ). Furthermore, in the narrow exit scenario, the number of participants choosing the "shy" video decreased, meaning fewer participants opted for walking alone or with a few others, while the number of participants choosing the "nervous" video increased, reflecting a preference for walking closely behind others. This could be because, in the narrow exit scenario, crowds congregate at the exit, diminishing individual walking preferences. In previous studies, personality has been shown to be stable, with personality traits generally not easily changing. However, these changes indicate that individual behavior can change with the environment(Martin et al., 2023 ). This may also explain the lower correlation between the "Pedestrian Walking Simulation Video Scale" and the "Pedestrian Walking Style Scale." During the development of the video scale, two key issues were content reliability and validity. Currently, studies on the reliability and validity of video scales are relatively scarce. Existing research on video scale validity focuses primarily on criterion-related validity, content validity, construct validity(Liegl et al., 2023 ; Susanty et al., 2023 ; Rejeski et al., 2010 ; Guerra et al., 2014 ), and convergent validity(Peter et al., 2015 ; Marsh et al., 2015 ), methods that are not significantly different from those used to validate traditional scales. However, reliability testing for video scales has faced challenges. Almost all studies only use test-retest reliability testing(Liegl et al., 2023 ; Susanty et al., 2023 ; Rejeski et al., 2010 ; Peter et al., 2015 ; Guerra et al., 2014 ; Marsh et al., 2015 ). This raises the question: are traditional reliability testing methods unsuitable for video scales? The core issue lies in the fact that video scales mostly collect categorical data. For instance, participants may be asked to "select the animation that best represents your activity limitation level(Liegl et al., 2023 )," which directly categorizes participants without generating a specific score. Traditional reliability testing methods, such as split-half reliability and internal consistency reliability, require data to be at least interval-scaled and therefore do not apply to categorical data. Although some video scales can be treated as binary scales (e.g., choosing a specific video is akin to answering "yes," while unchosen videos are akin to answering "no"), reliability tests for binary scales typically require the questions to be independent of each other. That is, the answer to one question should not affect the answers to others. In video scales, however, many video options are not independent. For example, in this study, participants could only choose one personality video in a given scenario (choosing a video is equivalent to answering "yes" for that personality, while not choosing other videos is equivalent to answering "no"). This characteristic limits the applicability of many traditional reliability and validity testing methods to video scales. Therefore, designing questions in ways that better suit different types of scales is crucial. As a novel measurement tool, the use of video scales in psychological measurement represents an important methodological innovation. Currently, video scales are primarily applied in fields such as traffic and elderly mobility. For example, in the field of traffic, virtual simulation technology has been used to simulate the evacuation behavior of crowds with different personality traits(Wang et al., 2021 ); in elderly mobility, it has been used to assess physical activity capacity in older adults(Marsh et al., 2011 ). These applications share the common characteristic of measuring behaviors that are easily observable, allowing objective data to be collected through direct observation or measurement. However, in psychological assessments, many internal traits (e.g., laziness, confidence) are difficult to assess through external behaviors alone. Researchers need to carefully design virtual scenarios that elicit behaviors related to these internal traits, avoiding reliance on subjective self-reporting. Developing appropriate video scales using virtual simulation technology to assess these less observable internal traits remains a pressing challenge. Although this study yielded positive results, it also has some limitations. First, the sample size was relatively small, and the participants came from specific populations, which may limit the generalizability of the results. Second, while virtual simulation technology can simulate real-world scenarios, virtual environments still differ from real-life situations to some extent, potentially influencing participants' real reactions. Additionally, the pedestrian simulation scenarios designed in this study were relatively simple and did not fully account for more complex social interactions and contextual variables, which may have affected the comprehensive assessment of different personality traits. Future research should focus on simulating scenarios that more closely resemble real-life situations to enhance the applicability of video scales in real-world contexts. The widespread application of ChatGPT in education(An and Ma, 2024 ) provides a solid foundation for introducing it into the field of psychological measurement. With ChatGPT's powerful language processing capabilities, it could be used to generate text-based examples according to specific assessment needs, which could then be turned into simulation videos through virtual simulation software. For instance, scenarios could be created to measure individual behavior in various social situations, such as interactions in public places or decision-making styles in work environments, which could be more realistically simulated through virtual technology. Moreover, future studies should explore how to develop lightweight, user-friendly video scales for common platforms such as mobile devices and social media, making them more accessible to a broader audience. By integrating measurement tools into daily life, researchers can more authentically capture individual behavioral characteristics, avoiding the subjectivity and boredom often associated with traditional self-report scales. This practical application can not only improve the accuracy of psychological assessments but also increase the ecological validity of the scales. 5. Conclusion This study developed the "Pedestrian Walking Simulation Video Scale" using virtual simulation technology and tested its validity and reliability. The results not only confirm the scientific and feasible use of virtual simulation technology as a tool for psychological measurement but also highlight its potential in broader applications. This study is not meant to replace traditional scales but to attempt the development of a more ecologically valid psychological measurement tool. The introduction of virtual simulation technology provides a more intuitive and dynamic new method for psychological research, helping to overcome the limitations of traditional questionnaire methods and promoting the further development of individual behavior assessments. Declarations Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of Beijing University of Technology (Certificate Number: BJUTCOM-202503-001). All participants provided informed consent prior to participation. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the Beijing Ideological and Political Work Research Project for Universities (Grant No. BJSZ2024ZL01) and the Beijing Social Science Foundation (Grant No. 24GJA001). Author Contribution Z.A. was responsible for the study design. L.L. wrote the manuscript and conducted data analysis. J.P. assisted with simulation modeling. All authors reviewed and approved the final manuscript. Acknowledgements We would like to express our sincere gratitude to all the participants who took part in this study. We also thank the research assistants who supported data collection and video production. This work was supported by the Beijing Ideological and Political Work Research Project for Universities (Strategic Project, Grant No. BJSZ2024ZL01), and the Beijing Social Science Foundation (General Project, Grant No. 24GJA001). The study forms part of the ongoing research project entitled "Research on the Application and Countermeasures of Generative AI in Ideological and Political Education in Universities." Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions. References Aghabayk K, Rejali S, Shiwakoti N. 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Zheng T, Qu W, Ge Y, Sun X, Zhang K. (2017). The joint effect of personality traits and perceived stress on pedestrian behavior in a Chinese sample. PLoS ONE, 12(11), e0188153. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7109404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508063220,"identity":"ccee269a-1afc-4162-b74e-a9ff6368c025","order_by":0,"name":"Zhefeng An","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhefeng","middleName":"","lastName":"An","suffix":""},{"id":508063221,"identity":"75ac0944-0097-44a5-87f4-6ff0d59663ef","order_by":1,"name":"Long Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3RPQuCQBjA8UeEs+GhWQjOTxAYB4Eg9lUUwcmlJRyFoBaj1cFvEThfHNRizY5F0ORgW2OONXltQffff/C8AKhUPxgxtq14Ji6dHZdcjgyRTwRUEYPq4MsRavpMaCsRpHVsSw5m+r6YryKmpfGjbsCj47SP4IWL/OxSHU47p4CQTXkfMYKU4yJiRNuUIwQelL0EQuBIRJDpeJckgwgEduvnBIkkwQr2eXdkGwlzCltiF2ud6W3bvdK2bte6STzaSz4yUfI17+RboVKpVH/RC8wbRRTIzYyfAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Li","suffix":""},{"id":508063222,"identity":"71fcbae9-bf5b-4300-86ab-1e494e83e622","order_by":2,"name":"Jingxuan Peng","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingxuan","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-07-12 16:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7109404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7109404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90812175,"identity":"a85d108c-b7d5-439f-903a-a45af589e4d3","added_by":"auto","created_at":"2025-09-08 12:17:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60811,"visible":true,"origin":"","legend":"\u003cp\u003eCorner\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109404/v1/e3a5f85114d8cfb808105923.jpg"},{"id":90812173,"identity":"edd50ac0-8eda-4dba-8ed1-574d6a1b6425","added_by":"auto","created_at":"2025-09-08 12:17:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51268,"visible":true,"origin":"","legend":"\u003cp\u003eHead-to-head encounter\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109404/v1/8354ec836d3473f119ea0e55.jpg"},{"id":90813121,"identity":"ddd9d46a-76b2-4fdf-b96d-bfafe8209b7d","added_by":"auto","created_at":"2025-09-08 12:25:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54147,"visible":true,"origin":"","legend":"\u003cp\u003eNarrow exit\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109404/v1/0109e8a972bad756602e9f35.jpg"},{"id":90812171,"identity":"a97f02ce-55c2-4d61-b895-ccc99ce27e04","added_by":"auto","created_at":"2025-09-08 12:17:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52999,"visible":true,"origin":"","legend":"\u003cp\u003eWalking in the same direction\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109404/v1/1c3e895a4f56477a638920eb.jpg"},{"id":90813528,"identity":"11adc4dc-b86e-424b-b525-9a49f626af9e","added_by":"auto","created_at":"2025-09-08 12:33:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58842,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Statistical Analysis of the Pedestrian Walking Style Scale\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7109404/v1/aff670d992d39d311b809246.jpg"},{"id":95312179,"identity":"90d137d5-6c4e-4a77-becd-1bd8c187d6dc","added_by":"auto","created_at":"2025-11-06 15:47:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7109404/v1/dd07a5ff-53aa-45b5-a2aa-c619bbd2a90d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Virtual Simulation Technology in the Measurement of Pedestrian Personality Traits","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn daily life, we encounter countless pedestrians every day. Have you ever wondered why some people walk quickly, while others prefer to walk slowly with their heads down? Some people tend to follow the crowd, while others prefer to walk alone. These different behavioral patterns may be closely related to individual personality traits(Satchell et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It has been shown that personality can influence various aspects of life, such as consumption habits, performance, interpersonal relationships, mental health, and even political stance(Cai and Liu, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn previous personality measurement studies, researchers typically used various personality scales for assessment, such as the Big Five Personality Traits (Big-5), the Tridimensional Personality Questionnaire (TPQ), and the Eysenck Personality Questionnaire (EPQ). Although these scales provide detailed personality information and have high accuracy, they often require participants to complete a long self-report questionnaire within 5 to 15 minutes. During this process, participants may experience boredom, fatigue, and frustration, which can reduce their focus on the questionnaire content, potentially affecting the reliability and validity of the data(Cred\u0026eacute; et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hilliard et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). More importantly, when participants make complex judgments about specific tasks, they often overlook contextual factors, which play a crucial role in task-related judgments(Rejeski et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thus impacting the ecological validity of the scales.\u003c/p\u003e\u003cp\u003eWith the rise of the digital age, technology may overcome these issues(Woods et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Virtual simulation technology allows researchers to simulate different scenarios in controlled and safe environments and measure individual responses. For example, computer-based gamified assessments not only provide sufficient precision in psychological measurement but also encourage natural reactions from participants, reducing the risk of cheating during the assessment process(Melchers and Basch, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Willis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, in the current body of literature on psychological measurement and evaluation, research exploring the use of virtual simulation technology to simulate individual behavior patterns for psychological assessment remains scarce. Therefore, this paper aims to explore how AnyLogic virtual simulation software can be used to create pedestrian walking simulation videos in various scenarios to measure and analyze different personalities based on walking styles in different contexts. AnyLogic is a powerful modeling and simulation tool that can simulate complex dynamic systems and diverse behavioral patterns(Niu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By designing different pedestrian simulation scenarios, such as corners, head-to-head encounters, walking in the same direction, and narrow exits, we can observe and record the behavioral characteristics of participants in these virtual environments, thus assessing their personality traits.\u003c/p\u003e\u003cp\u003eSpecifically, this study designed four different virtual pedestrian scenarios, each presenting walking videos of pedestrians with five different personality traits. Participants were asked to select the video that best matched their own walking style in each scenario and then complete a standardized personality scale to assess their personality traits. Using this method, researchers will validate whether the personality traits reflected in the walking videos chosen by participants in virtual scenarios align with those measured by the personality scale. This approach not only provides intuitive and dynamic behavioral data but also enhances the fun of psychological measurement and evaluation using virtual simulation technology compared to traditional methods, reducing the subjectivity of self-reported measures.\u003c/p\u003e\u003cp\u003eThrough this study, we aim to reveal the potential of virtual simulation technology in psychological measurement, promoting innovation in this field, and providing new tools and methods for applications in education, psychological counseling, and human resource management.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eThis section provides an overview of the specific tools and methods used in this study. According to scholar Guy\u0026rsquo;s description of behavioral differences, he believes that the adjectives \u0026quot;aggressive,\u0026quot; \u0026quot;impulsive,\u0026quot; \u0026quot;confident,\u0026quot; \u0026quot;shy,\u0026quot; \u0026quot;nervous,\u0026quot; and \u0026quot;active\u0026quot; comprehensively describe individual behavior within a group(Guy et al., 2011). However, when observing different pedestrian walking styles in real-life scenarios, it was found that \u0026quot;confident\u0026quot; and \u0026quot;active\u0026quot; traits are not easily distinguishable by walking styles. Therefore, in this study, these two traits were replaced with \u0026quot;steady,\u0026quot; a common personality trait possessed by most people(Sun and Chen, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on these five adjectives representing pedestrian walking styles, this study created corresponding simulation videos using AnyLogic software and compiled a personality trait scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Instruments and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. Virtual Simulation Software: In this study, AnyLogic Professional Edition 8.5 was used as the virtual simulation software tool. AnyLogic is a powerful multi-method simulation software suitable for simulating complex dynamic systems and diverse human behaviors.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. Pedestrian Walking Style Questionnaire: The questionnaire consists of two parts: the first part is the \u0026quot;Pedestrian Walking Simulation Video Scale,\u0026quot; and the second part is the \u0026quot;Pedestrian Walking Style Scale.\u0026quot; The research population includes commuters, students, and other pedestrians who frequently take the subway. To ensure the validity of the data, the initial questionnaire screening set the following standards:\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ea. The time taken to complete the questionnaire must not be less than 200 seconds.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003eb. The questionnaire must be fully completed with no missing answers.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003ec. There must be no obvious random response patterns in the answers.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003ed. The results should clearly distinguish between different personality traits.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003ePedestrian Walking Simulation Video Scale:\u003c/p\u003e\n\u003cp\u003eIn this study, AnyLogic was used to create four different simulation scenarios: a corner, head-to-head encounter, walking in the same direction, and narrow exits (see Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In each virtual scenario, videos of pedestrians with five different personality traits were created, each with a length of 8 seconds, totaling 20 videos. In each video, a black virtual pedestrian represents the experimental subject. Participants were asked to observe the walking style of the black virtual pedestrian in each scenario and select the video that most closely matched their own walking style. These videos displayed typical behavioral patterns of different personality traits in specific scenarios, such as walking speed, path choice, and interaction with other pedestrians. Based on observations of real-world pedestrian behavior and references to descriptions of pedestrian personality traits from related literature(Guy et al., 2011), the aggressive personality walks a relatively straight path, doesn\u0026rsquo;t mind bumping into others, and usually does not yield when encountering others; the impulsive personality also walks straight, at a faster pace, often overtaking other pedestrians but without collisions; the shy personality prefers less crowded areas and likes to walk alone or with a few others; the nervous personality tends to follow the crowd and is easily influenced by others; the steady personality walks in their own way without being influenced by others.\u003c/p\u003e\n\u003cp\u003ePedestrian Walking Style Scale:\u003c/p\u003e\n\u003cp\u003eThe scale used in this study combined traditional personality test questionnaires with self-developed items. Traditional personality tests are mostly based on psychological theories like the Big Five Personality Traits and the Eysenck Personality Model, providing comprehensive assessments of individual psychological traits. These standardized measurements usually include objective questions to understand participants\u0026apos; emotional preferences, behavioral responses, or views in specific situations. By including such questions, researchers can effectively identify and measure participants\u0026apos; underlying personality factors, thus providing a basis for studying individual behavior traits. In addition, based on observed differences in real-world pedestrian behavior, self-developed questions such as \u0026quot;I like walking alone or with a few people\u0026quot; and \u0026quot;Others usually yield to me when encountering me\u0026quot; were included to more accurately judge participants\u0026apos; psychological tendencies during walking.\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;Pedestrian Walking Style Scale\u0026quot; contains five dimensions: aggression, impulsivity, shyness, nervousness, and steadiness. The scale has 41 items, scored on a Likert scale with five options: \u0026quot;Very true,\u0026quot; \u0026quot;Somewhat true,\u0026quot; \u0026quot;Neutral,\u0026quot; \u0026quot;Somewhat untrue,\u0026quot; and \u0026quot;Not true\u0026quot;.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Participants\u003c/h2\u003e\n \u003cp\u003eTo facilitate sampling and statistical analysis, we distributed the questionnaires using an online survey platform. A total of 290 participants completed the survey, and after excluding 40 invalid questionnaires based on the above criteria, we obtained 250 valid responses, with an effective response rate of 86%. Additionally, we selected 35 participants to undergo a repeat measurement of the \u0026quot;Pedestrian Walking Simulation Video Scale\u0026quot; 7\u0026ndash;10 days later.\u003c/p\u003e\n \u003cp\u003eThis study was reviewed and approved by the Ethics Committee of Beijing University of Technology (Certificate Number: BJUTCOM-202503-001). The research involving human participants was conducted in accordance with internationally recognized ethical standards, including the Declaration of Helsinki, and followed the relevant national regulations on research ethics in China. All participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Methodology\u003c/h2\u003e\n \u003cp\u003eIn the \u0026quot;Pedestrian Walking Simulation Video Scale,\u0026quot; each scenario contained five different pedestrian walking videos, each representing a distinct personality trait. Participants were asked, \u0026quot;Which video best represents your walking style?\u0026quot; to force them to choose one video that most closely matched their own walking style. If participants chose the same personality video in at least two or more scenarios, they were categorized into the corresponding personality type. In the \u0026quot;Pedestrian Walking Style Scale,\u0026quot; the average scores across the five dimensions were compared, and participants were categorized into the personality type with the highest score.\u003c/p\u003e\n \u003cp\u003eData analysis was conducted using SPSS 25.0 statistical software. The data from the \u0026quot;Pedestrian Walking Style Scale\u0026quot; were subjected to item analysis, validity analysis, and reliability analysis. The data from the \u0026quot;Pedestrian Walking Simulation Video Scale\u0026quot; were examined for test-retest reliability and criterion-related validity to verify the reliability and validity of the research findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Pedestrian Walking Style Scale\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Item analysis\u003c/h2\u003e\u003cp\u003eThe item analyses were conducted using the critical ratio method. The critical ratio method was employed by calculating the total scores of the scale entries of the samples and sorting them from high to low according to the total scores. The samples with the top 27% and the bottom 27% of the total scores were selected as the high and low subgroups, respectively. Subsequently, the scores of each entry in the high and low subgroups were compared through independent samples t-tests, and a statistically significant difference was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicated that the entries exhibited a notable degree of differentiation and were therefore retained, whereas those with lesser differentiation were excluded. The formula is as follows: \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e, \u003cem\u003eD\u003c/em\u003e represents the degree of differentiation, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e denotes the average score of the high grouping, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e signifies the average score of the low grouping, and \u003cem\u003eN\u003c/em\u003e refers to the number of individuals in each group. The results demonstrated that there were no statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the high and low subgroups for questions 4, 13, 16, 20, 30, 31, 35, 36, and 40 on all items. Consequently, these questions were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Validity analysis\u003c/h2\u003e\u003cp\u003eThe suitability of the data for exploratory factor analysis was confirmed by the KMO value (0.89) and the significance of Bartlett's test of sphericity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The data were subjected to principal component analysis using the varimax rotation method. The results indicated that five factors were optimal for extraction. Therefore, the number of factors was restricted to five. According to the factor analysis results, items with high and similar factor coefficients or with loadings below 0.40 were excluded. Ultimately, 32 items were retained, representing five dimensions\u0026mdash;aggression, impulsivity, shyness, nervousness, and steadiness\u0026mdash;with a cumulative explained variance of 66.30%, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor loadings for each dimension\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003esteadiness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eimpulsivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eshyness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eaggression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003enervousness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eloading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eitem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eloading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eitem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eloading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eitem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eloading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eitem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eloading\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\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\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.783\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\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.837\u003c/p\u003e\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\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.809\u003c/p\u003e\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\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.791\u003c/p\u003e\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\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Reliability analysis\u003c/h2\u003e\u003cp\u003eThe Conbach's alpha coefficients for the overall scale and the five sub-dimensions of steadiness, impulsivity, shyness, aggression, and tension were 0.823, 0.892, 0.937, 0.862, 0.865, and 0.918, respectively. According to Wu Minglong's criteria for integrating various scholars, the overall Cronbach's alpha coefficients of the scale should be 0.80 or above, while the Cronbach's alpha coefficient of each subscale should be 0.70 or above. They have also established that the Cronbach's alpha coefficients of each subscale should be between 0.60 and 0.70, which is an acceptable range. As evidenced by the results, the Cronbach's alpha for each dimension of the Pedestrian Walking Style Scale is in accordance with the requisite standards for the study, thereby indicating that the scale exhibits satisfactory internal consistency reliability.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Measurement Metrics of the Pedestrian Walking Simulation Video Scale\u003c/h2\u003e\u003cp\u003eAfter determining the items for the Pedestrian Walking Simulation Video Scale, we tested its validity and reliability. First, a chi-square goodness-of-fit test was conducted on the number of participants who selected each personality video in the four scenarios to examine the randomness of their choices. The test results were as follows:In the \"corner\" scenario, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e=\u0026thinsp;22.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.In the \"head-on encounter\" scenario, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e =21.52, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.In the \"same-direction walking\" scenario, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e =30.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.In the \"narrow exit\" scenario, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e =14.80, P\u0026thinsp;\u0026lt;\u0026thinsp;0.005.These results indicate that, at a 95% confidence level, participants' choices of the five personality videos in the four scenarios were not random.\u003c/p\u003e\u003cp\u003eBased on this, the Pedestrian Walking Style Scale was used as the criterion, and Spearman correlation analysis was conducted to compare the personality traits measured by the two scales among the 250 participants. The results showed that the overall Spearman correlation coefficient between the Pedestrian Walking Simulation Video Scale and the Pedestrian Walking Style Scale was r\u0026thinsp;=\u0026thinsp;0.378, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, indicating a moderate positive correlation between the two. Additionally, the Spearman correlation coefficients between each scenario of the Pedestrian Walking Simulation Video Scale (corner, head-on encounter, same-direction walking, and narrow exit) and the Pedestrian Walking Style Scale were r\u0026thinsp;=\u0026thinsp;0.303 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), r\u0026thinsp;=\u0026thinsp;0.352 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), r\u0026thinsp;=\u0026thinsp;0.322 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and r\u0026thinsp;=\u0026thinsp;0.187 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively. Except for the \"narrow exit\" scenario, the other scenarios showed moderate correlations with the Pedestrian Walking Style Scale. We also conducted a test-retest reliability check on the Pedestrian Walking Simulation Video Scale for 35 participants, with a retest interval of 7\u0026ndash;10 days. The Kappa consistency coefficients for each scenario were as follows: Corner: K\u0026thinsp;=\u0026thinsp;0.73, Head-to-head encounter: K\u0026thinsp;=\u0026thinsp;0.779, Walking in the same direction: K\u0026thinsp;=\u0026thinsp;0.73, Narrow exit: K\u0026thinsp;=\u0026thinsp;0.70. The Kappa coefficients, which range from 0 to 1, suggest that higher values indicate greater consistency. The results show that the Pedestrian Walking Simulation Video Scale has high stability across measurements.\u003c/p\u003e\u003cp\u003eFurthermore, to better support the validity of the Pedestrian Walking Simulation Video Scale, we conducted a Kappa consistency check between the two parts of the Pedestrian Walking Style Scale to determine if both tools produced consistent results for the same group of participants. The Kappa coefficient was K\u0026thinsp;=\u0026thinsp;0.343, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, indicating a moderate level of agreement between the two methods in classifying personality traits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Descriptive Analysis of the Pedestrian Walking Style Questionnaire\u003c/h2\u003e\u003cp\u003eA descriptive statistical analysis of the Pedestrian Walking Simulation Video Scale revealed the following distribution of personality types among the participants: 81 (32.40%) were classified as steady, 52 (20.80%) as impulsive, 43 (17.20%) as shy, 36 (14.40%) as aggressive, and 38 (15.20%) as nervous. The detailed distribution of video selections across the four simulated scenarios is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eNumber of Participants Who Chose Each Video for Each Scenario in the Pedestrian Walking Simulation Video Scale\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evideo\u003c/p\u003e\u003cp\u003escene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorners\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHead-to-head encounter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWalking in the same direction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNarrow exits\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1(Steady type)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2(Impulsive type)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3(Shy type)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4(Aggressive type)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5(Nervous type)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e250\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\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the descriptive analysis of the Pedestrian Walking Style Scale showed that the majority of participants (40.40%) were identified as having a steady personality. The remaining distribution included impulsive (16.40%), shy (17.60%), aggressive (9.60%), and nervous (16.00%) personality types.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we used virtual simulation technology, particularly AnyLogic software, to construct various pedestrian simulation scenarios to measure individual personality traits. By conducting repeat measurements on 35 participants and comparing the results of the \"Pedestrian Walking Simulation Video Scale\" and \"Pedestrian Walking Style Scale\" for 250 participants, we tested the reliability and validity of the \"Pedestrian Walking Simulation Video Scale.\" The high test-retest reliability and moderate criterion-related validity provide strong preliminary support for the reliability of the video scale.\u003c/p\u003e\u003cp\u003eOn the basis of verifying reliability and validity, we discovered several interesting phenomena. Among the various pedestrian simulation scenarios, the \"same-direction walking\" scenario showed the highest correlation with the \"Pedestrian Walking Style Scale,\" while the \"narrow exit\" scenario showed the lowest correlation. The research found that, in the \"narrow exit\" scenario, the number of participants choosing the \"aggressive\" video increased significantly. This may be because narrow exits increase the likelihood of collisions, and previous research by Aghabayk has shown that collision experiences can increase the likelihood of pedestrians expressing anger(Aghabayk et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, in the narrow exit scenario, the number of participants choosing the \"shy\" video decreased, meaning fewer participants opted for walking alone or with a few others, while the number of participants choosing the \"nervous\" video increased, reflecting a preference for walking closely behind others. This could be because, in the narrow exit scenario, crowds congregate at the exit, diminishing individual walking preferences. In previous studies, personality has been shown to be stable, with personality traits generally not easily changing. However, these changes indicate that individual behavior can change with the environment(Martin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This may also explain the lower correlation between the \"Pedestrian Walking Simulation Video Scale\" and the \"Pedestrian Walking Style Scale.\"\u003c/p\u003e\u003cp\u003eDuring the development of the video scale, two key issues were content reliability and validity. Currently, studies on the reliability and validity of video scales are relatively scarce. Existing research on video scale validity focuses primarily on criterion-related validity, content validity, construct validity(Liegl et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Susanty et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rejeski et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Guerra et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and convergent validity(Peter et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marsh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), methods that are not significantly different from those used to validate traditional scales. However, reliability testing for video scales has faced challenges. Almost all studies only use test-retest reliability testing(Liegl et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Susanty et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rejeski et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peter et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Guerra et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Marsh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This raises the question: are traditional reliability testing methods unsuitable for video scales? The core issue lies in the fact that video scales mostly collect categorical data. For instance, participants may be asked to \"select the animation that best represents your activity limitation level(Liegl et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e),\" which directly categorizes participants without generating a specific score. Traditional reliability testing methods, such as split-half reliability and internal consistency reliability, require data to be at least interval-scaled and therefore do not apply to categorical data. Although some video scales can be treated as binary scales (e.g., choosing a specific video is akin to answering \"yes,\" while unchosen videos are akin to answering \"no\"), reliability tests for binary scales typically require the questions to be independent of each other. That is, the answer to one question should not affect the answers to others. In video scales, however, many video options are not independent. For example, in this study, participants could only choose one personality video in a given scenario (choosing a video is equivalent to answering \"yes\" for that personality, while not choosing other videos is equivalent to answering \"no\"). This characteristic limits the applicability of many traditional reliability and validity testing methods to video scales. Therefore, designing questions in ways that better suit different types of scales is crucial.\u003c/p\u003e\u003cp\u003eAs a novel measurement tool, the use of video scales in psychological measurement represents an important methodological innovation. Currently, video scales are primarily applied in fields such as traffic and elderly mobility. For example, in the field of traffic, virtual simulation technology has been used to simulate the evacuation behavior of crowds with different personality traits(Wang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); in elderly mobility, it has been used to assess physical activity capacity in older adults(Marsh et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These applications share the common characteristic of measuring behaviors that are easily observable, allowing objective data to be collected through direct observation or measurement. However, in psychological assessments, many internal traits (e.g., laziness, confidence) are difficult to assess through external behaviors alone. Researchers need to carefully design virtual scenarios that elicit behaviors related to these internal traits, avoiding reliance on subjective self-reporting. Developing appropriate video scales using virtual simulation technology to assess these less observable internal traits remains a pressing challenge.\u003c/p\u003e\u003cp\u003eAlthough this study yielded positive results, it also has some limitations. First, the sample size was relatively small, and the participants came from specific populations, which may limit the generalizability of the results. Second, while virtual simulation technology can simulate real-world scenarios, virtual environments still differ from real-life situations to some extent, potentially influencing participants' real reactions. Additionally, the pedestrian simulation scenarios designed in this study were relatively simple and did not fully account for more complex social interactions and contextual variables, which may have affected the comprehensive assessment of different personality traits.\u003c/p\u003e\u003cp\u003eFuture research should focus on simulating scenarios that more closely resemble real-life situations to enhance the applicability of video scales in real-world contexts. The widespread application of ChatGPT in education(An and Ma, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provides a solid foundation for introducing it into the field of psychological measurement. With ChatGPT's powerful language processing capabilities, it could be used to generate text-based examples according to specific assessment needs, which could then be turned into simulation videos through virtual simulation software. For instance, scenarios could be created to measure individual behavior in various social situations, such as interactions in public places or decision-making styles in work environments, which could be more realistically simulated through virtual technology. Moreover, future studies should explore how to develop lightweight, user-friendly video scales for common platforms such as mobile devices and social media, making them more accessible to a broader audience. By integrating measurement tools into daily life, researchers can more authentically capture individual behavioral characteristics, avoiding the subjectivity and boredom often associated with traditional self-report scales. This practical application can not only improve the accuracy of psychological assessments but also increase the ecological validity of the scales.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study developed the \"Pedestrian Walking Simulation Video Scale\" using virtual simulation technology and tested its validity and reliability. The results not only confirm the scientific and feasible use of virtual simulation technology as a tool for psychological measurement but also highlight its potential in broader applications. This study is not meant to replace traditional scales but to attempt the development of a more ecologically valid psychological measurement tool. The introduction of virtual simulation technology provides a more intuitive and dynamic new method for psychological research, helping to overcome the limitations of traditional questionnaire methods and promoting the further development of individual behavior assessments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of Beijing University of Technology (Certificate Number: BJUTCOM-202503-001). All participants provided informed consent prior to participation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by the Beijing Ideological and Political Work Research Project for Universities (Grant No. BJSZ2024ZL01) and the Beijing Social Science Foundation (Grant No. 24GJA001).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.A. was responsible for the study design. L.L. wrote the manuscript and conducted data analysis. J.P. assisted with simulation modeling. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe would like to express our sincere gratitude to all the participants who took part in this study. We also thank the research assistants who supported data collection and video production. This work was supported by the Beijing Ideological and Political Work Research Project for Universities (Strategic Project, Grant No. BJSZ2024ZL01), and the Beijing Social Science Foundation (General Project, Grant No. 24GJA001). The study forms part of the ongoing research project entitled \"Research on the Application and Countermeasures of Generative AI in Ideological and Political Education in Universities.\"\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghabayk K, Rejali S, Shiwakoti N. The Role of Big Five Personality Traits in Explaining Pedestrian Anger Expression. Sustainability. 2022;14(19):12099.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAn Z, Ma J. Research on the application of ChatGPT in education\u0026mdash;visual analysis based on CiteSpace. Educ Lifelong Dev Res. 2024;1(1):41\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai L, Liu X. Identifying Big Five personality traits based on facial behavior analysis. Front Public Health. 2022;10:1001828.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCred\u0026eacute; M, Harms P, Niehorster S, Gaye-Valentine A. An evaluation of the consequences of using short measures of the Big Five personality traits. J Personal Soc Psychol. 2012;102(4):874.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuerra RO, Oliveira BS, Alvarado BE, Curcio CL, Rejeski WJ, Marsh AP, Zunzunegui MV. Validity and applicability of a video-based animated tool to assess mobility in elderly L atin A merican populations. Geriatr Gerontol Int. 2014;14(4):864\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuy SJ, Kim S, Lin MC, Manocha D. (2011, August). Simulating heterogeneous crowd behaviors using personality trait theory. 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Evidence of big five and aggressive personalities in gait biomechanics. J Nonverbal Behav. 2017;41:35\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun HK, Chen CK. Model considering panic emotion and personality traits for crowd evacuation. Chin Phys B. 2023;32(5):050401.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSusanty S, Sarasmita MA, Sudarma IW, Azissah D, Suyanto J, Kamil SUR, Suharjiman S. Animated video development COVID-19 prevention and management for anxiety among older adults in Indonesia. Geriatr Nurs. 2023;49:13\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang H, Xu T, Li F. A novel emergency evacuation model of subway station passengers considering personality traits. Sustainability. 2021;13(18):10463.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWillis C, Powell-Rudy T, Colley K, Prasad J. Examining the use of game-based assessments for hiring autistic job seekers. J Intell. 2021;9(4):53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoods SA, Ahmed S, Nikolaou I, Costa AC, Anderson NR. Personnel selection in the digital age: A review of validity and applicant reactions, and future research challenges. Eur J work organizational Psychol. 2020;29(1):64\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng T, Qu W, Ge Y, Sun X, Zhang K. (2017). The joint effect of personality traits and perceived stress on pedestrian behavior in a Chinese sample. PLoS ONE, 12(11), e0188153.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"virtual simulation technology, AnyLogic, psychological measurement, personality assessment","lastPublishedDoi":"10.21203/rs.3.rs-7109404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7109404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePedestrians' personality traits partially determine their walking styles. A deeper understanding of pedestrian walking styles holds significant implications for research in the field of traffic safety. However, current studies lack measurement tools with high ecological validity for assessing pedestrians' personality traits. Virtual simulation technology, as an innovative approach capable of realistically simulating real-world scenarios, offers novel methods and tools in the field of psychological measurement. This study utilized AnyLogic virtual simulation software to develop the \"Pedestrian Walking Simulation Video Scale,\" consisting of 20 simulated pedestrian walking videos, and conducted experimental validation with 250 participants and 35 retest participants. Results indicated that the video scale demonstrated high test-retest reliability (Kappa values ranging from 0.70 to 0.78) and moderate criterion-related validity (Spearman correlation coefficient\u0026thinsp;=\u0026thinsp;0.378). Future studies should further optimize the design of video scales to enhance ecological validity, thereby promoting broader applications of virtual simulation technology in psychological measurement and personality assessment research.\u003c/p\u003e","manuscriptTitle":"Application of Virtual Simulation Technology in the Measurement of Pedestrian Personality Traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 12:17:33","doi":"10.21203/rs.3.rs-7109404/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":"f03764ec-6323-4d6a-a908-91eb6d9cfb5c","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-04T05:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 12:17:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7109404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7109404","identity":"rs-7109404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}