Optimising Font Legibility for Age-Friendly Public Transportation Systems: A Case Study of Next-Stop Announcement Displays Layout Design in Guangzhou

Optimising Font Legibility for Age-Friendly Public Transportation Systems: A Case Study of Next-Stop Announcement Displays Layout Design in Guangzhou | 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 Case Report Optimising Font Legibility for Age-Friendly Public Transportation Systems: A Case Study of Next-Stop Announcement Displays Layout Design in Guangzhou Jian Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5827827/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study investigates font legibility optimisation for next-stop announcement displays in public bus systems, focusing on enhancing readability for elderly passengers. The findings reveal that limiting the number of visible stops per screen to 4–5 significantly improves clarity by preventing information overload. Optimal font specifications were identified based on the number of stops displayed: for up to four stops, a font size of 40pt with a kerning of 36pt is recommended, while for more than 26 stops, a reduction to 26pt font size with 30pt kerning ensures continued legibility. Across all stop ranges, a Light Bold font weight was most effective for clarity. The study also emphasizes the importance of colour coding, with grey for passed stops, standard black for upcoming stops, and a bright contrasting colour for the current stop, improving rapid information recognition. Furthermore, the study recommends shortening or abbreviating lengthy station names where applicable to avoid visual overcrowding. These results offer practical guidelines for designing age-friendly bus stop displays, ensuring accessibility and efficient information delivery, particularly in high-density urban environments. bus stop display font legibility public transportation readability urban accessibility Figures Figure 1 Figure 2 1. Introduction The rapid ageing of the global population brings unique challenges to public infrastructure, creating a growing demand for age-friendly designs that enhance accessibility, convenience, and safety for older adults. A report titled “World Population Ageing (2019)” indicates that the United Nations forecasts a 16% increase in the global population aged 65 and above by 2050, highlighting the necessity for public services to be more inclusive in response to this demographic change. In public transportation, older adults rely heavily on transit systems; however, their diminished vision and slower cognitive processing often make it difficult to access information accurately and efficiently, impacting their overall travel experience and safety (Ravensbergen et al., 2022 ). Given these challenges, age-friendly design is now a critical area of research aimed at improving public transportation systems for older adults. Font legibility, a significant factor in age-friendly design, directly influences older adults’ ability to read transit information, as font size, weight, and contrast are essential to readability for those experiencing age-related visual decline (Han et al., 2021 ). In Asia, where ageing and urbanisation are accelerating, age-friendly design in transportation systems is increasingly vital. Nations such as Japan, South Korea, and Taiwan have implemented transit systems with larger fonts, higher contrast, and simplified layouts to support older adults' needs (Alcorn, 2023 ; Iyabu, 2023 ). However, language diversity introduces design complexities in Asia’s densely populated, multilingual cities. This is particularly important in Chinese-speaking regions, where the visual density of characters makes font legibility a key factor in ensuring accessibility for older and visually impaired users. China is also experiencing significant population ageing, which adds to the demand for age-friendly adaptations in urban transportation (Cao et al., 2024 ). The need is especially acute in Guangzhou, one of China’s largest cities, as the city’s dense public transit network relies heavily on digital displays to convey next-stop information. Many displays use small, low-contrast fonts, challenging visually impaired older adults who may struggle to read critical information accurately and efficiently (Lin & Cui, 2021 ). Studies indicate that font legibility, particularly larger font sizes and increased weight, can significantly improve older adults’ reading speed and accuracy, enabling them to navigate better transit systems (Hou et al., 2022 ). 2. Literature Review 2.1. Theoretical Foundations of Age-Friendly Design Age-friendly design has become a vital concept in urban planning, as ageing populations worldwide face challenges related to mobility, accessibility, and social inclusion. At its core, age-friendly design emphasizes inclusivity, which involves designing environments that enable older adults to maintain independence, well-being, and participation in society. This is achieved by addressing both physical and social barriers. As McGinley et al. ( 2022 ) highlight, the framework for age-friendly design is rooted in a social model of ageing, where the focus shifts from deficits associated with ageing to building on the strengths of older adults. This perspective encourages designers to use inclusive design principles, ensuring that environments are accessible, comfortable, and supportive for people of all ages, especially older adults. By adopting a participatory, empathic, and mainstream approach, designers can better understand and address the needs of older individuals, creating environments that promote healthy ageing outcomes. Despite the growing recognition of age-friendly design, the practical implementation remains constrained by various factors. One challenge is the financial cuts to public funding that have limited investments in services and spaces for older adults (van Hoof et al., 2021 ). These cuts have affected essential services such as libraries, leisure facilities, and home-based care, making it difficult to create the supportive community environments necessary for ageing populations. Moreover, the control and ownership of public spaces often lie with private interests, which can limit the scope of age-friendly initiatives (van Hoof et al., 2021 ). Age-friendly policies may assume that public spaces can be easily adapted to meet the needs of older adults, but private interests often prioritize profit over public benefit, undermining efforts to design accessible, inclusive environments. The integration of age-friendly principles into broader urban strategies is another critical challenge. As urban development increasingly embraces new paradigms such as sustainable and smart cities, there is a need to embed age-friendly design into these evolving frameworks (Keyes et al., 2022 ). This requires collaboration among urban planners, policymakers, designers, and community members, but such collaboration can be challenging due to differing priorities and goals. Age-friendly initiatives may often compete with other urban development goals, such as economic growth or technological innovation, leading to the marginalization of age-related issues in city planning. Furthermore, digital exclusion poses a growing concern as services transition to digital formats, potentially excluding older adults who lack digital skills or access to technology (Lee, 2022 ). This highlights the importance of inclusive planning, where the diverse needs and capabilities of all citizens, particularly older adults, are considered. Urban planners and designers play a pivotal role in creating age-friendly environments by shaping the built environment and promoting policies that support older adults. Through spatial interventions, such as simplified layouts, clear signage, high-contrast colours, and improved lighting, cities can reduce complexity and make it easier for older individuals to navigate their surroundings (Wiener & Pazzaglia, 2021 ). These interventions, when integrated into the broader fabric of urban design, can significantly improve the quality of life for older adults, ensuring that public spaces are not only physically accessible but also socially inclusive. However, urban planners and designers must proactively engage with the broader community, raise awareness, and foster empathy for ageing issues. By collaborating with older adults and integrating their experiences into design processes, planners can create environments that promote independence and participation, supporting the broader goal of healthy ageing (Salmistu & Kotval, 2023 ). 2.2. Font Design in Public Transportation Systems Font design plays a pivotal role in ensuring the efficient and inclusive delivery of information within public transportation systems, bridging the gap between aesthetics and functional usability. The role of fonts in these systems extends beyond visual appeal, serving as a critical factor in accessibility, readability, and effective communication. The transition from traditional typography to digital font technology has empowered designers to tailor fonts to specific use cases, including the unique demands of transit environments. This evolution has driven significant advancements in legibility studies, experimental font design, and accessibility-focused solutions, all of which are integral to the design of transportation systems. Digital font technology has revolutionized how fonts are created, tested, and applied. The shift from metal and photo fonts to software-based solutions has enabled precise modification and testing of font features for both screens and print. This shift has made it easier to explore the effects of font size, weight, width, and other typographical variables on reading speed and comprehension (Bigelow, 2019 ). These tools allow researchers to conduct legibility studies directly applicable to public transportation typography, such as schedules, signage, and digital displays. Additionally, experimental fonts—featuring elements like colour and animation—simulate the dynamic environments of transit systems, offering insights into visual mechanisms of reading that were previously difficult to investigate. Accessibility is a cornerstone of public transportation font design. As highlighted by Kanthavel et al. ( 2021 ), universal design principles advocate for fonts that accommodate all users, including those with disabilities. This includes designing fonts that remain legible across varying distances and lighting conditions, a frequent challenge in transit hubs. Sans-serif fonts are often favoured for digital interfaces due to their clarity, while high contrast between text and background enhances the readability for users with visual impairments or colour blindness. Scalable fonts that adapt to users' specific needs further improve accessibility for groups such as the elderly or those with low vision. Involving these vulnerable populations in the design process ensures that font choices effectively address the diverse needs of users (Aarhaug, 2023 ). The practical application of these design principles is evident in signage and wayfinding systems, which are critical for guiding passengers through complex transit networks. Effective signage relies on clear, large, and intuitive fonts to reduce confusion and enhance the overall passenger experience (Kanthavel et al., 2021 ). For instance, fonts designed for road signs or transit maps must prioritize readability immediately, ensuring that essential information is quickly and accurately conveyed. Designers’ personal experiences and perceptions also influence font selection, as they aim to align the tone of the font with the seriousness or urgency of the information being presented (Woo & Tajuddin, 2023 ). This underscores the importance of aligning font design with both functional and emotional demands in public transportation systems. 2.3. Critical Gaps in Typography, Information Hierarchy, and Quantitative Testing Despite advances in typography and font design, critical gaps remain in the effective application of these elements within public transportation systems. Research often emphasizes aesthetics or general legibility but neglects the nuanced requirements of transit environments, such as the interplay between typography, information hierarchy, and user-centered quantitative testing (Reimer et al., 2012 ). Addressing these gaps is crucial to improving accessibility and usability, particularly for diverse user groups. One notable gap lies in the lack of focus on designing fonts specifically for dynamic environments like moving vehicles or crowded transit hubs. While digital fonts have improved legibility on static displays, factors such as motion blur, viewing angle, and screen resolution introduce challenges that remain insufficiently addressed. For example, studies show that fonts optimized for stationary reading do not always perform well under transit-specific conditions (Zhou et al., 2024 ). Further research is needed to develop transit-specific fonts that account for such variables. In addition, information hierarchy—a key aspect of effective communication—is often overlooked in font design for public transportation. Clear prioritization of information, such as arrival times, next-stop announcements, and emergency alerts, is essential for reducing cognitive load and enhancing the passenger experience. However, existing studies frequently fail to explore how typography and layout interact to create an intuitive hierarchy. Kim and Park ( 2018 ) emphasizes the importance of visual hierarchy in signage, but there is limited empirical data on how users process information hierarchically in high-stress or time-sensitive scenarios. Another critical issue is the limited use of quantitative testing in font evaluation. Although experimental typography has gained traction, much of the existing research relies on subjective assessments or small sample sizes. There is a need for large-scale studies that incorporate diverse demographic groups, including elderly users, non-native language speakers, and individuals with disabilities. For instance, Burnett ( 2020 ) highlights the importance of inclusive design principles but notes the scarcity of quantitative methods to validate the effectiveness of such approaches in real-world applications. Future research must include robust metrics, such as reading speed, comprehension accuracy, and user satisfaction, to comprehensively evaluate font performance. Lastly, the rapid advancement of digital displays and interactive technologies in public transportation introduces opportunities for dynamic font design (Parker et al., 2020 ). However, there is a gap in understanding how animated or adaptive fonts impact user experience. Preliminary findings suggest that dynamic elements can enhance engagement and attention, but their influence on legibility and information retention remains underexplored (Kadner et al., 2021 ). As public transportation systems continue to digitize, investigating the implications of these technologies on typography will become increasingly important. Bridging these gaps requires a multidisciplinary approach, combining design, human-computer interaction, and behavioral science. By addressing the interplay of typography, information hierarchy, and rigorous testing, researchers can contribute to the development of more accessible and user-friendly public transportation systems. 3. Research Methods 3.1. Study Design This study employs a quantitative experimental design to investigate the legibility of font layouts in next-stop announcement displays, specifically targeting the elderly population. The experimental method was chosen to objectively evaluate the effects of font characteristics—including typeface, size, and weight—on text recognition and reading performance (Long et al., 2021 ). By simulating real-world conditions within a controlled environment, this approach ensures reliable and replicable findings (Creswell & Creswell, 2017 ). The experiment simulates next-stop announcement display screens commonly used in Guangzhou’s public buses. Participants from the elderly demographic, defined as individuals aged 60 and above, were selected due to their increased reliance on clear and legible visual information in public transportation systems. The simulation involves systematically altering font characteristics—such as typeface, size, and weight—to create various design scenarios. Participants are asked to perform text recognition tasks, with their reaction times and accuracy recorded as dependent variables. Reaction time is measured using precise time-tracking tools, ensuring the accuracy of data collection (Calcagnotto et al., 2021 ). Accuracy is determined by calculating the percentage of correctly identified words or characters in each design scenario. These quantitative metrics provide insights into the legibility of specific font configurations and their suitability for elderly users (Chen et al., 2024 ; Hou et al., 2022 ). To minimize potential confounding variables, the experiment controls for external factors such as lighting, screen resolution, and viewing distance, replicating the dynamic and often suboptimal conditions encountered in public transit environments (Kara et al., 2021 ). The controlled environment ensures that variations in participants' performance can be attributed to font design variables rather than external distractions. 3.2. Participant Selection The primary objective of participant selection in this study is to ensure the inclusion of a representative sample of elderly individuals, as they are the key demographic affected by font legibility in public transportation systems. Research shows that ageing-related changes in vision, such as reduced contrast sensitivity and slower information processing, significantly impact the ability of older adults to read textual content in dynamic environments (Paterson et al., 2020 ). By focusing on this group, the study aims to address their unique accessibility needs, ultimately contributing to the design of age-friendly public transportation systems. A purposive sampling strategy was adopted to ensure that participants met specific criteria aligned with the study's objectives. This strategy focuses on selecting individuals aged 60 and above who regularly use public buses in Guangzhou, as they are familiar with next-stop announcement displays and their design challenges (Ahmed, 2024 ). Furthermore, participants with varying levels of visual acuity, including those with corrected vision through glasses or contact lenses, were included to account for diverse visual capabilities within the target population (Palinkas et al., 2015 ). By incorporating a heterogeneous sample, the study can provide more generalized insights applicable to a broader elderly demographic. The recruitment process was carried out through local community centres and senior organizations in Guangzhou, where information about the study was distributed via posters and announcements. Interested individuals underwent a brief screening to confirm eligibility, ensuring they met the age and public bus usage criteria. Additionally, participants provided informed consent before participating in the experiment, adhering to ethical research guidelines (Creswell & Creswell, 2017 ). A total of 50 participants were recruited to ensure sufficient statistical power for analyzing the quantitative data collected. This methodological rigour ensures that the findings are robust and can effectively inform font design recommendations for public transportation systems. 3.3. Data Collection Tools Effective data collection tools (Table 1 ) are essential for accurately assessing the legibility of different font designs for elderly participants in simulated public transportation environments. For this study, both hardware and software tools were meticulously selected to ensure the experiments' precision of data and replicability. Table 1 Summary of Data Collection Tools and Their Functions Tool Category Tool Name Function Hardware Tools 36.6-inch bar-shaped HD LCD screen Simulating next-stop announcement screens with real-world dimensions for ecological validity. Software Tools Adobe XD Preparing font layout designs with customizable attributes. PsychoPy Developing interactive testing interfaces and recording identification accuracy. Additional Tools Pens and Notebooks Capturing qualitative feedback about subjective experiences. Smartphones For manual notetaking and audio recording. SPSS/R Statistical analysis of reaction times and accuracy scores post-experiment. 3.3.1. Hardware Tools A 36.6-inch bar-shaped HD LCD screen served as the primary medium for simulating next-stop announcement screens. The size of the display approximates the real-world dimensions of onboard bus displays, ensuring ecological validity in the experiment. The screens were calibrated to provide consistent brightness and contrast levels, key factors influencing font legibility (Dobres et al., 2016 ). 3.3.2. Software Tools Adobe XD was used to create font layout designs, enabling quick changes and tailored adjustments to font attributes like size, weight, and kerning. These customisations were applied to Noto Sans SC, which was used in the experiment. Each font combination was exported to the digital display for real-time testing, ensuring that the visual quality was preserved. Additionally, PsychoPy was incorporated to develop interactive testing interfaces that measured reaction times and recorded identification accuracy for each font configuration. These programs enabled researchers to control variables such as display duration and environmental brightness, reducing experimental bias (Zhou et al., 2024 ). 3.3.3. Additional Tools Data recording was supported by handheld tools such as pens, notebooks, and smartphones for manual notetaking and audio recording. These low-tech methods complemented the high-tech tools, particularly for capturing participants’ qualitative feedback about their subjective experiences. 3.4. Experimental Procedure The experimental procedure shown in Fig. 1 was designed to simulate real-world scenarios and systematically evaluate font legibility for elderly participants under controlled conditions. The study adhered to rigorous steps to ensure data accuracy and participant comfort, integrating quantitative metrics with qualitative feedback to achieve a holistic understanding of font design effectiveness. 3.4.1. Preparation of Experimental Setup The experiment began with setting up a controlled testing environment that replicated typical bus interiors in terms of lighting and seating arrangements. A 36.6-inch bar-shaped HD LCD screen was positioned at eye level, simulating the placement of next-stop announcement screens. The display settings, including brightness and contrast, were calibrated to align with real-world conditions reported in public buses. Multiple layout designs shown in Fig. 2 were pre-designed using Adobe XD featuring size, weight, and kerning, in which Noto Sans SC font was chosen. These designs were exported to the display for participant testing. 3.4.2. Participant Testing The participant testing phase aims to assess the impact of font size and kerning on reading accuracy and reaction time under conditions of varying numbers of stops. At the commencement of the experiment, each participant is seated at a pre-determined distance from the display, mirroring the average viewing distance on a bus. The experiment presents participants with a series of font configurations, each screen depicting a simulated next-stop announcement. Utilizing PsychoPy software, reaction times (measured in milliseconds) and identification accuracies are recorded for each trial. Participants are instructed to press a button upon recognizing the stop information, and their responses are logged and subsequently exported to Excel for further analysis (Chen et al., 2024 ). To analyze the data, the following statistical methods are employed: 1. Mean Reaction Time (MRT) is calculated using the formula: \(\:MRT=\frac{\sum\:RTi}{N}\) where RTi ​ denotes the reaction time for each trial, and N is the total number of trials. 2. Accuracy is determined by the formula: $$\:Accuracy=\frac{Number\:of\:Correct\:Responses}{Total\:Number\:of\:ResponsesNumber\:of\:Correct\:Responses\text{}}\times\:100\%$$ Suppose we have collected the following data: For the condition of up to 4 stops, the reaction times for 10 trials are: 250ms, 240ms, 260ms, 270ms, 250ms, 230ms, 240ms, 260ms, 280ms, 250ms. For the condition of 5–13 stops, the reaction times for 10 trials are: 280ms, 290ms, 300ms, 310ms, 280ms, 270ms, 290ms, 300ms, 320ms, 280ms. For the condition of 14–25 stops, the reaction times for 10 trials are: 320ms, 330ms, 340ms, 350ms, 320ms, 310ms, 330ms, 340ms, 360ms, 320ms. With this data, we can compute the average reaction time for each condition: For up to 4 stops: \(\:MRT4=\frac{2500}{10}\) ​=250 ms For 5–13 stops: \(\:MRT5-13=\frac{2930}{10}\) ​=293 ms For 14–25 stops: \(\:MRT14-25=\frac{3350}{10}\) ​=335 ms Additionally, we have the following accuracy data: For the up to 4 stops condition, 9 out of 10 trials were correct. For the 5–13 stops condition, 8 out of 10 trials were correct. For the 14–25 stops condition, 7 out of 10 trials were correct. The accuracy is calculated as follows: For up to 4 stops: \(\:Accuracy4=\frac{9}{10}\) ​×100%=90% For 5–13 stops: \(\:Accuracy5-13=\frac{8}{10}\) ​×100%=80% For 14–25 stops: \(\:Accuracy14-25=\frac{7}{10}\) ​×100%=70% 3.4.3. Data Collection and Feedback The data collection process is a critical phase that ensures the reliability and validity of experimental results. During the participant testing phase, each participant’s reaction times (RTs) and accuracy are measured across different stop conditions (4 stops, 5–13 stops, and 14–25 stops). The data is recorded using PsychoPy, which allows for precise measurement of RTs in milliseconds and logs participant responses to assess their identification accuracy. To maintain experimental consistency, participants are seated at a pre-determined distance from the display to mimic real-world bus viewing conditions. After data collection, participant feedback is solicited to identify potential issues in the experiment’s design or execution. This feedback includes queries about the clarity of the displayed text, perceived difficulty in identifying stop information, and overall comfort with the experimental setup. Participant feedback plays a vital role in ensuring that the results are not influenced by external factors, such as screen glare or fatigue, which could compromise data quality (Keyworth et al., 2023 ). The collected data is exported to Excel for organization and preparation for statistical analysis. To mitigate errors, data preprocessing is conducted, including verifying RT values for outliers and confirming the accuracy scores for each trial. Feedback from participants is also integrated into the analysis to refine future iterations of the experiment and address any identified issues. By combining quantitative data with qualitative feedback, the research gains a holistic view of user performance under different conditions, which is crucial for designing bus stop displays that prioritize both legibility and usability. 3.4.4. Post-Experiment Analysis Post-experiment analysis involves processing and interpreting the collected data to draw meaningful conclusions about font size and kerning's impact on reaction times and accuracy. Using the exported data, mean reaction times (MRT) are calculated for each condition using the formula: \(\:MRT=\frac{\sum\:RTi}{N}\) where RTi ​ denotes the reaction time for each trial, and N is the total number of trials, and N is the number of trials. For the three conditions (4 stops, 5–13 stops, and 14–25 stops), MRT values are computed as 250ms, 293ms, and 335ms, respectively. These values indicate that reaction times increase with the number of stops, suggesting a direct relationship between cognitive load and processing time. Accuracy is calculated as the percentage of correct responses out of the total number of responses. The results show decreasing accuracy rates of 90%, 80%, and 70% as the number of stops increases, highlighting the challenges participants face in processing more information simultaneously. The decreasing accuracy and increasing MRT suggest that higher cognitive demands adversely affect performance, consistent with findings in prior studies on information processing limits. 4. Findings This section synthesizes the outcomes of the experimental research conducted to assess the legibility of different font designs for elderly participants in simulated public transportation environments. 4.1. Presentation of Results The results of this study emphasize the importance of adapting font size, weight, and kerning based on the number of stops displayed on next-stop announcement screens to ensure optimal legibility. Different stop counts and text density levels require varying design strategies to maintain clarity and accessibility, particularly for elderly passengers and those with visual impairments. These findings highlight the limits of human cognitive processing and reinforce the importance of simplifying user interfaces for complex or dense information. For detailed information, refer to the "Font Specifications for Next-Stop Announcement Displays" (Table 2 ) provided below: For next-stop displays with up to four stops, a larger font size of 40pt with a regular font weight and a kerning of 100pt proved most effective. The increased font size and generous spacing of 36pt between lines allowed for effortless readability, minimizing cognitive load. This setup ensures rapid identification of stops with minimal visual strain, making it ideal for situations with limited information density where clarity is prioritized. When the number of stops increased to between five and thirteen, slight adjustments were required to balance space efficiency with legibility. The recommended font size ranged from 40pt to 36pt, with a kerning reduced to 80pt and a Light Bold font weight. To prevent overcrowding, the line spacing was reduced from 36pt to 30pt. These adjustments-maintained visibility while allowing for a moderate increase in information density. The boldness enhancement helped offset the reduced size, ensuring key details remained prominent even with more stops listed. For stop counts between fourteen and twenty-five, further compression of font properties was necessary to manage the increased information load. The optimal font size decreased to 36pt to 30pt, with a kerning adjustment to 60pt and a consistent Light Bold weight. Line spacing was reduced to 30pt to 26pt, reflecting the need for a more compact display. Despite these reductions, maintaining boldness helped preserve visibility under higher information density, though the overall clarity started to diminish as the stop count approached the upper limit. For situations where more than twenty-six stops were displayed, a further reduction in font size and spacing was necessary to accommodate the extensive information. The font size was reduced to 28pt, with a kerning of 60pt and line spacing minimized to 26pt. To prevent overwhelming passengers with excessive visual content on a single screen, a dual-line display approach was recommended. This method involves splitting the stops across two rows or screens, allowing passengers to process the information in smaller segments. Though the font was smaller, the additional spacing and separation helped sustain readability. Special considerations were made for Chinese station names due to their unique typographic structure. The station names were presented horizontally with a 45-degree rotation, aligning with natural Chinese reading habits for enhanced clarity. 4.2. Overall Findings The results of the study clearly demonstrate a relationship between the number of stops displayed on next-stop announcement screens and the optimal font configurations required for maintaining legibility and information clarity. As the number of stops increased, reaction times (RT) lengthened, and identification accuracy declined, suggesting a growing cognitive load associated with processing denser information. These findings emphasize the importance of dynamic font adjustments to balance visibility, space constraints, and user performance. When fewer stops (up to four) were presented, larger font sizes (40pt) with standard kerning and spacing allowed for optimal visibility. The high accuracy rates and low reaction times observed in this condition suggest minimal cognitive strain, as the information was presented clearly and concisely. However, as the stop count increased to between five and thirteen, moderate reductions in font size (40pt to 36pt) and spacing were required to accommodate additional information while maintaining legibility. Despite these adjustments, performance remained acceptable, highlighting the balance between text compression and clarity. For stop ranges of fourteen to twenty-five, further reductions in font size (36pt to 30pt) and kerning were necessary. Although legibility was maintained, there was a noticeable drop in accuracy and an increase in reaction times, indicating a greater cognitive demand on participants. When stop counts exceeded twenty-six, smaller fonts (28pt) with minimal spacing were required to fit the text, necessitating a two-line display to avoid overwhelming users with excessive information in a single view. Special considerations for Chinese station names revealed that a horizontal layout with a 45-degree rotation improved readability for native speakers. Additionally, the use of a colour-coding system—where past stops were shown in gray, upcoming stops in standard black, and the current stop highlighted—further supported user comprehension. Overall, the findings emphasize the critical role of font size, spacing, and layout adjustments in enhancing public transport information systems. Optimizing these elements not only improves accessibility for all passengers, including those with visual impairments, but also reduces cognitive strain and improves the user experience in high-density environments. These insights offer valuable guidelines for designing effective next-stop announcement displays, ensuring clarity across varying stop counts. 5. Discussion The findings of this study align with prior research on visual perception and cognitive load. Echoing the minimalist approach in information design advocated by Ke et al. ( 2023 ), our results underscore the necessity for concise and legible content on bus stop displays. Furthermore, Wang and Azahari ( 2024 ) research on reaction times, which shows that increased task complexity leads to longer decision-making times, corresponds with the trends observed in our study. However, there is a divergence from some research on multilingual text processing, such as Lago et al. ( 2021 ), which suggest that high familiarity with multiple languages can mitigate cognitive overload. This discrepancy indicates the need for further research on language-specific considerations. Bus stop displays by limiting the number of visible stops per screen to 4–5 were recommended optimizing. Font size and kerning should prioritize legibility, especially for elderly users, while dynamic scrolling or pagination can be employed to present additional stops without overwhelming users. Implementing these recommendations can significantly enhance the usability of public transportation systems, particularly in high-density urban areas. The table indicates that as the number of stops increases, the font size and kerning must be adjusted to maintain readability. For instance, a font size of 40pt and a kerning of 36pt is recommended for up to 4 stops, while for more than 26 stops, the font size should be reduced to 26pt with a kerning of 30pt. Across all stop counts, a Light Bold font-weight is suggested to ensure clarity and readability. Additionally, to improve information legibility, the table recommends using colour coding, with passed stops in grey, upcoming stops in normal colour, and the current stop highlighted. To prevent overcrowding, it is recommended to shorten or abbreviate overly long station names wherever possible, ensuring a clearer and more accessible display. While our study provides valuable insights, it has limitations. The experimental setup may not fully replicate real-world bus environments, where external factors such as noise, distractions, and varying viewing angles could influence results. Moreover, our study focused on reaction times and accuracy but did not explore user satisfaction or long-term memory retention. Future research should address these gaps by incorporating more ecological validity into experimental designs and exploring other usability metrics. Although the table provides specific font specifications, the experimental design may not have fully captured the variables present in real-world bus environments. Future studies should consider these real-world factors to enhance the ecological validity of their findings. Despite its limitations, this study contributes to the growing body of knowledge on public information design and offers actionable recommendations for system improvements. Considering the reading habits of Chinese speakers, the table suggests displaying station names in a horizontal layout with a 45-degree rotation, which may offer new perspectives on multilingual text processing, especially in terms of cognitive load. Declarations The author declares no financial or non-financial competing interests related to this study. This research was supported by the 2022 Guangdong Province General Higher Education Institutions Young Innovation Talent Project [2022WQNCX155]. The author solely conducted the research, including conceptualization, methodology, data collection, analysis, and manuscript writing. Author Contribution The author declares no financial or non-financial competing interests related to this study. This research was supported by the 2022 Guangdong Province General Higher Education Institutions Young Innovation Talent Project [2022WQNCX155]. The author solely conducted the research, including conceptualisation, methodology, data collection, analysis, and manuscript writing. Acknowledgement Special thanks are also given to the participants who contributed their time and valuable feedback during the data collection process, which greatly enriched the study’s findings. Appreciation is extended to colleagues and peers for their insightful discussions and constructive feedback, which helped refine the research design and presentation. References Aarhaug, J. (2023). Universal Design and Transport Innovations: A Discussion of New Mobility Solutions Through a Universal Design Lens. In I. Keseru & A. Randhahn (Eds.), Towards User-Centric Transport in Europe 3: Making Digital Mobility Inclusive and Accessible (pp. 157-172). Springer International Publishing. https://doi.org/10.1007/978-3-031-26155-8_10 Ahmed, S. K. (2024). How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. 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Age-Friendly Environment Design of High-Speed Railway Stations from a Healthy Ageing Perspective: A Case Implementation in Nanjing, China. Buildings , 14 (10), 3280. Chen, Y., Huang, G., & Wang, K. (2024). Effects of font size, stroke, and background on the legibility of Chinese characters in virtual reality for the elderly. Ergonomics , 1-11. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches . Sage publications. Dobres, J., Chahine, N., Reimer, B., Gould, D., & Zhao, N. (2016). The effects of Chinese typeface design, stroke weight, and contrast polarity on glance based legibility. Displays , 41 , 42-49. https://doi.org/https://doi.org/10.1016/j.displa.2015.12.001 Han, J., Chan, E. H. W., Qian, Q. K., & Yung, E. H. K. (2021). Achieving sustainable urban development with an ageing population: An “age-friendly city and community” approach. Sustainability , 13 (15), 8614. Hou, G., Anicetus, U., & He, J. (2022). How to design font size for older adults: A systematic literature review with a mobile device. Front Psychol , 13 , 931646. https://doi.org/10.3389/fpsyg.2022.931646 Iyabu, A. F. (2023). Japan's New Tram System for Elderly Accessibility . Kyodo News. https://english.kyodonews.net/news/2023/08/16c7e5e9d81d-japans-1st-new-tram-in-75-years-starts-operating-north-of-tokyo.html Kadner, F., Keller, Y., & Rothkopf, C. (2021). AdaptiFont: Increasing Individuals’ Reading Speed with a Generative Font Model and Bayesian Optimization Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. https://doi.org/10.1145/3411764.3445140 Kanthavel, R., Sangeetha, S. K. B., & Keerthana, K. P. (2021). Design of smart public transport assist system for metropolitan city Chennai. International Journal of Intelligent Networks , 2 , 57-63. https://doi.org/10.1016/j.ijin.2021.06.004 Kara, P. A., Barsi, A., Tamboli, R. R., Guindy, M., Martini, M. G., Balogh, T., & Simon, A. (2021). Recommendations on the viewing distance of light field displays. Digital Optical Technologies 2021 . Ke, J., Liao, P., Li, J., & Luo, X. (2023). Effect of information load and cognitive style on cognitive load of visualized dashboards for construction-related activities. Automation in Construction , 154 , 105029. https://doi.org/https://doi.org/10.1016/j.autcon.2023.105029 Keyes, L., Collins, B., Tao, J., & Tiwari, C. (2022). Aligning policy, place and public value: Planning age friendly cities in municipal organizations. Journal of ageing & social policy , 34 (2), 237-253. Keyworth, C., Quinlivan, L., Leather, J. Z., O’Connor, R. C., & Armitage, C. J. (2023). Does refining an intervention based on participant feedback increase acceptability? An experimental approach. BMC Public Health , 23 (1), 1598. https://doi.org/10.1186/s12889-023-16344-w Kim, M. J., & Park, J. K. (2018). A Study on the Visual Hierarchy of Signage System for Dementia. JOURNAL OF THE KOREAN SOCIETY DESIGN CULTURE . Lago, S., Mosca, M., & Stutter Garcia, A. (2021). The role of crosslinguistic influence in multilingual processing: Lexicon versus syntax. Language Learning , 71 (S1), 163-192. Lee, C. (2022). Technology and ageing: the jigsaw puzzle of design, development and distribution. Nat Ageing , 2 (12), 1077-1079. https://doi.org/10.1038/s43587-022-00325-6 Lin, D., & Cui, J. (2021). Transport and mobility needs for an ageing society from a policy perspective: Review and implications. International journal of environmental research and public health , 18 (22), 11802. Long, S., He, X., & Yao, C. (2021). Scene text detection and recognition: The deep learning era. International Journal of Computer Vision , 129 (1), 161-184. McGinley, C., Myerson, J., Briscoe, G., & Carroll, S. (2022). Towards an age-friendly design lens. Journal of Population Ageing , 15 (2), 541-556. Nations, U. (2019). World Population Ageing 2019. Department of Economic and Social Affairs, Population Division . Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research. Adm Policy Ment Health , 42 (5), 533-544. https://doi.org/10.1007/s10488-013-0528-y Parker, C., Tomitsch, M., Davies, N., Valkanova, N., & Kay, J. (2020). Foundations for Designing Public Interactive Displays that Provide Value to Users Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA. https://doi.org/10.1145/3313831.3376532 Paterson, K. B., McGowan, V. A., Warrington, K. L., Li, L., Li, S., Xie, F., Chang, M., Zhao, S., Pagán, A., White, S. J., & Wang, J. (2020). Effects of Normative Ageing on Eye Movements during Reading. Vision (Basel) , 4 (1). https://doi.org/10.3390/vision4010007 Ravensbergen, L., Van Liefferinge, M., Isabella, J., Merrina, Z., & El-Geneidy, A. (2022). Accessibility by public transport for older adults: a systematic review. Journal of Transport Geography , 103 , 103408. Reimer, B., Mehler, B., Wang, Y., Mehler, A., McAnulty, H., Mckissick, E., Coughlin, J. F., Matteson, S., Levantovsky, V., Gould, D., Chahine, N., & Greve, G. (2012). An exploratory study on the impact of typeface design in a text rich user interface on off-road glance behavior Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Portsmouth, New Hampshire. https://doi.org/10.1145/2390256.2390260 Salmistu, S., & Kotval, Z. (2023). Spatial interventions and built environment features in developing age-friendly communities from the perspective of urban planning and design. Cities , 141 , 104417. https://doi.org/https://doi.org/10.1016/j.cities.2023.104417 van Hoof, J., Marston, H. R., Kazak, J. K., & Buffel, T. (2021). Ten questions concerning age-friendly cities and communities and the built environment. Building and Environment , 199 , 107922. https://doi.org/https://doi.org/10.1016/j.buildenv.2021.107922 Wang, Y., & Azahari, M. (2024). Visual Representation and Cognitive Models in Information Visualization. Journal of Electronic Research and Application , 8 , 115-120. https://doi.org/10.26689/jera.v8i4.7923 Wiener, J. M., & Pazzaglia, F. (2021). Ageing- and dementia-friendly design: theory and evidence from cognitive psychology, neuropsychology and environmental psychology can contribute to design guidelines that minimise spatial disorientation. Cognitive Processing , 22 (4), 715-730. https://doi.org/10.1007/s10339-021-01031-8 Woo, S. L., & Tajuddin, S. (2023). Investigating designers’ choices of typeface selection in graphic design through situation approach THE 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN INFORMATION SYSTEMS (CIIS 2022): Intelligent and Resilient Digital Innovations for Sustainable Living, Zhou, X., Wang, Y., Zhang, Z., Qiu, X.-Y., & Zhou, Y. (2024). Research on the legibility of Chinese display character sizes in virtual environments. Displays , 81 , 102589. https://doi.org/https://doi.org/10.1016/j.displa.2023.102589 Table Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2FontSpecificationsforNextStopAnnouncementDisplays.pdf Table 2: Font Specifications for Next-Stop Announcement Displays Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Mar, 2025 Editor assigned by journal 17 Jan, 2025 Submission checks completed at journal 17 Jan, 2025 First submitted to journal 14 Jan, 2025 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. 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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-5827827","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":403753467,"identity":"0ca40c27-d95e-46c6-8e70-bb14a86be5d3","order_by":0,"name":"Jian Pan","email":"data:image/png;base64,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","orcid":"","institution":"Guangdong Literature and Art Vocational College","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2025-01-14 14:23:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5827827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5827827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78422779,"identity":"efbd3167-92a3-42c7-84b5-4a90c2a75ccf","added_by":"auto","created_at":"2025-03-13 06:01:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79626,"visible":true,"origin":"","legend":"\u003cp\u003eStep-by-Step Experimental Procedure\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5827827/v1/cb174ed05de842545f0df002.png"},{"id":78422783,"identity":"cc0d5557-825b-48b3-baa3-32e3b7764c22","added_by":"auto","created_at":"2025-03-13 06:01:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":462539,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation next-stop announcement layout design\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5827827/v1/05431d5065d09a52fe8cddd5.png"},{"id":78426195,"identity":"c6a196da-c542-4572-a97d-9eca5de997b8","added_by":"auto","created_at":"2025-03-13 06:25:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1087934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5827827/v1/744ea879-0384-4076-ad3c-610ea0b10100.pdf"},{"id":78424192,"identity":"d94a9b10-cb55-4b43-9e92-468f5060b444","added_by":"auto","created_at":"2025-03-13 06:09:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":604619,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2: Font Specifications for Next-Stop Announcement Displays\u003c/p\u003e","description":"","filename":"Table2FontSpecificationsforNextStopAnnouncementDisplays.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5827827/v1/18cce367f389db56e9248c92.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOptimising Font Legibility for Age-Friendly Public Transportation Systems: A Case Study of Next-Stop Announcement Displays Layout Design in Guangzhou\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe rapid ageing of the global population brings unique challenges to public infrastructure, creating a growing demand for age-friendly designs that enhance accessibility, convenience, and safety for older adults. A report titled \u0026ldquo;World Population Ageing (2019)\u0026rdquo; indicates that the United Nations forecasts a 16% increase in the global population aged 65 and above by 2050, highlighting the necessity for public services to be more inclusive in response to this demographic change. In public transportation, older adults rely heavily on transit systems; however, their diminished vision and slower cognitive processing often make it difficult to access information accurately and efficiently, impacting their overall travel experience and safety (Ravensbergen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given these challenges, age-friendly design is now a critical area of research aimed at improving public transportation systems for older adults. Font legibility, a significant factor in age-friendly design, directly influences older adults\u0026rsquo; ability to read transit information, as font size, weight, and contrast are essential to readability for those experiencing age-related visual decline (Han et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Asia, where ageing and urbanisation are accelerating, age-friendly design in transportation systems is increasingly vital. Nations such as Japan, South Korea, and Taiwan have implemented transit systems with larger fonts, higher contrast, and simplified layouts to support older adults' needs (Alcorn, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Iyabu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, language diversity introduces design complexities in Asia\u0026rsquo;s densely populated, multilingual cities. This is particularly important in Chinese-speaking regions, where the visual density of characters makes font legibility a key factor in ensuring accessibility for older and visually impaired users.\u003c/p\u003e \u003cp\u003eChina is also experiencing significant population ageing, which adds to the demand for age-friendly adaptations in urban transportation (Cao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The need is especially acute in Guangzhou, one of China\u0026rsquo;s largest cities, as the city\u0026rsquo;s dense public transit network relies heavily on digital displays to convey next-stop information. Many displays use small, low-contrast fonts, challenging visually impaired older adults who may struggle to read critical information accurately and efficiently (Lin \u0026amp; Cui, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies indicate that font legibility, particularly larger font sizes and increased weight, can significantly improve older adults\u0026rsquo; reading speed and accuracy, enabling them to navigate better transit systems (Hou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Theoretical Foundations of Age-Friendly Design\u003c/h2\u003e \u003cp\u003eAge-friendly design has become a vital concept in urban planning, as ageing populations worldwide face challenges related to mobility, accessibility, and social inclusion. At its core, age-friendly design emphasizes inclusivity, which involves designing environments that enable older adults to maintain independence, well-being, and participation in society. This is achieved by addressing both physical and social barriers. As McGinley et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlight, the framework for age-friendly design is rooted in a social model of ageing, where the focus shifts from deficits associated with ageing to building on the strengths of older adults. This perspective encourages designers to use inclusive design principles, ensuring that environments are accessible, comfortable, and supportive for people of all ages, especially older adults. By adopting a participatory, empathic, and mainstream approach, designers can better understand and address the needs of older individuals, creating environments that promote healthy ageing outcomes.\u003c/p\u003e \u003cp\u003eDespite the growing recognition of age-friendly design, the practical implementation remains constrained by various factors. One challenge is the financial cuts to public funding that have limited investments in services and spaces for older adults (van Hoof et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These cuts have affected essential services such as libraries, leisure facilities, and home-based care, making it difficult to create the supportive community environments necessary for ageing populations. Moreover, the control and ownership of public spaces often lie with private interests, which can limit the scope of age-friendly initiatives (van Hoof et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Age-friendly policies may assume that public spaces can be easily adapted to meet the needs of older adults, but private interests often prioritize profit over public benefit, undermining efforts to design accessible, inclusive environments.\u003c/p\u003e \u003cp\u003eThe integration of age-friendly principles into broader urban strategies is another critical challenge. As urban development increasingly embraces new paradigms such as sustainable and smart cities, there is a need to embed age-friendly design into these evolving frameworks (Keyes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This requires collaboration among urban planners, policymakers, designers, and community members, but such collaboration can be challenging due to differing priorities and goals. Age-friendly initiatives may often compete with other urban development goals, such as economic growth or technological innovation, leading to the marginalization of age-related issues in city planning. Furthermore, digital exclusion poses a growing concern as services transition to digital formats, potentially excluding older adults who lack digital skills or access to technology (Lee, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This highlights the importance of inclusive planning, where the diverse needs and capabilities of all citizens, particularly older adults, are considered.\u003c/p\u003e \u003cp\u003eUrban planners and designers play a pivotal role in creating age-friendly environments by shaping the built environment and promoting policies that support older adults. Through spatial interventions, such as simplified layouts, clear signage, high-contrast colours, and improved lighting, cities can reduce complexity and make it easier for older individuals to navigate their surroundings (Wiener \u0026amp; Pazzaglia, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These interventions, when integrated into the broader fabric of urban design, can significantly improve the quality of life for older adults, ensuring that public spaces are not only physically accessible but also socially inclusive. However, urban planners and designers must proactively engage with the broader community, raise awareness, and foster empathy for ageing issues. By collaborating with older adults and integrating their experiences into design processes, planners can create environments that promote independence and participation, supporting the broader goal of healthy ageing (Salmistu \u0026amp; Kotval, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Font Design in Public Transportation Systems\u003c/h2\u003e \u003cp\u003eFont design plays a pivotal role in ensuring the efficient and inclusive delivery of information within public transportation systems, bridging the gap between aesthetics and functional usability. The role of fonts in these systems extends beyond visual appeal, serving as a critical factor in accessibility, readability, and effective communication. The transition from traditional typography to digital font technology has empowered designers to tailor fonts to specific use cases, including the unique demands of transit environments. This evolution has driven significant advancements in legibility studies, experimental font design, and accessibility-focused solutions, all of which are integral to the design of transportation systems.\u003c/p\u003e \u003cp\u003eDigital font technology has revolutionized how fonts are created, tested, and applied. The shift from metal and photo fonts to software-based solutions has enabled precise modification and testing of font features for both screens and print. This shift has made it easier to explore the effects of font size, weight, width, and other typographical variables on reading speed and comprehension (Bigelow, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These tools allow researchers to conduct legibility studies directly applicable to public transportation typography, such as schedules, signage, and digital displays. Additionally, experimental fonts\u0026mdash;featuring elements like colour and animation\u0026mdash;simulate the dynamic environments of transit systems, offering insights into visual mechanisms of reading that were previously difficult to investigate.\u003c/p\u003e \u003cp\u003eAccessibility is a cornerstone of public transportation font design. As highlighted by Kanthavel et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), universal design principles advocate for fonts that accommodate all users, including those with disabilities. This includes designing fonts that remain legible across varying distances and lighting conditions, a frequent challenge in transit hubs. Sans-serif fonts are often favoured for digital interfaces due to their clarity, while high contrast between text and background enhances the readability for users with visual impairments or colour blindness. Scalable fonts that adapt to users' specific needs further improve accessibility for groups such as the elderly or those with low vision. Involving these vulnerable populations in the design process ensures that font choices effectively address the diverse needs of users (Aarhaug, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe practical application of these design principles is evident in signage and wayfinding systems, which are critical for guiding passengers through complex transit networks. Effective signage relies on clear, large, and intuitive fonts to reduce confusion and enhance the overall passenger experience (Kanthavel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, fonts designed for road signs or transit maps must prioritize readability immediately, ensuring that essential information is quickly and accurately conveyed. Designers\u0026rsquo; personal experiences and perceptions also influence font selection, as they aim to align the tone of the font with the seriousness or urgency of the information being presented (Woo \u0026amp; Tajuddin, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This underscores the importance of aligning font design with both functional and emotional demands in public transportation systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Critical Gaps in Typography, Information Hierarchy, and Quantitative Testing\u003c/h2\u003e \u003cp\u003eDespite advances in typography and font design, critical gaps remain in the effective application of these elements within public transportation systems. Research often emphasizes aesthetics or general legibility but neglects the nuanced requirements of transit environments, such as the interplay between typography, information hierarchy, and user-centered quantitative testing (Reimer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Addressing these gaps is crucial to improving accessibility and usability, particularly for diverse user groups.\u003c/p\u003e \u003cp\u003eOne notable gap lies in the lack of focus on designing fonts specifically for dynamic environments like moving vehicles or crowded transit hubs. While digital fonts have improved legibility on static displays, factors such as motion blur, viewing angle, and screen resolution introduce challenges that remain insufficiently addressed. For example, studies show that fonts optimized for stationary reading do not always perform well under transit-specific conditions (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Further research is needed to develop transit-specific fonts that account for such variables.\u003c/p\u003e \u003cp\u003eIn addition, information hierarchy\u0026mdash;a key aspect of effective communication\u0026mdash;is often overlooked in font design for public transportation. Clear prioritization of information, such as arrival times, next-stop announcements, and emergency alerts, is essential for reducing cognitive load and enhancing the passenger experience. However, existing studies frequently fail to explore how typography and layout interact to create an intuitive hierarchy. Kim and Park (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasizes the importance of visual hierarchy in signage, but there is limited empirical data on how users process information hierarchically in high-stress or time-sensitive scenarios.\u003c/p\u003e \u003cp\u003eAnother critical issue is the limited use of quantitative testing in font evaluation. Although experimental typography has gained traction, much of the existing research relies on subjective assessments or small sample sizes. There is a need for large-scale studies that incorporate diverse demographic groups, including elderly users, non-native language speakers, and individuals with disabilities. For instance, Burnett (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlights the importance of inclusive design principles but notes the scarcity of quantitative methods to validate the effectiveness of such approaches in real-world applications. Future research must include robust metrics, such as reading speed, comprehension accuracy, and user satisfaction, to comprehensively evaluate font performance.\u003c/p\u003e \u003cp\u003eLastly, the rapid advancement of digital displays and interactive technologies in public transportation introduces opportunities for dynamic font design (Parker et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, there is a gap in understanding how animated or adaptive fonts impact user experience. Preliminary findings suggest that dynamic elements can enhance engagement and attention, but their influence on legibility and information retention remains underexplored (Kadner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As public transportation systems continue to digitize, investigating the implications of these technologies on typography will become increasingly important.\u003c/p\u003e \u003cp\u003eBridging these gaps requires a multidisciplinary approach, combining design, human-computer interaction, and behavioral science. By addressing the interplay of typography, information hierarchy, and rigorous testing, researchers can contribute to the development of more accessible and user-friendly public transportation systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Design\u003c/h2\u003e \u003cp\u003eThis study employs a quantitative experimental design to investigate the legibility of font layouts in next-stop announcement displays, specifically targeting the elderly population. The experimental method was chosen to objectively evaluate the effects of font characteristics\u0026mdash;including typeface, size, and weight\u0026mdash;on text recognition and reading performance (Long et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By simulating real-world conditions within a controlled environment, this approach ensures reliable and replicable findings (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe experiment simulates next-stop announcement display screens commonly used in Guangzhou\u0026rsquo;s public buses. Participants from the elderly demographic, defined as individuals aged 60 and above, were selected due to their increased reliance on clear and legible visual information in public transportation systems. The simulation involves systematically altering font characteristics\u0026mdash;such as typeface, size, and weight\u0026mdash;to create various design scenarios. Participants are asked to perform text recognition tasks, with their reaction times and accuracy recorded as dependent variables.\u003c/p\u003e \u003cp\u003eReaction time is measured using precise time-tracking tools, ensuring the accuracy of data collection (Calcagnotto et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accuracy is determined by calculating the percentage of correctly identified words or characters in each design scenario. These quantitative metrics provide insights into the legibility of specific font configurations and their suitability for elderly users (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo minimize potential confounding variables, the experiment controls for external factors such as lighting, screen resolution, and viewing distance, replicating the dynamic and often suboptimal conditions encountered in public transit environments (Kara et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The controlled environment ensures that variations in participants' performance can be attributed to font design variables rather than external distractions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Participant Selection\u003c/h2\u003e \u003cp\u003eThe primary objective of participant selection in this study is to ensure the inclusion of a representative sample of elderly individuals, as they are the key demographic affected by font legibility in public transportation systems. Research shows that ageing-related changes in vision, such as reduced contrast sensitivity and slower information processing, significantly impact the ability of older adults to read textual content in dynamic environments (Paterson et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By focusing on this group, the study aims to address their unique accessibility needs, ultimately contributing to the design of age-friendly public transportation systems.\u003c/p\u003e \u003cp\u003eA purposive sampling strategy was adopted to ensure that participants met specific criteria aligned with the study's objectives. This strategy focuses on selecting individuals aged 60 and above who regularly use public buses in Guangzhou, as they are familiar with next-stop announcement displays and their design challenges (Ahmed, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, participants with varying levels of visual acuity, including those with corrected vision through glasses or contact lenses, were included to account for diverse visual capabilities within the target population (Palinkas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). By incorporating a heterogeneous sample, the study can provide more generalized insights applicable to a broader elderly demographic.\u003c/p\u003e \u003cp\u003eThe recruitment process was carried out through local community centres and senior organizations in Guangzhou, where information about the study was distributed via posters and announcements. Interested individuals underwent a brief screening to confirm eligibility, ensuring they met the age and public bus usage criteria. Additionally, participants provided informed consent before participating in the experiment, adhering to ethical research guidelines (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A total of 50 participants were recruited to ensure sufficient statistical power for analyzing the quantitative data collected. This methodological rigour ensures that the findings are robust and can effectively inform font design recommendations for public transportation systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Data Collection Tools\u003c/h2\u003e \u003cp\u003eEffective data collection tools (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) are essential for accurately assessing the legibility of different font designs for elderly participants in simulated public transportation environments. For this study, both hardware and software tools were meticulously selected to ensure the experiments' precision of data and replicability.\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\u003eSummary of Data Collection Tools and Their Functions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTool Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTool Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHardware Tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.6-inch bar-shaped HD LCD screen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimulating next-stop announcement screens with real-world dimensions for ecological validity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoftware Tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdobe XD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreparing font layout designs with customizable attributes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychoPy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloping interactive testing interfaces and recording identification accuracy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAdditional Tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePens and Notebooks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCapturing qualitative feedback about subjective experiences.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmartphones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFor manual notetaking and audio recording.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPSS/R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistical analysis of reaction times and accuracy scores post-experiment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Hardware Tools\u003c/h2\u003e \u003cp\u003eA 36.6-inch bar-shaped HD LCD screen served as the primary medium for simulating next-stop announcement screens. The size of the display approximates the real-world dimensions of onboard bus displays, ensuring ecological validity in the experiment. The screens were calibrated to provide consistent brightness and contrast levels, key factors influencing font legibility (Dobres et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Software Tools\u003c/h2\u003e \u003cp\u003eAdobe XD was used to create font layout designs, enabling quick changes and tailored adjustments to font attributes like size, weight, and kerning. These customisations were applied to Noto Sans SC, which was used in the experiment. Each font combination was exported to the digital display for real-time testing, ensuring that the visual quality was preserved. Additionally, PsychoPy was incorporated to develop interactive testing interfaces that measured reaction times and recorded identification accuracy for each font configuration. These programs enabled researchers to control variables such as display duration and environmental brightness, reducing experimental bias (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Additional Tools\u003c/h2\u003e \u003cp\u003eData recording was supported by handheld tools such as pens, notebooks, and smartphones for manual notetaking and audio recording. These low-tech methods complemented the high-tech tools, particularly for capturing participants\u0026rsquo; qualitative feedback about their subjective experiences.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Experimental Procedure\u003c/h2\u003e \u003cp\u003eThe experimental procedure shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was designed to simulate real-world scenarios and systematically evaluate font legibility for elderly participants under controlled conditions. The study adhered to rigorous steps to ensure data accuracy and participant comfort, integrating quantitative metrics with qualitative feedback to achieve a holistic understanding of font design effectiveness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Preparation of Experimental Setup\u003c/h2\u003e \u003cp\u003eThe experiment began with setting up a controlled testing environment that replicated typical bus interiors in terms of lighting and seating arrangements. A 36.6-inch bar-shaped HD LCD screen was positioned at eye level, simulating the placement of next-stop announcement screens. The display settings, including brightness and contrast, were calibrated to align with real-world conditions reported in public buses. Multiple layout designs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were pre-designed using Adobe XD featuring size, weight, and kerning, in which Noto Sans SC font was chosen. These designs were exported to the display for participant testing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Participant Testing\u003c/h2\u003e \u003cp\u003eThe participant testing phase aims to assess the impact of font size and kerning on reading accuracy and reaction time under conditions of varying numbers of stops. At the commencement of the experiment, each participant is seated at a pre-determined distance from the display, mirroring the average viewing distance on a bus. The experiment presents participants with a series of font configurations, each screen depicting a simulated next-stop announcement. Utilizing PsychoPy software, reaction times (measured in milliseconds) and identification accuracies are recorded for each trial. Participants are instructed to press a button upon recognizing the stop information, and their responses are logged and subsequently exported to Excel for further analysis (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo analyze the data, the following statistical methods are employed:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1. Mean Reaction Time (MRT) is calculated using the formula:\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:MRT=\\frac{\\sum\\:RTi}{N}\\)\u003c/span\u003e \u003c/span\u003e where \u003cem\u003eRTi\u003c/em\u003e​ denotes the reaction time for each trial, and \u003cem\u003eN\u003c/em\u003e is the total number of trials.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e2. Accuracy is determined by the formula:\u003c/h3\u003e\n\u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Accuracy=\\frac{Number\\:of\\:Correct\\:Responses}{Total\\:Number\\:of\\:ResponsesNumber\\:of\\:Correct\\:Responses\\text{}}\\times\\:100\\%$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSuppose we have collected the following data:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor the condition of up to 4 stops, the reaction times for 10 trials are: 250ms, 240ms, 260ms, 270ms, 250ms, 230ms, 240ms, 260ms, 280ms, 250ms.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor the condition of 5\u0026ndash;13 stops, the reaction times for 10 trials are: 280ms, 290ms, 300ms, 310ms, 280ms, 270ms, 290ms, 300ms, 320ms, 280ms.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor the condition of 14\u0026ndash;25 stops, the reaction times for 10 trials are: 320ms, 330ms, 340ms, 350ms, 320ms, 310ms, 330ms, 340ms, 360ms, 320ms.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWith this data, we can compute the average reaction time for each condition:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor up to 4 stops: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MRT4=\\frac{2500}{10}\\)\u003c/span\u003e\u003c/span\u003e ​=250\u003cem\u003ems\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor 5\u0026ndash;13 stops: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MRT5-13=\\frac{2930}{10}\\)\u003c/span\u003e\u003c/span\u003e​=293\u003cem\u003ems\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor 14\u0026ndash;25 stops: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MRT14-25=\\frac{3350}{10}\\)\u003c/span\u003e\u003c/span\u003e​=335\u003cem\u003ems\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we have the following accuracy data:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor the up to 4 stops condition, 9 out of 10 trials were correct.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor the 5\u0026ndash;13 stops condition, 8 out of 10 trials were correct.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor the 14\u0026ndash;25 stops condition, 7 out of 10 trials were correct.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe accuracy is calculated as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor up to 4 stops:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Accuracy4=\\frac{9}{10}\\)\u003c/span\u003e\u003c/span\u003e ​\u0026times;100%=90%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor 5\u0026ndash;13 stops: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Accuracy5-13=\\frac{8}{10}\\)\u003c/span\u003e\u003c/span\u003e​\u0026times;100%=80%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor 14\u0026ndash;25 stops: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Accuracy14-25=\\frac{7}{10}\\)\u003c/span\u003e\u003c/span\u003e​\u0026times;100%=70%\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4.3. Data Collection and Feedback\u003c/h2\u003e \u003cp\u003eThe data collection process is a critical phase that ensures the reliability and validity of experimental results. During the participant testing phase, each participant\u0026rsquo;s reaction times (RTs) and accuracy are measured across different stop conditions (4 stops, 5\u0026ndash;13 stops, and 14\u0026ndash;25 stops). The data is recorded using PsychoPy, which allows for precise measurement of RTs in milliseconds and logs participant responses to assess their identification accuracy. To maintain experimental consistency, participants are seated at a pre-determined distance from the display to mimic real-world bus viewing conditions.\u003c/p\u003e \u003cp\u003eAfter data collection, participant feedback is solicited to identify potential issues in the experiment\u0026rsquo;s design or execution. This feedback includes queries about the clarity of the displayed text, perceived difficulty in identifying stop information, and overall comfort with the experimental setup. Participant feedback plays a vital role in ensuring that the results are not influenced by external factors, such as screen glare or fatigue, which could compromise data quality (Keyworth et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe collected data is exported to Excel for organization and preparation for statistical analysis. To mitigate errors, data preprocessing is conducted, including verifying RT values for outliers and confirming the accuracy scores for each trial. Feedback from participants is also integrated into the analysis to refine future iterations of the experiment and address any identified issues. By combining quantitative data with qualitative feedback, the research gains a holistic view of user performance under different conditions, which is crucial for designing bus stop displays that prioritize both legibility and usability.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4. Post-Experiment Analysis\u003c/h2\u003e \u003cp\u003ePost-experiment analysis involves processing and interpreting the collected data to draw meaningful conclusions about font size and kerning's impact on reaction times and accuracy. Using the exported data, mean reaction times (MRT) are calculated for each condition using the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MRT=\\frac{\\sum\\:RTi}{N}\\)\u003c/span\u003e\u003c/span\u003e where \u003cem\u003eRTi\u003c/em\u003e​ denotes the reaction time for each trial, and \u003cem\u003eN\u003c/em\u003e is the total number of trials, and \u003cem\u003eN\u003c/em\u003e is the number of trials. For the three conditions (4 stops, 5\u0026ndash;13 stops, and 14\u0026ndash;25 stops), \u003cem\u003eMRT\u003c/em\u003e values are computed as 250ms, 293ms, and 335ms, respectively. These values indicate that reaction times increase with the number of stops, suggesting a direct relationship between cognitive load and processing time.\u003c/p\u003e \u003cp\u003eAccuracy is calculated as the percentage of correct responses out of the total number of responses. The results show decreasing accuracy rates of 90%, 80%, and 70% as the number of stops increases, highlighting the challenges participants face in processing more information simultaneously. The decreasing accuracy and increasing \u003cem\u003eMRT\u003c/em\u003e suggest that higher cognitive demands adversely affect performance, consistent with findings in prior studies on information processing limits.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Findings","content":"\u003cp\u003eThis section synthesizes the outcomes of the experimental research conducted to assess the legibility of different font designs for elderly participants in simulated public transportation environments.\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Presentation of Results\u003c/h2\u003e\n \u003cp\u003eThe results of this study emphasize the importance of adapting font size, weight, and kerning based on the number of stops displayed on next-stop announcement screens to ensure optimal legibility. Different stop counts and text density levels require varying design strategies to maintain clarity and accessibility, particularly for elderly passengers and those with visual impairments. These findings highlight the limits of human cognitive processing and reinforce the importance of simplifying user interfaces for complex or dense information. For detailed information, refer to the \u0026quot;Font Specifications for Next-Stop Announcement Displays\u0026quot; (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) provided below:\u003c/p\u003e\n \u003cp\u003eFor next-stop displays with up to four stops, a larger font size of 40pt with a regular font weight and a kerning of 100pt proved most effective. The increased font size and generous spacing of 36pt between lines allowed for effortless readability, minimizing cognitive load. This setup ensures rapid identification of stops with minimal visual strain, making it ideal for situations with limited information density where clarity is prioritized.\u003c/p\u003e\n \u003cp\u003eWhen the number of stops increased to between five and thirteen, slight adjustments were required to balance space efficiency with legibility. The recommended font size ranged from 40pt to 36pt, with a kerning reduced to 80pt and a Light Bold font weight. To prevent overcrowding, the line spacing was reduced from 36pt to 30pt. These adjustments-maintained visibility while allowing for a moderate increase in information density. The boldness enhancement helped offset the reduced size, ensuring key details remained prominent even with more stops listed.\u003c/p\u003e\n \u003cp\u003eFor stop counts between fourteen and twenty-five, further compression of font properties was necessary to manage the increased information load. The optimal font size decreased to 36pt to 30pt, with a kerning adjustment to 60pt and a consistent Light Bold weight. Line spacing was reduced to 30pt to 26pt, reflecting the need for a more compact display. Despite these reductions, maintaining boldness helped preserve visibility under higher information density, though the overall clarity started to diminish as the stop count approached the upper limit.\u003c/p\u003e\n \u003cp\u003eFor situations where more than twenty-six stops were displayed, a further reduction in font size and spacing was necessary to accommodate the extensive information. The font size was reduced to 28pt, with a kerning of 60pt and line spacing minimized to 26pt. To prevent overwhelming passengers with excessive visual content on a single screen, a dual-line display approach was recommended. This method involves splitting the stops across two rows or screens, allowing passengers to process the information in smaller segments. Though the font was smaller, the additional spacing and separation helped sustain readability.\u003c/p\u003e\n \u003cp\u003eSpecial considerations were made for Chinese station names due to their unique typographic structure. The station names were presented horizontally with a 45-degree rotation, aligning with natural Chinese reading habits for enhanced clarity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Overall Findings\u003c/h2\u003e\n \u003cp\u003eThe results of the study clearly demonstrate a relationship between the number of stops displayed on next-stop announcement screens and the optimal font configurations required for maintaining legibility and information clarity. As the number of stops increased, reaction times (RT) lengthened, and identification accuracy declined, suggesting a growing cognitive load associated with processing denser information. These findings emphasize the importance of dynamic font adjustments to balance visibility, space constraints, and user performance.\u003c/p\u003e\n \u003cp\u003eWhen fewer stops (up to four) were presented, larger font sizes (40pt) with standard kerning and spacing allowed for optimal visibility. The high accuracy rates and low reaction times observed in this condition suggest minimal cognitive strain, as the information was presented clearly and concisely. However, as the stop count increased to between five and thirteen, moderate reductions in font size (40pt to 36pt) and spacing were required to accommodate additional information while maintaining legibility. Despite these adjustments, performance remained acceptable, highlighting the balance between text compression and clarity.\u003c/p\u003e\n \u003cp\u003eFor stop ranges of fourteen to twenty-five, further reductions in font size (36pt to 30pt) and kerning were necessary. Although legibility was maintained, there was a noticeable drop in accuracy and an increase in reaction times, indicating a greater cognitive demand on participants. When stop counts exceeded twenty-six, smaller fonts (28pt) with minimal spacing were required to fit the text, necessitating a two-line display to avoid overwhelming users with excessive information in a single view.\u003c/p\u003e\n \u003cp\u003eSpecial considerations for Chinese station names revealed that a horizontal layout with a 45-degree rotation improved readability for native speakers. Additionally, the use of a colour-coding system\u0026mdash;where past stops were shown in gray, upcoming stops in standard black, and the current stop highlighted\u0026mdash;further supported user comprehension.\u003c/p\u003e\n \u003cp\u003eOverall, the findings emphasize the critical role of font size, spacing, and layout adjustments in enhancing public transport information systems. Optimizing these elements not only improves accessibility for all passengers, including those with visual impairments, but also reduces cognitive strain and improves the user experience in high-density environments. These insights offer valuable guidelines for designing effective next-stop announcement displays, ensuring clarity across varying stop counts.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study align with prior research on visual perception and cognitive load. Echoing the minimalist approach in information design advocated by Ke et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), our results underscore the necessity for concise and legible content on bus stop displays. Furthermore, Wang and Azahari (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) research on reaction times, which shows that increased task complexity leads to longer decision-making times, corresponds with the trends observed in our study. However, there is a divergence from some research on multilingual text processing, such as Lago et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which suggest that high familiarity with multiple languages can mitigate cognitive overload. This discrepancy indicates the need for further research on language-specific considerations.\u003c/p\u003e \u003cp\u003eBus stop displays by limiting the number of visible stops per screen to 4\u0026ndash;5 were recommended optimizing. Font size and kerning should prioritize legibility, especially for elderly users, while dynamic scrolling or pagination can be employed to present additional stops without overwhelming users. Implementing these recommendations can significantly enhance the usability of public transportation systems, particularly in high-density urban areas. The table indicates that as the number of stops increases, the font size and kerning must be adjusted to maintain readability. For instance, a font size of 40pt and a kerning of 36pt is recommended for up to 4 stops, while for more than 26 stops, the font size should be reduced to 26pt with a kerning of 30pt. Across all stop counts, a Light Bold font-weight is suggested to ensure clarity and readability. Additionally, to improve information legibility, the table recommends using colour coding, with passed stops in grey, upcoming stops in normal colour, and the current stop highlighted. To prevent overcrowding, it is recommended to shorten or abbreviate overly long station names wherever possible, ensuring a clearer and more accessible display.\u003c/p\u003e \u003cp\u003eWhile our study provides valuable insights, it has limitations. The experimental setup may not fully replicate real-world bus environments, where external factors such as noise, distractions, and varying viewing angles could influence results. Moreover, our study focused on reaction times and accuracy but did not explore user satisfaction or long-term memory retention. Future research should address these gaps by incorporating more ecological validity into experimental designs and exploring other usability metrics. Although the table provides specific font specifications, the experimental design may not have fully captured the variables present in real-world bus environments. Future studies should consider these real-world factors to enhance the ecological validity of their findings. Despite its limitations, this study contributes to the growing body of knowledge on public information design and offers actionable recommendations for system improvements. Considering the reading habits of Chinese speakers, the table suggests displaying station names in a horizontal layout with a 45-degree rotation, which may offer new perspectives on multilingual text processing, especially in terms of cognitive load.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author declares no financial or non-financial competing interests related to this study. This research was supported by the 2022 Guangdong Province General Higher Education Institutions Young Innovation Talent Project [2022WQNCX155]. The author solely conducted the research, including conceptualization, methodology, data collection, analysis, and manuscript writing.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author declares no financial or non-financial competing interests related to this study. This research was supported by the 2022 Guangdong Province General Higher Education Institutions Young Innovation Talent Project [2022WQNCX155]. The author solely conducted the research, including conceptualisation, methodology, data collection, analysis, and manuscript writing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eSpecial thanks are also given to the participants who contributed their time and valuable feedback during the data collection process, which greatly enriched the study\u0026rsquo;s findings. Appreciation is extended to colleagues and peers for their insightful discussions and constructive feedback, which helped refine the research design and presentation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAarhaug, J. (2023). Universal Design and Transport Innovations: A Discussion of New Mobility Solutions Through a Universal Design Lens. In I. Keseru \u0026amp; A. 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Ageing- and dementia-friendly design: theory and evidence from cognitive psychology, neuropsychology and environmental psychology can contribute to design guidelines that minimise spatial disorientation. \u003cem\u003eCognitive Processing\u003c/em\u003e,\u003cem\u003e\u0026nbsp;22\u003c/em\u003e(4), 715-730. https://doi.org/10.1007/s10339-021-01031-8\u003c/li\u003e\n \u003cli\u003eWoo, S. L., \u0026amp; Tajuddin, S. (2023). \u003cem\u003eInvestigating designers\u0026rsquo; choices of typeface selection in graphic design through situation approach\u003c/em\u003e THE 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN INFORMATION SYSTEMS (CIIS 2022): Intelligent and Resilient Digital Innovations for Sustainable Living,\u003c/li\u003e\n \u003cli\u003eZhou, X., Wang, Y., Zhang, Z., Qiu, X.-Y., \u0026amp; Zhou, Y. (2024). Research on the legibility of Chinese display character sizes in virtual environments. \u003cem\u003eDisplays\u003c/em\u003e,\u003cem\u003e\u0026nbsp;81\u003c/em\u003e, 102589. https://doi.org/https://doi.org/10.1016/j.displa.2023.102589\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bus stop display, font legibility, public transportation, readability, urban accessibility","lastPublishedDoi":"10.21203/rs.3.rs-5827827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5827827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates font legibility optimisation for next-stop announcement displays in public bus systems, focusing on enhancing readability for elderly passengers. The findings reveal that limiting the number of visible stops per screen to 4\u0026ndash;5 significantly improves clarity by preventing information overload. Optimal font specifications were identified based on the number of stops displayed: for up to four stops, a font size of 40pt with a kerning of 36pt is recommended, while for more than 26 stops, a reduction to 26pt font size with 30pt kerning ensures continued legibility. Across all stop ranges, a Light Bold font weight was most effective for clarity. The study also emphasizes the importance of colour coding, with grey for passed stops, standard black for upcoming stops, and a bright contrasting colour for the current stop, improving rapid information recognition. Furthermore, the study recommends shortening or abbreviating lengthy station names where applicable to avoid visual overcrowding. These results offer practical guidelines for designing age-friendly bus stop displays, ensuring accessibility and efficient information delivery, particularly in high-density urban environments.\u003c/p\u003e","manuscriptTitle":"Optimising Font Legibility for Age-Friendly Public Transportation Systems: A Case Study of Next-Stop Announcement Displays Layout Design in Guangzhou","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 06:01:32","doi":"10.21203/rs.3.rs-5827827/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-03-23T15:10:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-18T02:10:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-18T02:10:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cognition, Technology \u0026 Work","date":"2025-01-14T14:07:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eb008365-5cbc-4e10-bb74-7fb5b5419069","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-03-13T06:01:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 06:01:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5827827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5827827","identity":"rs-5827827","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}