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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Modeling vehicle-cyclists' interactions to support automated driving and advanced driving assistance systems</title>
      <link>https://trid.trb.org/View/2666908</link>
      <description><![CDATA[Cycling has gained increasing popularity across Europe, yet the frequency and severity of cyclist-vehicle conflicts at unsignalized intersections remain key road-safety concerns. This study investigates the interaction between drivers and cyclists in such settings, focusing on the role of intersection visibility (IV), difference in time to arrival (DTA) of the car and bicycle, and drivers' gaze behavior in shaping yielding decisions, braking patterns, and speed profiles. Using a driving simulator equipped with eye-tracking technology, participants completed multiple drives through the digital twin of a real-world intersection. The IV was systematically varied by repositioning a parked truck, while the DTA was controlled by triggering the virtual cyclist's approach at different temporal offsets relative to the car's arrival.Mixed-effects Bayesian regression models revealed that both IV and DTA significantly influenced the drivers' likelihood of yielding: higher visibility and a shorter time difference between vehicle and cyclist arrivals consistently increased yielding rates. Gaze behavior also emerged as a critical factor; earlier fixation on the crossing cyclist strongly correlated with the likelihood of deciding to yield. In contrast, no single predictor significantly explained the distance at which drivers initiated braking. Speed-profile analyses further underscored the finding that drivers' deceleration strategies are shaped by visibility constraints and perceived temporal pressure from oncoming cyclists.These findings highlight the importance of visibility, temporal cues, and visual attention metrics in intersection designs and advanced driver assistance systems. Safety technologies and automated features can more accurately anticipate driver-cyclist interactions when gaze behavior is integrated into their predictive models. Future work should confirm these insights through on-road studies, as well as exploring additional intersection layouts and environmental conditions to obtain more data that can lead to enhance both infrastructure design and automated vehicle algorithms.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666908</guid>
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    <item>
      <title>Automatic bicycle balance assistance reduces probability of falling at low speeds when subjected to handlebar perturbations version 5</title>
      <link>https://trid.trb.org/View/2666903</link>
      <description><![CDATA[Uncontrolled bicycles are generally unstable at low speeds. We add an automatically controlled steering motor to a consumer electric bicycle that stabilizes the riderless bicycle down to just below 4 kmh−1 to assist a rider in balancing the vehicle. We hypothesize that a such a stabilized bicycle will reduce the probability of falling. To test the system’s possible assistance during falls, we applied varying magnitude external handlebar perturbations to twenty-six participants who rode on a treadmill with the balance assist system both activated and deactivated. We show that the probability of recovering from a handlebar perturbation significantly increases when the balance assist is activated at a travel speed of 6 kmh−1. This positive effect is most prominent at and around the individual riders’ perturbation resistance threshold. We conclude that use of a balance assist system in real world bicycling can reduce the number of falls that occur near riders’ control authority limits.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666903</guid>
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    <item>
      <title>Citizen-science approach for an environmental analysis: The case study of university cyclists in Bologna</title>
      <link>https://trid.trb.org/View/2667072</link>
      <description><![CDATA[Climate change represents a critical vulnerability in urban areas. Shifting from the concept of Urban Resilience (UR) to Urban Sustainability (US) is considered a driver for reducing the impacts of climate change. This transition is feasible by encouraging bottom-up citizen engagement in urban policies and promoting sustainable active mobility, a sector in which cycling plays a significant role. This paper proposes an innovative methodology based on a citizen-science approach (bottom-up level), developed and carried out in Bologna, Italy, to investigate the correlation between the number of bike rides and users' awareness of environmental pollutants. About 50 bicycles specifically designed with environmental sensors to collect environmental data (PM10 and PM2.5) were distributed to university staff. A statistical analysis exploring possible relationships was carried out, and the main outcome was the identification of a multiple regression analysis (MLR) between trips, pollutants, and other variables related to the built environment. This integrated approach represents a novel contribution to the field, combining environmental monitoring and active mobility to support citizen-informed urban sustainability strategies.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667072</guid>
    </item>
    <item>
      <title>Discrepancies between intention, self-reported behavior, and actual behavior in e-bike helmet wearing: Evidence from child passengers with machine learning analyses</title>
      <link>https://trid.trb.org/View/2692528</link>
      <description><![CDATA[Helmets are a critical passive safety device that can dramatically reduce fatalities for e-bike riders and passengers. Most existing studies focused on improving intentions or self-reported behavior rather than actual behavior. The gap between intentions, self-reported behavior and actual behavior suggests that policies based on the former alone will have a limited impact on increasing helmet use. This study conducted 1035 questionnaires and follow-up field observations of parents putting helmets on their child passengers before riding e-bikes in Zhenjiang, China. This approach overcame the challenge of observing the intentions, self-reported behavior, and actual behavior of the same respondents. The results reveal substantial discrepancies: 84.5% of parents' intentions did not align with their actual behavior, and 72.6% of self-reports were inconsistent with observed actions. We used an XGBoost model interpreted via SHAP values to analyze these gaps. The model performed well in identifying gaps, achieving accuracy and precision rates above 80%, and recall and F1 scores above 90%. The number of helmets carried is a key factor contributing to the gap between intentions, self-reported behavior, and actual behavior. Ownership of dedicated child-helmets and parents’ helmet wearing behavior could also reduce the gaps. Psychological factors, such as subjective norm, perceived behavioral control, and attitude, also have significant impacts on the gaps. Practical recommendations include redesigning e-bikes to accommodate multiple helmets, deploying public helmet storage lockers at key destinations, and tailoring enforcement and education efforts to overcome specific behavioral obstacles.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692528</guid>
    </item>
    <item>
      <title>Understanding environmental factors affecting cyclists' perceptions in high-density neighborhoods using street view imagery</title>
      <link>https://trid.trb.org/View/2676357</link>
      <description><![CDATA[Cycling plays a crucial role in shaping sustainable transportation systems in high-density urban areas. However, insufficient studies have measured the eye-level cycling environment and analyzed its influence on cyclists' perceptions, particularly in high-density contexts. This study explores how various environmental factors, including GIS-based and eye-level indicators, affect cyclists' perceptions of safety, comfort, and attractiveness. By collecting street view images from cyclists' perspectives and conducting questionnaire surveys for image ratings, we investigated both linear and non-linear relationships between environmental factors and cyclists' perceptions. The findings highlight the substantial impact of cycling infrastructure, street greenery, and urban vibrancy, with different factors playing distinct roles in shaping various dimensions of perception. The results offer valuable insights into the complex relationship between the environment and cyclists' perceptions, helping urban planners and decision-makers develop targeted strategies for improving the cycling environment.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:17:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676357</guid>
    </item>
    <item>
      <title>Ultra-Wideband Technology for Improved Detection of Vulnerable Road Users in Urban Settings: Dataset and Evaluation</title>
      <link>https://trid.trb.org/View/2659155</link>
      <description><![CDATA[Autonomous Vehicles face significant safety challenges in complex urban environments, particularly in detecting and tracking vulnerable road users like pedestrians and cyclists, who are at higher risk of fatal accidents. This paper explores the potential of Ultra-Wideband technology as an additional sensing modality, known for its high ranging accuracy and robustness in challenging environments. Through real-world experiments, we provide a qualitative analysis of Ultra-Wideband performance in scenarios prone to intermittent vision failures, demonstrating its effectiveness in improving vulnerable road users' detection in urban driving scenarios. To enable its widespread application in autonomous driving, we also present WiDEVIEW, the first multimodal dataset that integrates LiDAR, three RGB cameras, GPS/IMU, and Ultra-Wideband sensors for providing urban driving scenarios with extensive pedestrian-vehicle interactions, which can aid in studying pedestrian-vehicle interactions, developing better pedestrian detection and tracking and eventually safe autonomous navigation algorithms by augmenting Ultra-Wideband and using the complimentary properties of Ultra-Wideband sensing with vision and LiDAR data. Finally, we demonstrate the potential applications of the Ultra-Wideband technology in vehicle to vehicle communication and vulnerable road users localization scenarios.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659155</guid>
    </item>
    <item>
      <title>Using brain response to measure pedestrian safety perception: Evidence from video and VR experiments</title>
      <link>https://trid.trb.org/View/2684271</link>
      <description><![CDATA[Accurately assessing pedestrians’ safety perceptions is essential for human-centric design, improving actual safety outcomes, and promoting walking in urban areas. However, the coexistence of pedestrians with micromobility users, such as cyclists, in shared spaces introduces complex interaction dynamics that challenge accurate evaluation using conventional methods. Traditional tools, such as image- or video-based surveys with Likert-scale questionnaires, are cost-effective but often limited by hypothetical bias and subjectivity. To address these limitations, the authors compare pedestrian experiences during bicycle interactions in both virtual reality (VR) and video-based environments using self-reported Likert-scale ratings and brain activity data. Participants were evaluated in two infrastructure scenarios: separated and shared lanes. The results show that brain response significantly increases when a pedestrian is overtaken by a cyclist in shared spaces, an effect observable only in VR, not in video-based conditions, highlighting VR’s superior ability to capture spatial cognition. Moreover, individuals with prior negative experiences, such as involvement in bicycle-related accidents, exhibit heightened brain activity during shared-space encounters in VR, a pattern not reflected in their subjective ratings. These findings underscore the value of brain response as an objective measure for understanding pedestrian experience in complex traffic environments. They also demonstrate the potential of immersive technologies to advance the assessment of perceived safety and guide the design of safer, more inclusive pedestrian infrastructure.]]></description>
      <pubDate>Mon, 27 Apr 2026 17:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684271</guid>
    </item>
    <item>
      <title>Benefits and Impacts of Complete Streets</title>
      <link>https://trid.trb.org/View/2579556</link>
      <description><![CDATA[Complete Streets (CS) is an approach for planning and designing streets that enables safety and mobility for all roadway users, i.e., pedestrians, bicyclists, motorists, and transit riders. CS policies can assist agencies at all levels in redefining their decision-making processes regarding multimodal transportation needs by creating vital strategies. Since the initial development of the CS concept in the early 2000’s, more than 1,700 agencies have implemented such policies in the USA. The Kentucky Transportation Cabinet (KYTC) recently developed the Complete Streets, Roads, and Highways Manual aiming to enhance a safe and equitable transportation system throughout the state. This generated the need to develop a methodology or a tool to evaluate and assess the potential benefits of CS projects. The primary objective of this study is to outline the potential benefits associated with the implementation of CS and develop an assessment framework. For this purpose, two literature reviews are conducted. The first focused on CS policies and the second on potential benefits from CS projects. The findings of these reviews allowed for the development of an assessment framework that KYTC can use to identify potential impacts and benefits of CS projects. These findings can serve as valuable insights for transportation agencies, aiding them in prioritizing CS projects. Future tasks will develop a comprehensive assessment tool to further facilitate decision-making and implementation.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579556</guid>
    </item>
    <item>
      <title>Anomaly detection as modularity-based community detection</title>
      <link>https://trid.trb.org/View/2682754</link>
      <description><![CDATA[When measuring how drivers overtake cyclists, one of the underlying problems is extracting the overtaking event from a time series of lateral distance readings. This note aims to describe a simple approach that seems effective in applications like ours. It consists of carefully transforming our problem into a network problem, then leveraging a community detection algorithm to extract subsequence candidates. Lastly, we choose the anomalous subsequence from the set of returned subsequences. To the best of our knowledge, this approach to anomaly detection does not appear in the literature even though it is intuitive, offers a fair amount of control, and is not computationally expensive. Our goal is to present the crux of the method with clarity and identify where more effort could improve it. We demonstrate our approach with modularity-based community detection and point out a shared nature of our approach with density-based cluster detection methods.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682754</guid>
    </item>
    <item>
      <title>The micromobility mindset: Socio-technical drivers of bike share scheme adoption in the UK</title>
      <link>https://trid.trb.org/View/2679167</link>
      <description><![CDATA[This paper explores the public's perceptions and socio-technical factors influencing the adoption of bike share schemes in the UK. The data was collected via an e-survey with 643 valid responses. The consistent Partial Least Square Structural Equation Modelling (PLS-SEM) bootstrapping algorithm was used to analyse the extended UTAUT2 model proposed by this study. Results show that most bike share scheme users preferred e-bikes over pedal bikes; most non-users have bike share schemes available locally; most frequent cyclists do not use bike share schemes; and the key motivator to use bike share schemes was improved safety and cycle infrastructure. For behavioural intention, the best predictor is perceived efficiency, followed by enjoyment, habit, and cycling infrastructure, while practical concerns, journey concerns, and social influence did not directly increase intention. For usage behaviour, key factors were cycling frequency, social influence, cycling infrastructure, and ease of use. The study provides a set of policy and practice recommendations in relation to the management and implementation of bike share schemes for the UK Government, local authorities and operators to increase bike share scheme adoption, including stronger legislation supporting integrated transport, better promotion of bike share schemes, improved stakeholder engagement and more inclusive infrastructure.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679167</guid>
    </item>
    <item>
      <title>Sharing the road with autonomous vehicles: US cyclists' attitudes, concerns, and infrastructure needs</title>
      <link>https://trid.trb.org/View/2655806</link>
      <description><![CDATA[Cyclist safety remains an important issue, with U.S. cycling fatalities rising to 966 in 2021 (a 1.9 % increase over 2020). As autonomous vehicles (AVs) become more common in mixed traffic, understanding their safety implications for cyclists is essential, since cyclists lack the physical protection of motor vehicle occupants and depend on predictable interactions to prevent crashes. Existing research rarely explores how cyclists perceive and engage with AVs, leaving infrastructure and communication needs largely underexamined. This study assessed U.S. cyclists' attitudes toward AVs, their anxiety about sharing the road, and their preferences for traffic infrastructure and AV communication interfaces. We conducted an online survey with 231 U.S. cyclists, measuring attitude, perceived usefulness, anxiety, receptivity (cyclists' willingness to access AVs), and preferences for four infrastructure designs and five AV-to-cyclist communication signs. Cyclists reported a positive attitude (mean score of 4.68 out of 7) and perceived usefulness (4.6 out of 7) of AVs despite moderate anxiety (3.48 out of 7). The results of a structural equation modeling analysis show that perceived usefulness and anxiety collectively explained 88 % (Adjusted R²) of the variance in receptivity. Protected cycle lanes with discontinuous (chosen by 68 % of participants) or continuous barriers (74 %) ranked highest for infrastructure. A combined visual/audio sign (52 %) and a cyclist-icon visual sign (47 %) were most preferred for communication. Incorporating cyclist-focused infrastructure and clear multisensory AV communication features can improve acceptance and safety as AVs are integrated into mixed-traffic environments.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655806</guid>
    </item>
    <item>
      <title>Trends and patterns of work-related cyclist fatalities in Brazil, 2014-2022</title>
      <link>https://trid.trb.org/View/2611424</link>
      <description><![CDATA[Bicycles are common mode of transport in countries with well-developed road infrastructure and traffic law enforcement. In Brazil, their use for economic activities, particularly on app-based deliveries, has surged. However, work-related cyclist fatalities (WRCFs) remain an understudied public health concern. This study characterizes WRCFs among individuals aged 10-69 in Brazil from 2014 to 2022, analyzing key demographic, occupational, temporal, and geographic trends. Using data from Brazil's Mortality Information System (MIS), the authors examined deaths classified under ICD-10 codes V10-V19 (pedal cyclist injured in transport accidents). Proportional mortality, mean age at death (MAoD), median, interquartile range and Years of Potential Life Lost (YPLL) were calculated. Of 6590 cyclist-related fatalities, 272 (4.1%) were confirmed as WRCFs. Most victims were male (87.1%), aged 29-58 years (68.3%), white (49.7%), had low education levels (< 12 years, 82.4%) and were single (45.9%). Fatalities seem concentrated in the South/Southeast regions (71%), with 61.3% involving collisions with motor vehicles (ICD-10 codes V13-V14). The highest proportions of WRCFs occurred among industrial (42%) and sales/trade (30.9%) workers. The MAoD was 43.6 years, with a total of 8896.4 YPLL. Notably, work-related fatality classification was missing in 57% of cases, highlighting the need for improved death certificate reporting in the country. Enhancing data quality is essential to understanding the risks faced by workers, many of whom are engaged in precarious, informal employment without social protection.]]></description>
      <pubDate>Tue, 21 Apr 2026 09:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611424</guid>
    </item>
    <item>
      <title>Optimizing External Human-Machine Interfaces (eHMIs) Designs in Autonomous Vehicles to Improve Communication with Drivers and Bicyclists</title>
      <link>https://trid.trb.org/View/2691668</link>
      <description><![CDATA[Autonomous Vehicles (AVs) will transform road safety and efficiency in the years to come, but achieving this requires large-scale deployment, trust, and understanding from all human road users, including drivers and bicyclists. External Human-Machine Interfaces (eHMIs) are becoming a crucial part of the process, enabling intuitive communication between AVs and other road users. This project aims to develop, assess, and optimize the concept of eHMIs to foster positive perceptions, build trust, and ensure safe interactions in mixed traffic scenarios. This study will involve a test of about 40 participants who will interact with AVs fitted with various eHMI prototypes under controlled conditions using driving and bicycle simulators. Behavioral metrics like the perception-reaction time (PRT), the perceived level of comfort, and the perceived level of trust, as well as transportation metrics like travel time, intersection clearance time, and near-miss incidents, will be assessed for different designs for the eHMI, including visual-based (LED Displays, Symbolic Messages, Color-coded Signals, Animated Indicators, etc.) and multimodal designs. Longitudinal experiments will measure the impact of acclimatization and determine the best eHMI setups, followed by field tests under realistic conditions for verification. User-focused optimization tools will also be designed to adapt enhanced eHMI setups to various demands and scenarios. Expected outcomes will include best-in-class eHMI designs for increased road safety, operational efficiency, and user confidence, providing valuable guidance for city planners, policymakers, and AV manufacturers.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:39:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691668</guid>
    </item>
    <item>
      <title>Modeling Bicyclist Behavioral Patterns and Multi-Faceted Decision-Making Strategies in Urban Settings with Limited Infrastructure: Guidance for Future Development</title>
      <link>https://trid.trb.org/View/2691667</link>
      <description><![CDATA[While bicycling is an essential mode of urban transportation, most parts of U.S. cities lack adequate infrastructure to keep bicyclists safe and allow them to travel efficiently. This research aims to model how psychological, street, and infrastructure characteristics influence bicyclist behavior in urban settings with inadequate bicycling infrastructure, such as in the Greater Houston area (Houston-The Woodlands-Sugar Land), Texas. This research will integrate quantitative and qualitative methods to develop a model that supports adaptive decision-making for bicycling in urban areas with limited infrastructure. The project will recruit 40 adult bicyclists to participate in surveys and bicycle simulator testing. A realistic urban network will be simulated in the bicycle simulator to replicate bicycling conditions under varying (infrastructure quality, traffic volume, visibility, etc.), psychological (risk perception, motivation, and attitudes, etc.), and operational (route choice, adaptation, and interaction with other modes of transportation, etc.) scenarios. Various techniques, including both qualitative and quantitative methods, can be used to identify key drivers of route choice and to develop optimal strategies for efficiency and safety. The findings will inform action-oriented urban planning and policy recommendations for enhancing bicycle infrastructure and safety. The outcomes have the potential to offer a replicable methodology for implementation in similarly challenged cities, providing active urban transportation and improved public health.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:34:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691667</guid>
    </item>
    <item>
      <title>Assessment of cyclists' cognitive workload through eye tracker and EEG sensors: Sensitivity to individual and external factors in a real-world experiment</title>
      <link>https://trid.trb.org/View/2680139</link>
      <description><![CDATA[This exploratory study aims to assess cyclists' mental workload by collecting psychophysiological data, including eye-tracking and electroencephalography (EEG). A multi-factor, real-world experiment was conducted to correlate psychophysiological measures with kinematic riding features, objects in the cyclist's field of vision, and the surrounding road and urban context. Additionally, the study investigates whether subjective ratings align with objective measures.  Participants first completed a pre-questionnaire capturing demographic information and cycling frequency. Then, they rode an instrumented bicycle equipped with GNSS/INS sensors in real traffic conditions while wearing eye-tracking glasses and an EEG headset. This setup tracked the bicycle path and recorded gaze behavior and brain activity. After completing the route, participants provided segment-level ratings of mental workload using the NASA-TLX questionnaire.  The ten participants who provided useful data indicated that cyclists could be grouped based on their recorded mental states and the visual patterns identified along the route. Due to the complexity of the correlations and the heterogeneity of the data, machine learning was applied to investigate the relevance of different features in the variability of cognitive sensor measures. The eye tracker and EEG measures revealed individual factors influencing mental workload levels and showed evidence of common and differentiated sensitivity to factors related to objects in the field of view, spatial context, and kinematic riding behavior. Similarly, various levels of correlation were found between subjective and objective data when measuring mental workload.  This real-world pilot study assessed mental workload using objective psychophysiological data collected via sensors, offering insights into interpreting visual patterns and EEG indicators to support the selection of the most appropriate measures for further studies. Future research can use the proposed experimental design and methodological framework to validate and extend the results to a larger population and road and traffic conditions.]]></description>
      <pubDate>Wed, 08 Apr 2026 15:32:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680139</guid>
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