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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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    <item>
      <title>Video-based Analysis of the Rideability and Safety of Dedicated Bicycle Lanes: Evidence from Fukuyama, Japan</title>
      <link>https://trid.trb.org/View/2669738</link>
      <description><![CDATA[To reduce conflicts and ensure safe and comfortable mobility for both pedestrians and cyclists, the development of dedicated bicycle lanes is crucial. However, compared to many European cities, Japanese roads are often narrower, making it challenging to allocate dedicated space for bicycles. Effectively prioritizing the installation of dedicated bicycle lanes requires a deep understanding of real-world street usage patterns. This study provides a quantitative assessment of how spatial separation, operationalized as the installation of dedicated bicycle lanes, affects cyclists’ safety and rideability, based on image analysis. The analysis utilizes camera data collected in Fukuyama City, focusing on dedicated bicycle lane before and after installation. The results indicate that while the introduction of dedicated lanes reduced the available space and subsequently decreased bicycle speeds (i.e., did not improve rideability), the physical separation between bicycle and pedestrian areas significantly reduced interactions between pedestrian and bicycle, leading to enhanced safety. Therefore, although no improvement in rideability was observed, the study suggests that the development of dedicated bicycle lanes has a positive impact on safety and supports their strategic implementation.]]></description>
      <pubDate>Tue, 12 May 2026 09:11:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669738</guid>
    </item>
    <item>
      <title>Shaping the future of cycling safety: A research agenda for the next two decades</title>
      <link>https://trid.trb.org/View/2669927</link>
      <description><![CDATA[The global shift toward sustainable transportation has raised the profile of cycling. Yet cycling safety still faces persistent challenges (e.g., fragmented governance, inequitable infrastructure, scarce research) that are often overshadowed by motorized transport agendas. This paper presents findings from a workshop held at the 12th International Cycling Safety Conference (ICSC2024) in Imabari, Japan, which brought together an interdisciplinary group of 31 experts (researchers, practitioners, and policymakers) to explore prospective research directions for cycling safety over the next two decades. Drawing on submitted abstracts, group dialogues, and post-event reflections, we used participatory methods, speculative exercises, and collaborative discussions to conduct a thematic analysis that organized key factors into five domains: society, policy, infrastructure, vehicles, and road users. This framework supports a long-term research agenda to address the interconnected challenges of cycling safety. Key priorities include: (i) behavioral and societal studies to make cycling safer and more appealing for diverse users; (ii) development of AI-enabled safety technologies; (iii) establishment of international infrastructure standards; and (iv) tools to anticipate risks linked to emerging vehicle technologies. Additional directions involve the use of eXtended Reality (XR) for behavioral research, multimodal integration, and the ethical and privacy dimensions of data collection. Practically, the findings highlight the importance of participatory and multidisciplinary approaches for tackling real-world safety issues and guiding future research.]]></description>
      <pubDate>Tue, 12 May 2026 09:11:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669927</guid>
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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>
    </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>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>Enhancing Pedestrian and Cyclist Safety in African Cities Through Living Labs: An Integrative Analysis</title>
      <link>https://trid.trb.org/View/2579546</link>
      <description><![CDATA[In many Sub-Saharan African cities, a substantial portion of the population relies on walking and cycling, particularly for access to public transport. However, the safety of pedestrians and cyclists is a significant challenge, with intersections and overpasses designed primarily for motorized vehicles. Pedestrians often lack recognition as legitimate road users, facing inadequate respect compared to motorized vehicles. Traditional mobility initiatives targeting local residents prove insufficient for engaging a diverse demographic, especially active mobility users. The research landscape on cyclists and pedestrians in Africa is underdeveloped, and conventional urban planning instruments are ill-suited for assessing their unique challenges. To address these issues, Living Labs are introduced to present and test scenarios for optimizing street space usage. These scenarios undergo rigorous evaluation for potential integration into urban environments. A multimodal approach is adopted, involving participatory crowd mapping and video analysis. A platform-independent crowd-mapping web app engages cyclists and pedestrians in real-time, providing insights into user behavior and preferences. Simultaneously, longitudinal video-based traffic conflict analysis quantitatively maps user behavior over time. It involves a systematic approach to observe and document instances of traffic conflicts and related occurrences pertaining to safety and operational aspects. This integrative approach within the Living Labs framework offers a comprehensive methodology for understanding, evaluating, and enhancing the mobility experiences of cyclists and pedestrians in African urban contexts. By combining participatory mapping tools and video analysis, this research methodology provides valuable insights into user behavior, contributing to the development of safer and more inclusive urban spaces for pedestrians and cyclists.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579546</guid>
    </item>
    <item>
      <title>Low Stress Cycling Route Network Design with Bicycle Accessibility Evaluation</title>
      <link>https://trid.trb.org/View/2579543</link>
      <description><![CDATA[The purpose of this case study is to create a bicycle infrastructure route evaluation method, based on street-level bicycle accessibility. The center of Athens, from Monastiraki to Gyzi, was defined as the evaluation area. Initially, data were collected for each road segment separately in the demarcated study area, from which cyclist's stress levels were extracted. Together with other suitable evaluation criteria, stress levels contributed to the final equation which calculated the “real length” of each route segment based on safety and speed of cyclists and through this procedure the appropriate roads for the Cycling Route Network were chosen. This evaluation method contributes to already known and used methodologies. The methodology allows automatization of bicycle network route choice through a software, or an application.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579543</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>Non-linear effects of built environment on cyclists’ perceived safety and comfort using online pairwise voting</title>
      <link>https://trid.trb.org/View/2651617</link>
      <description><![CDATA[Understanding how built environment (BE) features influence cyclists’ perceptions is critical for designing human-centric and bike-friendly spaces. While previous research has largely focused on objective cycling conditions, this study addresses a key gap by investigating the non-linear relationships between BE features and cyclists’ subjective perceptions of safety and comfort, through a novel integration of Street View Images (SVIs), computer vision (CV), and explainable machine learning (ML). Using a pairwise voting survey, 150 participants evaluated 300 SVIs, with the results extrapolated to 5,176 locations using CV and ML models. The Shapley Additive Explanations (SHAP) framework quantified threshold effects and highlighted asymmetries between perceived safety and comfort, revealing three key insights: (1) Over 80% of subjective perceptions are explained by spatial chaos, greenery, cycle lane type, feature entropy, perceived road width, and crowdedness, though their relative importance and thresholds vary; (2) Several BE features exhibit threshold effects, including notable inverted “V-shaped” trends, with safety demonstrating higher sensitivity and lower tolerance to BE features compared to comfort; (3) Comfort-oriented interventions are particularly effective in heritage areas where structural constraints limit safety improvements. These findings emphasize the need for localized and context-sensitive strategies that differentiate between safety-oriented and comfort-prioritized interventions, enabling more effective cyclist-friendly urban design.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651617</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>MASH Evaluation of Minnesota Department of Transportation Bicycle and Pedestrian Bridge Rail and Hybrid Bridge Rail</title>
      <link>https://trid.trb.org/View/2691797</link>
      <description><![CDATA[The Minnesota Department of Transportation (MnDOT) currently uses two bridge rail systems that are configured with a concrete bridge rail and a steel beam and post rail. The first is a concrete bridge rail with an attached bicycle and pedestrian rail and the second is a hybrid concrete bridge rail with a lower brush curb and an upper steel beam and post railing structure. Both bridge railings were previously developed and successfully crash tested under the safety criteria of NCHRP Report 350. MnDOT desired to evaluate these bridge railings to the current safety standards of AASHTO’s 𝘔𝘢𝘯𝘶𝘢𝘭 𝘧𝘰𝘳 𝘈𝘴𝘴𝘦𝘴𝘴𝘪𝘯𝘨 𝘚𝘢𝘧𝘦𝘵𝘺 𝘏𝘢𝘳𝘥𝘸𝘢𝘳𝘦 (MASH). The concrete railing with a bicycle and pedestrian rail was crash tested to MASH TL-3, and it satisfied all safety criteria for MASH test 3-11. The hybrid bridge railing was subjected to all three prescribed crash tests in the MASH TL-4 matrix. The two tests with passenger vehicles were conducted near the downstream end transition to a concrete end buttress to evaluate vehicle snag, while the single-unit truck test was conducted near the middle of the rail to evaluate the railing’s strength. The hybrid railing passed all safety performance criteria.]]></description>
      <pubDate>Tue, 14 Apr 2026 10:08:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691797</guid>
    </item>
    <item>
      <title>Exploring network scale separation strategies for car-bicycle integration</title>
      <link>https://trid.trb.org/View/2679119</link>
      <description><![CDATA[This study investigates strategies to mitigate car–bicycle conflicts in mixed traffic and their impacts on traffic speed and safety. It proposes and evaluates an approach that separates bicycles and cars onto different roads in a network. Various scenarios were compared with a baseline, accounting for traffic volume, modal share, and road hierarchy where bicycles and cars are separated. The performance of each scenario was evaluated from the perspectives of motorists and cyclists, considering car and bicycle efficiency across different trip lengths, as well as cycling stress levels assessed using the Level of Traffic Stress (LTS) score. The methodology involved estimating travel times using a traffic simulator and generating reachable areas for bicycles and cars. The study provides insights for designing multimodal transportation systems that consider both the benefits of shared road space and the potential advantages of separating bicycles and cars onto different roads. The main results are as follows: (1) Cars and bicycles show a trade-off relationship in transport efficiency in all network scenarios; the scenarios differ in the road hierarchy levels at which car and bicycle traffic are separated onto different roads; (2) Separating bicycles from cars on middle-class and local roads can upgrade the cycling environment, including efficiency and comfort, both on roads and at intersections; (3) To reconcile conflicts between motorized speed and cyclists’ comfort, enlarging high-hierarchy roads for car-dedicated use can be effective.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:07:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679119</guid>
    </item>
    <item>
      <title>Safety, identity, and inequity at the last mile: A qualitative study of app-based bicycle delivery riders in Spain</title>
      <link>https://trid.trb.org/View/2680148</link>
      <description><![CDATA[The expansion of the gig economy has led to a growing number of urban workers engaged in app-based food delivery. This sector, often seen as flexible, conceals complex occupational, legal, and psychosocial risks. Recent evidence suggests that delivery riders’ safety is shaped not only by infrastructure or individual behavior, but also by precarious work conditions, limited legal protections, and forms of social exclusion that remain largely unaddressed. This qualitative study examined how safety, identity, and equity are experienced and negotiated in app-based bicycle delivery in Spain, with attention to algorithmic timing, organizational rules, and street-level conditions. Twenty semi-structured interviews were conducted with food delivery riders (mostly migrant men) in urban areas of Spain. A reflexive thematic analysis (inductive) was applied, with attention to patterns, contrasts across cases, and speech insights suggesting broader socio-labor dynamics. Three core themes were identified: (1) persistent exposure to traffic and environmental hazards, often aggravated by digital pressures and limited enforcement of safety regulations; (2) a fragmented social identity, with riders feeling excluded from both formal labor structures and mainstream cycling culture; and (3) strong perceptions of systemic inequity, including legal precarity, economic fragility, and marginalization in public and policy narratives, which may influence how riders manage risk in practice (e.g., rule compliance, incident reporting) and, in turn, safety outcomes. The findings highlight the vulnerabilities of bicycle food delivery riders and suggest the need to rethink how safety, labor protections, and urban inclusion are framed and implemented in this sector.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680148</guid>
    </item>
    <item>
      <title>2024 FARS/CRSS Coding Manual for Pedestrian Bicyclist Crash Typing Guide for Coders Using the FARS/CRSS Ped/Bike Typing Tool</title>
      <link>https://trid.trb.org/View/2686626</link>
      <description><![CDATA[Countermeasures to prevent pedestrian and bicyclist crashes are often hindered by State crash files that contain  insufficient details about these crashes. To remedy this, Pedestrian and Bicycle Crash Typing was developed to  describe the pre-crash actions of the people and organizations involved to better define the sequence of events  and precipitating actions leading to crashes between motor vehicles and pedestrians or bicyclists. In 2010  NHTSA adopted parts of a stand-alone crash typing application called the Pedestrian and Bicycle Crash Analysis  Tool into its two records-based data collection systems, the Fatality Analysis Reporting System, and the National  Automotive Sampling System General Estimates System. In 2016 the Crash Report Sampling System replaced  the legacy NASS-GES. This is the coding manual for that system and provides information for the 2024 data  year.]]></description>
      <pubDate>Mon, 06 Apr 2026 10:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686626</guid>
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