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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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      <title>Boston Blind Zone Safety Initiative: Current Fleet Analysis, Market Scan, and Proposed Direct Vision Rating Framework</title>
      <link>https://trid.trb.org/View/2692319</link>
      <description><![CDATA[In about one-quarter of low-speed, truck-involved, vulnerable road user fatalities in the U.S., a driver’s direct vision was impaired. A driver has direct vision of an object outside the vehicle when it can be seen without the aid of mirrors or camera displays. Vehicles vary in how near drivers can see outside the vehicle to the front and to the side. This paper reports a first-in-the-U.S. effort with the U.S. Department of Transportation Volpe Center, the Boston Public Health Commission, and the Boston Transportation Department to assess the direct vision for vehicles used for Boston’s Schools, Fire, and Public Works Departments. The research team quantitatively measured direct vision in 21 vehicles using both a manual approach and a camera-based approach. Using these methods, this paper proposes a direct vision rating system that the City of Boston and other fleets can incorporate into procurement. The proposed system includes front and passenger five-star ratings based on the distance at which a child or adult would be visible directly in front of or to the passenger side of the vehicle, calibrated to federally and locally defined intersection geometric standards. A five-star vehicle enables drivers to see children in the crosswalk and children on bicycles in a buffered bicycle lane. In 11 of the 21 vehicles, drivers whose vehicle was stopped at the stop bar before a crosswalk at an intersection could not adequately see a child in the crosswalk in front or an adult on a bicycle on the side.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692319</guid>
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    <item>
      <title>A Computer Vision Approach to Evaluating Crosswalk Safety for Vulnerable Road Users</title>
      <link>https://trid.trb.org/View/2686215</link>
      <description><![CDATA[With advancements in computer vision and cloud computing, Surrogate Safety Measures (SSMs) now provide actionable insights to mitigate safety concerns before collisions occur. This study, conducted as part of the STREET21 research project and contributes to the existing body of knowledge by examining Post-Encroachment Time (PET), a time-based SSM, at a high-traffic urban intersection which many young vulnerable users (university students) cross as pedestrians for their daily commuting needs. In total 513 traffic conflict events were identified and mapped for the purposes of the analysis. The spatial analysis provides critical insights into the patterns of traffic conflicts. Results of the quantitative analysis demonstrate that pedestrian conflicts predominantly involved right-turning vehicles, followed by through vehicles, potentially indicative of red-light violations. The applied methodology underscores the efficacy of video analytics as a scalable alternative to traditional crash data analysis, enabling the evaluation of intersection designs and temporary treatments before permanent implementation.]]></description>
      <pubDate>Thu, 02 Apr 2026 13:51:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686215</guid>
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    <item>
      <title>To pass or not to pass: a field experiment on human values in interactions with automated vehicles</title>
      <link>https://trid.trb.org/View/2681207</link>
      <description><![CDATA[Vulnerable road users (VRUs), such as pedestrians and cyclists, are at high risk in road traffic, accounting for more than half of all global traffic fatalities. Ensuring safe interactions with highly automated vehicles (AVs) requires understanding and predicting VRUs' behaviour. This study investigated the relevance and predictive role of human values alongside environmental factors in real-world VRU-AV interactions. In a field experiment using a Wizard-of-Oz paradigm, 28 pedestrians and 29 cyclists interacted with an oncoming vehicle in a space-sharing scenario. Human values were assessed both qualitatively and quantitatively, while distance to the vehicle and driving mode (AV vs. manually driven) were manipulated. Results show that numerous human values (e.g. comprehensibility, legal compliance, self-efficacy, relaxedness) were rated as highly relevant, but only values related to relaxed interaction significantly predicted pedestrians' behaviour. Distance predicted interaction behaviour for VRU groups, whereas driving mode had no effect. Overall, the findings highlight the importance of considering both environmental factors and human values. The study demonstrates that values provide a broader perspective for understanding VRU behaviour and informing the design of safe, trustworthy, and acceptable VRU-AV interactions.]]></description>
      <pubDate>Fri, 27 Mar 2026 13:40:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681207</guid>
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    <item>
      <title>TripleA: An Unsupervised Domain Adaptation Framework for Nighttime VRU Detection</title>
      <link>https://trid.trb.org/View/2561881</link>
      <description><![CDATA[Detecting vulnerable road users (VRUs) at night presents significant challenges. Numerous methods rely heavily on annotations, yet the low visibility of nighttime images poses difficulties for labeling. To obviate the need for nighttime annotations, unsupervised domain adaptation manifests as a viable solution. However, existing approaches primarily focus on semantic-level domain gaps, often overlooking pixel-level discrepancies caused by inherent degradations in the nighttime domain. These degradations can impair machine vision and limit detection performance. In this paper, we propose TripleA, an unsupervised domain adaptation framework tailored for nighttime VRU detection. TripleA includes triple alignment. First, it aligns daytime and nighttime images to generate synthetic nighttime images, which are then enhanced for illumination and noise. To remove noise, we introduce an illumination difference-aware denoising network, incorporating a novel pseudo-supervised attention to achieve pixel-wise noise distribution alignment. This alignment is driven by pseudo-ground truth generated through a carefully designed exchange-recombination strategy, facilitating self-supervised training of the denoising network. Additionally, we introduce degradation alignment to ensure domain-invariant degradation encoding, which enhances the network’s robustness for real-world nighttime images. Extensive experiments demonstrate the effectiveness of our framework for nighttime VRU detection, all without the need for annotated nighttime data.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561881</guid>
    </item>
    <item>
      <title>Safety at Minor Intersections along Highways with Roadside Developments: A Case Study in Indian Context</title>
      <link>https://trid.trb.org/View/2659335</link>
      <description><![CDATA[The present paper reports an investigation on the safety deficiencies at minor investigation and identifying the crash potentials using surrogate safety measures on a typical Indian Highway having roadside developments. Intersections along the highways are one of the high risk locations for road crashes due to more number of conflicts. A safety audit of six representative minor intersections, both 3-legged and 4-legged, revealed that there are significant deficiencies at those locations. Crash potentials are estimated in terms of Post Encroachment Time for cross and rear-end collisions from manual and videography traffic survey data, which indicated that the number of critical conflicts are significantly high particularly for Vulnerable Road Users. Also more number of critical conflicts occur when major road vehicle is a 2-Wheeler. This necessitates the adoption of suitable safety treatments at/near the intersections. The methodology is beneficial in assessing the crash potentials of other intersections in developing countries context.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659335</guid>
    </item>
    <item>
      <title>Harnessing household travel survey with smart card data to generate spatiotemporally-diverse activity schedules for transit users</title>
      <link>https://trid.trb.org/View/2651540</link>
      <description><![CDATA[Current activity-based models (ABMs) rely on household travel survey (HTS) data to generate daily activity schedules for transit users. However, HTS suffers from limited sampling, resulting in low spatiotemporal diversity. Smart card (SC) data offer broader transit coverage but lack sociodemographic, non-transit trips, and trip-level details, making integration with HTS challenging. This study introduces a novel two-stage data fusion framework that combines detailed but sparse HTS data with high-coverage SC data to generate complete, diverse, and up-to-date activity schedules for transit users. In Stage 1, the framework learns a latent class structure to align the spatiotemporal characteristics of transit trips across datasets and estimates a fused joint distribution over all attributes except the spatiotemporal details of non-transit trips. Stage 2 imputes these missing spatiotemporal details to complete full trip chains. A key innovation is the construction of a latent space with optimal complexity that preserves key statistical properties while enhancing the diversity of synthesized activity patterns. The framework ensures scalability by decomposing the fusion task into analytically tractable sub-problems. The model properties are first validated in a controlled experiment. Further validation using data from 3.4 million SC users in Seoul, South Korea, shows that the fused population closely aligns with external cellular signaling data and significantly outperforms HTS alone – generating up to 2.92 million unique synthetic schedules (an 82.8 ×  increase over HTS). In sum, the proposed method lays the groundwork for integrating diverse data sources into ABMs, enhancing their ability to generate diverse synthetic mobility patterns, including underrepresented segments.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651540</guid>
    </item>
    <item>
      <title>Guide for Road Safety Assessments for Urban Railway Crossings and Stations</title>
      <link>https://trid.trb.org/View/2679448</link>
      <description><![CDATA[This guide explains that safety is not just about the trains themselves, but also about how people get to and from stations. Problems often come from poor sidewalks, missing sign age, bad lighting, and busy roads near stations. Children, older adults, and people with disabilities are especially at risk. In some locations, crossings lack proper barriers or signals, increasing the chance of road crashes. Reliable data on crashes is also often missing, making it difficult to pinpoint where the greatest problems lie. The guide presents a step-by-step approach to improving safety around urban railway stations and crossings. It starts by defining which parts of the city are most affected by the station/crossing (typically the immediate surroundings), the places people walk or cycle from, and the wider area served by buses or other transport. Using maps and data, cities can identify high-risk spots such as poorly lit intersections or busy crossings. Then, teams should conduct on-site inspections, checking for obstacles, unsafe crossings, and places where people might feel unsafe, especially at night. Based on these findings, local authorities can implement practical improvements: better lighting, clearer crossings, safer sidewalks, ramps for accessibility, and more organized traffic and parking. The guide also shares examples from cities that have made improvements.]]></description>
      <pubDate>Mon, 23 Mar 2026 08:34:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679448</guid>
    </item>
    <item>
      <title>Using Connected Intelligent Transportation to Enhance Vulnerable Road User Safety: Data Management Plan</title>
      <link>https://trid.trb.org/View/2679067</link>
      <description><![CDATA[This project, titled "Pedestrian and Driver Response Analysis in Virtual Reality Environments" (VRU Project), focuses on collecting data through radar detection of pedestrians and video footage of pedestrian reactions in virtual reality (VR) environments. This project utilizes radar and camera technologies to understand pedestrian and driver behaviors in VRU scenarios. The goal is to analyze how different alert modes in VR environments affect pedestrian and driver responses, with a special emphasis on understanding their behavior in relation to autonomous vehicles. The data collected will be both objective, from radar measurements, and subjective, from video analysis. The nature of the data will be predominantly qualitative, captured through video, and quantitative, through radar data analysis.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:38:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679067</guid>
    </item>
    <item>
      <title>Using Connected Intelligent Transportation to Enhance Vulnerable Road User Safety</title>
      <link>https://trid.trb.org/View/2679068</link>
      <description><![CDATA[This project leverages connected intelligent transportation technologies to enhance the safety of vulnerable road users (VRUs) within evolving urban road systems. This project targets three fundamental barriers: the lack of high-fidelity experimental platforms, localization failures in Global Navigation Satellite System (GNSS)-denied areas, and insufficient trajectory prediction for heterogeneous agents. Our goal is to develop an integrated cooperative system that combines virtual reality, wireless communication, and physics-informed learning. To achieve this, we develop Sky-Drive, a distributed multi-agent simulation platform that enables human-in-the-loop interaction testing. Furthermore, the project implements a cellular Vehicle-to-Everything (C-V2X)-based cooperative localization framework to ensure lane-level accuracy and a kinematics-aware multigraph attention network for precise motion forecasting. The output of this project provides a comprehensive technological foundation for enabling the safe and equitable coexistence of autonomous vehicles (AVs) and VRUs in mixed traffic environments.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:38:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679068</guid>
    </item>
    <item>
      <title>Behavioral Traffic Safety Challenges: Counter Measure Report (ADA Version)</title>
      <link>https://trid.trb.org/View/2681398</link>
      <description><![CDATA[Improving safety for people walking and bicycling requires more than infrastructure alone. It calls for a cultural shift toward shared responsibility, supported by the Safe System Approach and Minnesota’s Toward Zero Deaths framework. The tool was modified to develop an ADA version. The content in the tool helps agencies begin to build the foundation necessary to influence a culture of safety when planning pedestrian and bicycle infrastructure. The twelve strategies included as a part of the decision tree were chosen by the Technical Advisory Panel as potential strategies for consideration, not an exhaustive list, and should be applied using sound engineering judgment.]]></description>
      <pubDate>Thu, 19 Mar 2026 08:56:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681398</guid>
    </item>
    <item>
      <title>Virtual reality simulations on pedestrians’ perceived risk in interactions with cyclists</title>
      <link>https://trid.trb.org/View/2666954</link>
      <description><![CDATA[With the growing popularity of cycling and walking as modes of transportation, pedestrians and cyclists are often considered vulnerable road users due to their lack of safety and the risk of injuries in collisions with other road users or each other. Understanding pedestrian safety perceptions in mixed-use urban environments is essential for developing safer shared infrastructure. This study analyzes pedestrian risk perception in three cyclist–pedestrian interaction scenario: a blind corner, a crosswalk, and a shared path. Unlike prior studies that focus on pedestrian-vehicle interactions, this study examines perceived risk in cyclist-pedestrian environments using both virtual reality (VR) and matched real-world video conditions. A total of 42 participants experienced all scenarios and provided subjective risk ratings. Results from paired sample t-tests revealed no significant differences in perceived risk between VR and video conditions for all scenarios (p-value > 0.05). However, shared path conditions showed the highest perceived risk, underlining the need for infrastructure designs that reduce user conflicts, such as physical separation. The study extends the validated use of VR to cyclist–pedestrian conflict scenarios within pedestrian safety research and underscores the importance of designing shared facilities that accommodate the needs of all non-motorized users.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666954</guid>
    </item>
    <item>
      <title>Language matters: an experimental study of language patterns' effects on traffic safety perceptions in Germany</title>
      <link>https://trid.trb.org/View/2679029</link>
      <description><![CDATA[Traffic crashes claim over 1.19 million lives globally each year, yet public support for proven safety measures remains limited. Research suggests that media language patterns may influence public perceptions of traffic violence and policy preferences. This study replicates Goddard et al.'s (2019) experimental design study in the German context, examining how editorial patterns in crash reporting affect responsibility attribution, penalty preferences, and policy support. Using a randomized controlled experiment (N = 1,537), participants read one of three versions of a fictitious news article: status-quo language reflecting common German reporting patterns, agent-focused language avoiding victim-blaming formulations, or agent-focused plus contextual information. Results show that shifting from victim-focused to agent-focused language substantially reduced pedestrian responsibility attribution (from 48.9% to 44.4%) and increased responsibility attributed to the driver (from 43.5% to 48.1%). Adding contextual information enlarged these effects, with driver responsibility attribution reaching 54.8% and pedestrian responsibility attribution dropping to 33.2%. Contextual framing also increased support for structural interventions and reduced support for campaigns appealing to individual behavior. These findings confirm that language patterns in German road traffic collision reporting—including metonymy, passive voice, reflexive verbs, and the lack of context information—systematically shift perceived responsibility toward vulnerable road users. The study demonstrates that more precise language in traffic reporting can increase public support for evidence-based safety policies, suggesting an ethical imperative for improved editorial practices.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679029</guid>
    </item>
    <item>
      <title>Welfare-optimal public transport fares in Denmark</title>
      <link>https://trid.trb.org/View/2635396</link>
      <description><![CDATA[Public transport fare policies are important in shaping efficient, equitable, and sustainable transportation systems. In this study, we optimise a welfare function across a spectrum of flat and distance-based pricing strategies while also analysing the distributional impacts and effects on equity. The analysis reveals that a Danish distance-based fare system replicating the current prices is sub-optimal from both a welfare economic and equity perspective. Using a comprehensive demand model for weekday trips (under 50km) in Denmark, we demonstrate that a flat fare of 24.2 DKK ( ∼  € 3.24), would improve welfare and equity without causing significant operational changes. Effects are driven by consumer surplus gains and increasing market shares of active modes for shorter distances and public transport for longer distances. These mobility changes yield significant health benefits for the population and reduce environmental costs. Nevertheless, the socio-spatial distribution of welfare effects is uneven, with most gains observed in rural and suburban areas and across younger and lower-income groups.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635396</guid>
    </item>
    <item>
      <title>Machine learning-based real-time crash risk forecasting for pedestrians</title>
      <link>https://trid.trb.org/View/2632164</link>
      <description><![CDATA[Recent developments in artificial intelligence (AI) have made significant improvements in understanding and enhancing pedestrian safety—a vulnerable road user group that receives less attention than motorized road users do. Specifically, AI-based video analytics have provided insight into facilitating real-time safety at signalized intersections. However, past studies have not fully realized the essence of real-time analysis, which underpins forecasting pedestrian collision likelihood by analyzing how past extreme events influence future risk over sequential intervals. To this end, we combine extreme value theory and machine learning models for real-time pedestrian collision risk forecasting. Traffic conflicts and their associated variables were identified from 288 ​h of video footage obtained from three signalized intersections in Queensland, Australia, via computer vision techniques, including YOLO and DeepSORT, to obtain the post encroachment time for vehicle‒pedestrian interactions. A Bayesian non-stationary peak over threshold (POT) is developed to obtain real-time pedestrian crash risk at the signal cycle level. The performance of the POT model is compared with observed crashes, and the results demonstrate the reasonable accuracy of the model. The estimated pedestrian crash risk at each signal cycle forms contiguous univariate time series data (which serve as ground truth), which are used as input to develop time series machine learning models (recurrent neural networks (RNNs) and long short-term memory (LSTM)). Both of these models forecast pedestrian crash risk, with the RNN model outperforming the competing model and demonstrating that pedestrian crash risk can be reliably estimated 30−33 ​min in advance.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632164</guid>
    </item>
    <item>
      <title>Beyond the Data: A Holistic Approach to Serving Communities</title>
      <link>https://trid.trb.org/View/2663653</link>
      <description><![CDATA[This article explores how blending qualitative and quantitative data can transform transportation decision making, drawing from two case studies: a countywide roundabout prioritization effort in Ohio and a statewide vulnerable road user (VRU) active transportation analysis in West Virginia. Together, these examples demonstrate how transportation professionals can move beyond numbers to build infrastructure that is data-driven, people-centered, and built to last. In today’s transportation landscape, where public trust and funding constraints are constant challenges, integrating human narratives into technical processes is not optional—it is foundational. These projects demonstrate that data-driven methods can also be people-centered, transparent, and resilient. The lesson is clear: transportation professionals must move beyond the comfort of hard numbers and embrace the full spectrum of evidence to build infrastructure that communities trust and support.]]></description>
      <pubDate>Thu, 26 Feb 2026 09:22:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663653</guid>
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