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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>Risk assessment of traffic violations in autonomous vehicles considering driving behavior</title>
      <link>https://trid.trb.org/View/2663296</link>
      <description><![CDATA[The advent of autonomous vehicles brings profound changes to transportation while introducing new safety challenges, especially regarding traffic regulation compliance. This paper proposes a novel comprehensive risk assessment method considering driving behaviors to quantify traffic violation risks posed by autonomous systems. Grounded in Chinese traffic regulations, the method analyzes and defines key indicators of autonomous driving behavior and classifies risks into real-time and cumulative categories. By incorporating environmental safety entropy, a comprehensive risk profile is produced. Through simulation analysis using specific scenarios and datasets, combined with validation through on-road tests conducted with autonomous vehicles, the findings affirm the efficacy and practicality of this approach in quantifying the risks associated with autonomous driving. This research offers a scientific framework for quantifying risks and informing policy related to autonomous driving. It also aids in optimizing safety and reducing violations, enhancing the reliability of autonomous vehicle technology.]]></description>
      <pubDate>Thu, 14 May 2026 17:04:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663296</guid>
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    <item>
      <title>Surface Parking Lots in Downtown Areas and the Role of Regulatory Delay in Optimal Dynamic Land Use*</title>
      <link>https://trid.trb.org/View/2659429</link>
      <description><![CDATA[In this paper, we explore the implication for urban form, urban structure and optimal land use policy of vacant land used for downtown surface parking lots in urban areas. We develop a dynamic, spatial general equilibrium urban model to show cases where vacant land can be optimal and suboptimal depending upon its temporary use, economic and regulatory conditions as well as externalities. We show in numerical simulations how the structure of the urban economy responds to different policies and consider their implications for different types of cities. These results have important implications for cities concerned about the impacts of vacant land and in particular of surface parking lots in downtown areas.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659429</guid>
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    <item>
      <title>The causal effects of removing Hook-Turn regulation on road safety</title>
      <link>https://trid.trb.org/View/2651547</link>
      <description><![CDATA[Traffic safety and related policy interventions have garnered increasing attention from both scholars and policymakers. While prior research has primarily focused on the role of infrastructure improvements in mitigating traffic accident risks, relatively little attention has been given to the impact of hook-turn (HT) traffic regulations. HT is a specialized traffic regulation implemented at intersections, requiring vehicles to proceed to the far side of the cross street and wait for the green light in the intersecting direction before completing their turn. This regulation has been adopted in countries or regions such as Japan, Australia, and Taiwan. This paper empirically examines the causal effects of HT regulation on traffic accidents by exploiting a policy reform in Tainan City, Taiwan, where HT was removed in some townships. Using this policy change as a quasi-natural experiment, we apply a difference-in-discontinuity design to estimate its impact on traffic accident outcomes, including the number of accidents, the number of victims, and the number of vehicles involved. Our findings indicate that the policy reform led to a 21% reduction in accident cases and a 19% decrease in the number of victims. The reduction is primarily driven by injury-related rather than fatal incidents. Additionally, the total number of vehicles involved in accidents declined by 28%, with larger reductions observed for motorcycles (–26%) than for automobiles (–18%). A back-of-the-envelope calculation suggests that the policy resulted in a 4.7% decrease in total medical expenditures among residents in the treated areas during the study period.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651547</guid>
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    <item>
      <title>Slow Down, Move Over Laws: National Survey of Drivers’ Knowledge, Attitudes, and Behaviors, 2025</title>
      <link>https://trid.trb.org/View/2686242</link>
      <description><![CDATA[More than 2,100 people were struck and killed while stranded or working on the roadside in 2019–2023. Included among them were drivers and passengers who exited disabled vehicles, as well as roadside assistance providers, law enforcement officers, firefighters, emergency medical services providers, and “Good Samaritans” attempting to help them. Slow Down, Move Over (SDMO) laws seek to protect these vulnerable road users. These laws generally require drivers in the lane adjacent to a roadside worker or disabled vehicle to move to a different lane if possible and/or reduce their speed. While details vary by state, every U.S. state has some form of SDMO law. This research seeks to measure drivers’ knowledge of and attitudes toward SDMO laws, as well as self-reported driving behavior in relevant situations, in a large, nationally representative survey of drivers.]]></description>
      <pubDate>Thu, 09 Apr 2026 13:41:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686242</guid>
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      <title>Fatality Analysis Reporting System Analytical User’s Manual, 1975-2024</title>
      <link>https://trid.trb.org/View/2686624</link>
      <description><![CDATA[The Fatality Analysis Reporting System (FARS) is a census of fatal motor vehicle crashes with a set of data files documenting all qualifying fatalities that occurred within the 50 States, the District of Columbia, and Puerto Rico since 1975. Providing data about fatal crashes  involving all types of vehicles, the FARS is used to identify highway safety problem areas,  provide a basis for regulatory and consumer information initiatives, and form the basis for cost  and benefit analyses of highway safety initiatives. To qualify as a FARS case, the crash had to involve a motor vehicle traveling on a trafficway customarily open to the public and must have resulted in the death of a motorist or a non-motorist within 30 days of the crash. This multi-year analytical user’s manual provides documentation on the historical coding practices of FARS from 1975 to 2024. In other words, this manual presents the evolution of  FARS coding from inception through present. It includes the data elements that are contained in  FARS and other useful information that will enable the users to become familiar with the data  system. The FARS/NASS GES and FARS/CRSS Coding and Validation Manuals provide more  detailed definitions for each data element and attribute for a given year. NHTSA’s National  Center for Statistics and Analysis (NCSA) publishes these manuals for each year of data  collection, and they are available at NCSA Publications — Manuals and Documentation — FARS. The compilation of FARS data for more than four decades has been a NHTSA priority. These  data store valuable information that have been preserved over time and are available for present  and future use. This analytical user’s manual should help improve the usefulness and  accessibility of the FARS data. With the exception of personal notes, there is no reason to keep  older versions of this reference manual. All information in earlier editions has been retained in  this newer version. This is the updated and revised Fatality Analysis Reporting System Analytical User’s Manual for the period 1975 to 2024.]]></description>
      <pubDate>Mon, 06 Apr 2026 10:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686624</guid>
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    <item>
      <title>Decision Making in Urban Traffic: A Game Theoretic Approach for Autonomous Vehicles Adhering to Traffic Rules</title>
      <link>https://trid.trb.org/View/2561880</link>
      <description><![CDATA[One of the primary challenges in urban autonomous vehicle decision-making and planning lies in effectively managing intricate interactions with diverse traffic participants characterized by unpredictable movement patterns. Additionally, interpreting and adhering to traffic regulations within rapidly evolving traffic scenarios pose significant hurdles. This paper proposed a rule-based autonomous vehicle decision-making and planning framework which extracts right-of-way from traffic rules to generate behavioural parameters, integrating them to effectively adhere to and navigate through traffic regulations. The framework considers the strong interaction between traffic participants mathematically by formulating the decision-making and planning problem into a differential game. By finding the Nash equilibrium of the problem, the autonomous vehicle is able to find optimal decisions. The proposed framework was tested under simulation as well as full-size vehicle platform, the results show that the ego vehicle is able to safely interact with surrounding traffic participants while adhering to traffic rules.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561880</guid>
    </item>
    <item>
      <title>Risky driving habits among Tunisia's informal transport drivers: What can the theory of planned behavior tell us?</title>
      <link>https://trid.trb.org/View/2666644</link>
      <description><![CDATA[The purpose of this study was to investigate the risky driving behavior of informal transport drivers in Tunisia using the Theory of Planned Behavior. A self-report questionnaire was administered to 315 participants from five Tunisian governorates. The study results indicated that three different types of behavior (bad, negligent and intentional behaviors) constitute risky driving behaviors among informal drivers. The informal culture, subjective norms, risky driving attitudes and perceived behavioral control were found to be important factors explaining risky driving behaviors. The findings showed that the informality culture trivializes and facilitates these risky driving behaviors. The implications of this study suggest that it is necessary to consider the informal transport sector when developing policies and regulations related to road safety. In addition, it is important to understand the cultural and social norms of informal transport drivers to effectively reduce risky driving behaviors. Furthermore, safety campaigns and educational initiatives should aim to reduce risky driving behavior among informal transport drivers. Finally, the results of this study can be used to inform future research and policy development in the field of road safety.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666644</guid>
    </item>
    <item>
      <title>Analyzing the relationship between on-street parking demand and illegal parking</title>
      <link>https://trid.trb.org/View/2663846</link>
      <description><![CDATA[Promoting road safety in urban environments requires an understanding of the factors that create hazardous road conditions. Illegal parking can cause a wide range of safety concerns, from blocking driver sightlines at crosswalks to obstructing bike lanes causing cyclists to enter vehicle traffic. Some of the causes of illegal parking have been studied before but there appears to be a gap in the literature regarding the influence of different on-street parking occupancy levels on illegal parking activity.  The relationship between on-street parking location occupancy and illegal parking is examined in this paper using two data sources. First, on-street parking payment transaction data for locations managed by the Toronto Parking Authority is used to determine parking location occupancy. Second, parking infraction data from the City of Toronto is used to examine the prevalence of illegal parking activity.  The study starts with identifying the five streets of each road type (arterial, collector, and local) within downtown Toronto that have the most parking infractions. The locations are selected only from downtown Toronto to help control for external factors (e.g., built environment). Next, parking infractions that occurred adjacent to paid on-street parking and that have potential safety implications are selected for further analysis. The parking infractions are then paired with the occupancy percentage of the adjacent paid on-street parking location at the time the infraction occurred. Finally, frequency distribution graphs are produced comparing the number of parking infractions to the adjacent parking location occupancy percentages. Some modifications are made to the frequency distribution graphs to control for certain factors (e.g., length of road). Linear and exponential regression models are produced to determine the trends within the frequency distribution graphs.  Overall, there is a positive relationship between parking location occupancy and illegal parking activity that may cause road safety concerns. For different times of the week, weekday mornings, afternoons, and evenings are analyzed but no weekend times were examined due to minimal data. All three times periods showed statistically significant relationships and parking location occupancy had a stronger effect on illegal parking activity in the morning. For the three types of road classification examined within the study (arterial, collector, and local), only local roads showed a statistically significant relationship. Finally, loading areas are shown to lower illegal parking activity.  The results show that there is a positive effect on road safety as on-street parking location occupancy decreases. This emphasizes the need for illegal parking to be managed through stringent enforcement and the creation of loading areas.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663846</guid>
    </item>
    <item>
      <title>Safe roads for all road users – insights from the German “bicycle capital”</title>
      <link>https://trid.trb.org/View/2663373</link>
      <description><![CDATA[In previous years, the traffic accident statistics of the state of North Rhine-Westphalia repeatedly showed a very poor accident record for Münster. This led to an urgent and comprehensive need for action at various levels. To achieve improvements as quickly and efficiently as possible, several analyses and surveys have been conducted to improve road safety. The knowledge gained from this has been continuously incorporated into the ongoing work to improve road safety. In 2007, a “Traffic Accident Prevention” regulatory partnership was therefore established with the aim of developing joint strategies and measures to improve road safety. Its building blocks include the four fields of action “Monitoring and punishment”, “Construction and traffic engineering”, “Traffic education and road safety advice” and “Public relations”, which are all closely interlinked. Since then, reactive and preventive measures have been used to make Münster safer for all road users. The city of Münster has thus been carrying out holistic / integrative road safety work at various levels for many years, which has been evaluated several times. The road safety program is now well established and has become an integral part of current practice. Increasing and changing mobility and the growing city are constantly presenting road safety work with new challenges. If we now consider the requirements of climate protection, we can see that the traffic-safe city of tomorrow should be a bicycle and pedestrian-friendly infrastructure in particular. This is why the decision was made in 2020 to realign road safety work. This contribution provides an overview of the past, present and future of road safety work for a safer city of Münster for everyone.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663373</guid>
    </item>
    <item>
      <title>Navigating the Rules: Integrating TD3 and Sensor Fusion for Traffic-Aware Autonomous Vehicle Path Planning</title>
      <link>https://trid.trb.org/View/2642285</link>
      <description><![CDATA[This work presents a novel algorithm for local path planning for autonomous vehicles (AVs), prioritizing safety and adherence to traffic regulations. The algorithm integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) with sensor fusion based on Nvidia Convolutional Neural Network (NCNN). Using the CARLA simulator and real-world datasets like KITTI and WAYMO, the proposed algorithm leverages Imitation Learning (IL) and Deep Reinforcement Learning (DRL). IL uses human driving data for rule-abiding behavior, while DRL refines decisions through real-time interaction. This integrated approach overcomes the limitations of individual techniques, such as the lack of generalizability in Supervised Learning (SL) methods. CARLA simulations show 100% route completion, zero collisions, and perfect traffic rules adherence. Real-world tests confirmed its ability to generalize from simulations to real driving conditions. Sensor fusion mitigates individual sensor weaknesses, enhancing safety and efficiency.]]></description>
      <pubDate>Wed, 18 Feb 2026 08:51:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642285</guid>
    </item>
    <item>
      <title>The epidemiology of electric scooter-related injuries in Malaga: Effects of shifting from sidewalks to streets under new traffic regulations</title>
      <link>https://trid.trb.org/View/2630640</link>
      <description><![CDATA[To analyze injuries associated with electric scooter use in Malaga and assess the impact of recent traffic regulations, specifically the shift of electric scooters from sidewalks to streets with a speed limit of 25 km/h. Retrospective descriptive study of patients attending the emergency department of the Regional University Hospital of Malaga due to electric scooter injuries between January 2018 and December 2022. The study cohort was divided into pre- and post-regulation periods, using January 2021 as the index date. A total of 404 patients were included. Most injuries affected the upper extremities, head and neck, face, and lower extremities. After the regulation, pedestrian collisions decreased (10.9 % → 5.2 %; p = 0.039), while collisions with cars increased (8.8 % → 18 %; p = 0.016). Injury severity and fracture locations remained similar before and after the regulation. Alcohol consumption and nighttime riding were associated with higher odds of moderate to severe injuries. The number of electric scooter-related accidents has increased over the years. Shifting electric scooters from sidewalks to streets reduces pedestrian collisions but increases exposure to motor vehicles, highlighting a key policy trade-off. Most injuries involve the head and facial bones, supporting helmet use. These findings provide evidence to inform future preventive measures and regulatory strategies.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:07:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630640</guid>
    </item>
    <item>
      <title>RuleNet: rule-priority-aware multi-agent trajectory prediction in ambiguous traffic scenarios</title>
      <link>https://trid.trb.org/View/2597092</link>
      <description><![CDATA[Accurately predicting surrounding traffic participants’ intentions and future trajectories is essential for automated vehicles in interactive scenarios. These interactions often involve diverse semantic interpretations embedded within different traffic rules. Accordingly, learning the priority characteristics of traffic rules offers a promising pathway to improving prediction performance. However, traffic rules are frequently ambiguous and are overlooked by trajectory prediction models. To address this issue, this paper introduces RuleNet, a multi-agent trajectory prediction framework that incorporates the priority evaluation of ambiguous traffic rules. RuleNet consists of three primary components. First, built upon Graph Neural Networks, it extracts agent kinematics, road topology, and traffic rule representations. Next, a multi-attention mechanism is employed to model interactions among agents, between historical and predicted trajectories, and across different prediction modes, thereby generating initial trajectory proposals. Finally, a rule-guided refinement module is introduced to adjust the predictions in accordance with learned rule priorities. This study focuses on two key traffic rule categories: safety and right-of-way, which are quantified using time to collision and relative distance, depending on the interaction type. Rule priorities are calculated through Signal Temporal Logic robustness measures and integrated into the prediction refinement process. Comprehensive experiments on the INTERACTION dataset validate the effectiveness of RuleNet. Results show that it outperforms existing baselines, achieving a 1.8–5.1% increase in prediction accuracy and a 16.8% improvement in safety. Furthermore, ablation studies are conducted to examine the influence of individual rule types and fusion strategies on model performance. The findings highlight three main findings: 1) Distance-based rules considerably improve prediction accuracy in ambiguous intersection scenarios. 2) Temporal rules are more influential in interactions involving vulnerable road users than in vehicle-to-vehicle cases. 3) Integrating rule priorities into both feature extraction and attention mechanisms yields the best overall performance.]]></description>
      <pubDate>Mon, 24 Nov 2025 15:30:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597092</guid>
    </item>
    <item>
      <title>Living on the highway? Addressing highway infrastructure potential through object detection</title>
      <link>https://trid.trb.org/View/2606861</link>
      <description><![CDATA[The rapid increasing demand for freight transport has precipitated a critical need for expanded highway infrastructure, including Heavy Goods Vehicle (HGV) parking spaces. Utilizing state-of-the-art object detection techniques in satellite imagery, we conduct a comprehensive analysis to assess the current availability of HGV parking facilities along German highways. We relate our results to HGV traffic volume data. Our findings reveal local disparities in infrastructure supply and demand. In a next step, we conduct location analysis to determine regions impacted the most by identified undersupply. Our results deliver valuable insights to both, specialized real estate developers and policymakers likewise.]]></description>
      <pubDate>Tue, 11 Nov 2025 09:25:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606861</guid>
    </item>
    <item>
      <title>Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea</title>
      <link>https://trid.trb.org/View/2591752</link>
      <description><![CDATA[For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal documents formulated in natural language. Temporal logic is a suitable concept to formalize such traffic rules. Still, temporal logic rules often result in constraints that are hard to solve using optimization-based motion planners. Reinforcement learning (RL) is a promising method to find motion plans for autonomous vehicles. However, vanilla RL algorithms are based on random exploration and do not automatically comply with traffic rules. Our approach accomplishes guaranteed rule-compliance by integrating temporal logic specifications into RL. Specifically, we consider the application of vessels on the open sea, which must adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). To efficiently synthesize rule-compliant actions, we combine predicates based on set-based prediction with a statechart representing our formalized rules and their priorities. Action masking then restricts the RL agent to this set of verified rule-compliant actions. In numerical evaluations on critical maritime traffic situations, our agent always complies with the formalized legal rules and never collides while achieving a high goal-reaching rate during training and deployment. In contrast, vanilla and traffic rule-informed RL agents frequently violate traffic rules and collide even after training.]]></description>
      <pubDate>Wed, 29 Oct 2025 13:36:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591752</guid>
    </item>
    <item>
      <title>A Comprehensive Survey on Driving Compliance Assessment Methodologies for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2591344</link>
      <description><![CDATA[Autonomous vehicles (AVs) have emerged as a focal point in contemporary technological research, promising significant enhancements in safety and efficiency for transportation systems and heralding a fundamental transformation in future mobility. Despite notable advancements in autonomous driving technology, the full-scale mass production and integration of AVs into transportation networks face substantial challenges, with driving compliance assessment (DCA) of driving behavior being a critical barrier. Compliance assessment encompasses multiple dimensions: from meeting basic safety standards and adhering to traffic regulations to implementing advanced driving protocols. This article systematically elaborates and compares methodologies at various levels of DCA, highlighting the importance and complexity of compliance evaluations in the development of AVs. The survey reveals deficiencies in the existing compliance assessment frameworks for AVs, particularly highlighting their lack of systematic and adaptive capabilities to effectively respond to new scenarios, demands, and technologies. It proposes potential research directions to address these gaps. Through a comprehensive examination of these methodologies, the article aims to provide theoretical and evidence-based guidance for the safe integration of AVs into both current and future transportation systems.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591344</guid>
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