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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>Assessing the Static and Dynamic Safety Performance of Heavy Vehicle Drivers Using Slacks-Based Measure Super-Efficiency Data Envelopment Analysis and the Malmquist Index</title>
      <link>https://trid.trb.org/View/2784761</link>
      <description><![CDATA[Road freight transportation is essential for social production, commodity circulation, and online consumption, offering flexibility and efficiency. However, traffic accidents and fatalities involving heavy vehicles have been high, mainly as a result of driver error. This research establishes a framework for evaluating the static and dynamic safety efficiency of heavy vehicle drivers, utilizing data envelopment analysis and the Malmquist index. First, the drivers’ aggressive behavior, abnormal status, and risky maneuver data were collected to construct a risk indicator database. Second, the static efficiency of drivers is evaluated using data envelopment analysis to obtain individual efficiency scores and rankings, and the improvement direction for each individual is determined based on the reference set. Furthermore, the model’s accuracy and stability were verified through one-way ANOVA and ranked difference tests. After that, the Malmquist index was used to analyze the dynamic efficiency of individuals and to examine the changing trend in individual efficiency over time. Finally, the static and dynamic efficiencies were combined to further analyze overall driving performance, and targeted improvement measures were implemented for different drivers. The study provides a comprehensive evaluation of drivers’ static and dynamic efficiency, offering targeted improvement measures and supporting freight management platforms.]]></description>
      <pubDate>Mon, 05 Oct 2026 08:57:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784761</guid>
    </item>
    <item>
      <title>Analyzing Residential Speeding Using Connected Vehicle Data: A Case Study in the Charlottesville, VA Area</title>
      <link>https://trid.trb.org/View/2775486</link>
      <description><![CDATA[This study uses connected vehicle data to analyze speeding behavior on residential roads. A scalable pipeline processes trajectory data and supplements missing speed limits to generate summaries at OpenStreetMap's way ID level. The findings reveal a highly skewed distribution of both aggressive and reckless speeding. Based on a case study of Charlottesville, Virginia's connected vehicle data on residential roads, we found that 38% of segments had at least one instance of aggressive speeding, and 20% had at least one instance of reckless speeding. In addition, the point-based nighttime speeding rates were higher than daytime rates for the vast majority of residential segments, and extreme violations on specific road segments highlight how severe the issue can be. Several segments rank among the top 10 for both aggressive and reckless speeding incidents, indicating particularly high-risk residential roads. These findings support the need for both spatial and behavioral interventions. This analysis provides a rich foundation for policy and planning, offering a valuable complement to traditional enforcement and planning tools. In conclusion, this framework sets the foundation for future applications in traffic safety analytics, demonstrating the growing potential of telematics data to inform safer, more livable communities.]]></description>
      <pubDate>Thu, 24 Sep 2026 17:08:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775486</guid>
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    <item>
      <title>Regulating “Unruly Passengers” in the EU: A Need or an Unnecessary Pursuit?</title>
      <link>https://trid.trb.org/View/2775391</link>
      <description><![CDATA[“Unruly passengers” seem to be a never-ending topic in the circles of aviation stakeholders for the past couple of decades.  Regardless of the existing international air law, numerous soft law instruments prepared to public and private air law organizations, and studies undertaken – statistics show that the number of unruly passengers is on a constant rise and industry keeps looking for solutions to deal more successfully with the issue.  At the EU level, only a handful of Member States have ratified the Tokyo Convention with the 2014 Montreal Protocol.  There is some regulation to deal with this negative trend, but no comprehensive approach has been taken to prevent and combat unruly passengers.  National authorities of EU Members States impose more severe penalties on passengers whose behavior on board flights in unacceptable, hoping to address this issue that is impairing the safety and quality of air travel, whereas air carriers are helping each other by sharing records on those passengers, introducing different methods aiming to achieve more efficiency, and cooperating with public and private authorities.  With these concerns in mind, and considering the existing EU aviation regulations and policy, this research explores the possibilities to institutionalize certain recommendations through amending the existing EU regulations.  By analyzing the most important elements of the “unruly passenger” issue, this paper tracks several points that could be regulated in a better and more efficient way, and offers suggestions for what could be done, and how.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775391</guid>
    </item>
    <item>
      <title>Leveraging connected vehicle data for roadway safety evaluation and management: segment- and intersection-level driving volatility analysis</title>
      <link>https://trid.trb.org/View/2767360</link>
      <description><![CDATA[The advancement of Intelligent Transportation Systems (ITS) and the proliferation of Connected and Automated Vehicles (CAVs) have enabled the collection of large-scale, high-resolution vehicle movement data through crowdsourcing. This wealth of data presents a unique opportunity to move beyond traditional, retrospective crash-based analyses toward more proactive and dynamic approaches to traffic safety assessment and management. This study proposes a comprehensive framework for driving volatility analysis at both the segment and intersection levels using large-scale connected vehicle trajectory data. Driving volatility—characterized by abrupt changes in acceleration, deceleration, and speed—is used as a behavioral safety surrogate to identify locations with elevated crash risk before crashes occur. The analysis integrates volatility metrics with roadway geometry, traffic volume, and historical crash data to develop predictive models of crash frequency. Results reveal a strong and statistically significant relationship between elevated driving volatility and higher crash frequencies, with segment-level volatility demonstrating particularly robust predictive power. These findings highlight the potential of incorporating driving volatility into ITS-based safety monitoring systems, enabling real-time detection of high-risk locations and more targeted, data-driven safety interventions. By leveraging CAVs as mobile sensors within an ITS framework, transportation agencies can enhance situational awareness, support proactive traffic safety management strategies, and improve roadway safety outcomes. This approach represents a significant shift toward real-time, behavior-based safety diagnostics in the era of intelligent and connected transportation systems.]]></description>
      <pubDate>Wed, 16 Sep 2026 16:15:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767360</guid>
    </item>
    <item>
      <title>Discourtesy: an anger evoking factor among Iranian drivers</title>
      <link>https://trid.trb.org/View/2761604</link>
      <description><![CDATA[Driving anger is known to be associated with aggressive driving, which can lead to a higher probability of accidents. Since the Driving Anger Scale (DAS) was first introduced in the US, different versions of the scale have been used in many countries to measure anger evoked by different traffic situations. The first aim of this study was to test the validity and reliability of the UK-DAS in Iran. The second aim was to examine the relationship between demographic factors and the DAS factors found in Iran. A total of 409 questionnaires were completed by Iranian drivers, out of which, 205 were used for exploratory factor analysis and 204 were used for confirmatory factor analysis. The results revealed a reliable four-factor structure in Iran including 19 of the original 21 items from the UK-DAS. These four factors were interpreted as discourtesy, direct hostility, reckless driving and progress impeded. Further analyses showed that younger, compared to older, participants scored higher on driving anger evoked by other road users’ discourtesy and direct hostile behaviours. With the factor structure validated in Iran the DAS can now be used in further research, for example to examine how Iranian drivers express their anger in different traffic situations. Cross-culturally, the results can be compared with other countries.]]></description>
      <pubDate>Tue, 15 Sep 2026 10:54:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761604</guid>
    </item>
    <item>
      <title>Analyzing Residential Speeding Using Connected Vehicle Data: A Case Study in the Charlottesville, VA Area</title>
      <link>https://trid.trb.org/View/2772607</link>
      <description><![CDATA[This study uses connected vehicle data to analyze speeding behavior on residential roads. A scalable pipeline processes trajectory data and supplements missing speed limits to generate summaries at OpenStreetMap’s way ID level. The findings reveal a highly skewed distribution of both aggressive and reckless speeding. Based on a case study of Charlottesville, VA’s connected vehicle data on residential roads, 38% of segments had at least one instance of aggressive speeding, and 20% had at least one instance of reckless speeding. In addition, the point-based nighttime speeding rates were higher than daytime rates for the vast majority of residential segments, and extreme violations on specific road segments highlight how severe the issue can be. Several segments rank among the top 10 for both aggressive and reckless speeding incidents, indicating particularly high-risk residential roads. These findings support the need for both spatial and behavioral interventions. This study provides a rich foundation for policy and planning, offering a valuable complement to traditional enforcement and planning tools. In conclusion, this framework sets the foundation for future applications in traffic safety analytics, demonstrating the growing potential of telematics data to inform safer, more livable communities.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772607</guid>
    </item>
    <item>
      <title>Understanding aggressive driving: a dual-theory approach using the norm Activation model and the theory of planned behavior</title>
      <link>https://trid.trb.org/View/2709497</link>
      <description><![CDATA[This study integrates the Norm Activation Model (NAM) and the Theory of Planned Behavior (TPB), along with two additional constructs – Multitasking While Driving (MWD) and Ego-Driven Driving Perception (EDDP) – to examine the psychological mechanisms underlying aggressive driving behaviors (ADB). A cross-sectional online survey was conducted among urban drivers in Tehran, Iran, using a convenience sampling strategy. The survey was distributed via social media platforms, and participation was voluntary, yielding 510 valid responses. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that personal norms, influenced by awareness of consequences and ascription of responsibility, reduce ADB, while attitudes, perceived behavioral control, and subjective norms intensify it. Notably, EDDP, influenced by MWD, weakens ethical norms and increases aggression, highlighting the interplay between personal morality and self-perceived expertise in shaping driving behaviors, while offering useful insights for urban traffic safety policies and driver behavior interventions.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709497</guid>
    </item>
    <item>
      <title>Road rage in the digital age: Social media, sleep, and aggressive driving</title>
      <link>https://trid.trb.org/View/2721822</link>
      <description><![CDATA[Research indicates that road rage is widespread, with nearly 92% of Americans reporting that they have witnessed such incidents and 89% stating that they have been victims of aggressive driving behaviors. In recent years, advances in technology may have given rise to new psychological antecedents of driving anger and aggressive driving. One potential factor is problematic social media use (PSMU). Although existing literature has established links between PSMU and aggression, and identified sleep disturbance as a mediator, no studies identified to date have explored whether these findings extend to the domain of driving anger. To address this gap, the authors surveyed 227 adult drivers (mean age = 30 years; 74% female; 50% Hispanic) to examine the predictive role of PSMU and the mediating role of sleep disturbance in driving anger, the expression of driving anger, and adverse driving-related incidents. Findings indicated that PSMU was significantly and positively related to driving anger. Moreover, sleep disturbance significantly mediated the effect of PSMU on driving anger caused by traffic delays, and on several forms of driving anger expression (e.g., verbal, and physical aggression) and driving incidents (e.g., accidents and close calls). Findings suggest that interventions aimed at reducing PSMU may reduce anger and aggression while driving. Moreover, sleep quality assessments could be integrated into driving safety programs or interventions for angry drivers. At the societal level, these findings point to the importance of social media involvement and sleep quality not only as public health concerns but also as factors in traffic safety.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721822</guid>
    </item>
    <item>
      <title>Generating Test Cases for Autonomous Vehicles with Controllable Levels of Difficulty</title>
      <link>https://trid.trb.org/View/2732157</link>
      <description><![CDATA[Autonomous vehicle (AV) safety validation increasingly relies on scenario-based testing. However, existing approaches to test scenario generation do not provide mechanisms to systematically regulate scenario difficulty. To address this critical limitation, this paper introduces a novel game-theoretic framework for adversarial safety validation. The interaction between the AV-under-test and a strategically obstructing rear vehicle is modelled as a Stackelberg game. The level of adversarial intensity, which reflects the level of difficulty, of a scenario can be controlled by a single tunable parameter called aggressiveness at both the action level and the interaction level. The efficacy of this approach is studied through the highway lane-changing operational design domain. Simulation experiments demonstrate that increasing aggressiveness reduces the success rate of lane-changing and prolongs maneuver duration for the successful attempts. These results confirm that this parameter can effectively and systematically control the difficulty level of test scenarios, providing a valuable tool for rigorous and reproducible AV safety validation.]]></description>
      <pubDate>Thu, 20 Aug 2026 09:55:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732157</guid>
    </item>
    <item>
      <title>Mitigating driver confusion and aggression in mixed traffic: Effects of V2V communication and driving style</title>
      <link>https://trid.trb.org/View/2737140</link>
      <description><![CDATA[Drivers in human-driven vehicles (HDVs) can get confused when encountering automated vehicles (AVs) driving so defensively that their behavior differs from what human drivers actually expect. The confusion may potentially provoke aggression toward AVs and jeopardize traffic safety. This study investigated whether a vehicle-to-vehicle (V2V) communication system can help mitigate HDV drivers’ confusion and aggressive driving behavior toward AVs in HDV-AV interaction, considering the driving styles of both HDV drivers and AVs. Forty-eight participants, classified by driving style (defensive, moderate, or aggressive), completed a driving simulator experiment in two blocks (V2V-off vs. V2V-on). In each block, they interacted with aggressive AVs and defensive AVs, respectively. Behavioral indicators of confusion and aggressive driving behavior were recorded from the driving simulator, and subjective confusion was measured via questionnaires. The results showed that compared to the V2V-off condition, interaction under the V2V-on condition significantly reduced HDV drivers’ confusion and mitigated aggressive driving behavior during the interaction. Aggressive AVs that asserted their right-of-way elicited less confusion among HDV drivers than defensive AVs that were more inclined to yield to other vehicles. These findings suggest that V2V communication and aggressive yet rule-compliant AV driving styles help reduce driver confusion and aggressive driving behavior, thereby improving HDV drivers’ experiences and reducing conflict risk during HDV-AV negotiations.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:03:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737140</guid>
    </item>
    <item>
      <title>A Secure Machine Learning-Based Framework for Aggressive Driving Behavior Analysis in Iov System</title>
      <link>https://trid.trb.org/View/2732091</link>
      <description><![CDATA[The increase in the number of road accidents is one of the major global concerns nowadays. The aggressive and abnormal driving behavior of the drivers is the primary cause of this issue. Such behavior can be observed frequently in urban traffic environments, leading to severe consequences including injuries, fatalities, and property damage. Therefore, it is very important to have a certain intelligent mechanism that can monitor driving behavior in real time and take appropriate action if it is found to be aggressive or abnormal. This can be possible by integrating the Internet of Things (IoT), digital twins, and machine learning technology. In this paper, we have proposed a secure machine learning-based framework for the detection of aggressive driving behavior in Internet of Vehicles (IoV) systems. It is assumed that smart IoT computing devices, such as sensors and cameras, are installed on roads and in moving vehicles to capture data such as sudden acceleration, harsh braking, sharp turns, tailgating, etc. This data is sent to the cloud servers in a secure manner, incorporating mutual authentication and key establishment between communicating entities. Machine learning models that are deployed on cloud servers are trained and tested against such data to analyze driving patterns. The machine learning classifiers that are utilized in the proposed work are Random Forest, K-nearest neighbor, AdaBoost, and Multi-layer perceptron (MLP) classifier, where Random Forest has achieved the maximum accuracy and F1-score. Finally, the proposed scheme is also compared with other existing schemes based on different parameters and outperforms those schemes.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732091</guid>
    </item>
    <item>
      <title>A stochastic jump-process driving dynamic model with application to traffic safety</title>
      <link>https://trid.trb.org/View/2716705</link>
      <description><![CDATA[Emerging high frequency, high resolution, large scale driving data reflect driving dynamics and provide valuable information on driving risk. Microscopic driving models are one of the fundamental tools for analyzing driving kinematic data and understanding temporal patterns within instantaneous driving decisions. Safety-critical events, characterized by abrupt and severe jerky behavior, lie outside the capabilities of such models. This paper proposes a stochastic driving model incorporating a novel jump process to capture aggressive driving behaviors related to crashes. The model parameters are estimated using a nonparametric approach. A new safety evaluation metric, Jump Size, is derived based on the stochastic model, accounting for the cumulative sum of squared jump sizes. The proposed safety metric demonstrates superior out-of-sample performance with solid theoretical foundations. We demonstrated the proposed stochastic model using the data from the Second Strategic Highway Research Program Naturalistic Driving Study. The proposed stochastic model outperforms its state-of-the-art counterparts and can benefit microscopic driving simulations and safety evaluations of automated driving systems.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716705</guid>
    </item>
    <item>
      <title>Rakd: Risk-Aware Knowledge Distillation for Autonomous Driving in Adversarial Scenarios</title>
      <link>https://trid.trb.org/View/2731608</link>
      <description><![CDATA[Aggressive driving behaviors of surrounding road users pose substantial threats to the safety of autonomous vehicles. However, current imitation learning methods struggle to learn a risk-aware driving policy, as they fail to learn the intrinsic decision-making principles and safety-relevant features from high-dimensional records. To address this issue, we propose a robust risk-aware knowledge distillation (RAKD) framework. RAKD employs a privileged teacher based on reinforcement learning to supervise a vision-based student via policy and feature distillation. The privileged teacher leverages two novel safety-critical representations in bird's-eye view and risk-aware reward shaping to enhance risk-sensitive learning, establishing superior imitation benchmarks through on-policy learning. Building on these benchmarks, we integrate a teachable module in the student network, aligning with the teacher's features through multi-scale attention-based feature distillation, focusing on collision risk-related information. Experimental results demonstrate that our RAKD-trained student achieves superior driving score and collision avoidance in diverse adversarial scenarios compared to baselines.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731608</guid>
    </item>
    <item>
      <title>The impact of hostility on airline pilots' safety behaviors: The roles of impulsivity and communication</title>
      <link>https://trid.trb.org/View/2686783</link>
      <description><![CDATA[Human error is a critical cause of aviation accidents, and individual psychological factors strongly influence pilots' safety behaviors. Therefore, it is essential to identify factors that may affect pilots' safety behaviors and explore their mechanisms. Based on the General Aggression Model (GAM), this study investigated whether, how, and under what conditions the mental health factor of hostility impacts pilots' safety behaviors, using a sample of 893 Chinese commercial airline pilots. The results show that hostility has a direct negative effect on pilots' safety behaviors, and this relationship is partially mediated by impulsivity. Additionally, communication negatively moderates the indirect effect of hostility on safety behaviors through impulsivity: when communication levels are higher, impulsivity is relatively lower, and the negative indirect effect of hostility on safety behaviors is weaker. Finally, the study provides practical recommendations for aviation safety management regarding hostility intervention, impulsivity control, and enhancement of communication and collaboration.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686783</guid>
    </item>
    <item>
      <title>Analyzing driver behavior: the influence of demographics on risky, aggressive, distracted, and unlawful driving behaviors</title>
      <link>https://trid.trb.org/View/2720319</link>
      <description><![CDATA[Road traffic injuries remain a major public health concern in Pakistan and other low- and middle-income countries, where driver behaviour is a dominant crash-contributing factor. However, limited multi-city evidence exists on how demographic characteristics jointly influence aberrant driving across different dimensions. This study investigates the associations among age, gender, and driving experience, and four dimensions of aberrant driving behaviour—risky, aggressive, distracted, and unlawful—using a cross-sectional survey of 400 drivers from 10 major urban centers in Punjab, Pakistan. A Driver Behaviour Questionnaire (DBQ)-based instrument was used to construct composite indices, and descriptive statistics, independent-samples t-tests, one-way analysis of variance, and multiple linear regression were applied. Risky driving had the highest mean score (3.81 on a five-point scale), with higher levels among drivers with ≥10 years of experience (mean = 4.02). Younger drivers reported higher levels of aggressive behaviour, while male drivers exhibited higher levels of aggressive, distracted, and unlawful behaviour. The findings indicate that dangerous driving in Pakistan is not limited to young drivers but extends to middle-aged and experienced drivers. This study contributes to road-safety literature by providing multi-city, disaggregated DBQ-based evidence from Pakistan, showing that demographic effects are not uniform across aberrant-driving dimensions and that risky and unlawful behaviours are not confined to young or inexperienced drivers but also extend to middle-aged and experienced drivers.]]></description>
      <pubDate>Mon, 06 Jul 2026 08:54:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720319</guid>
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