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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7RHJpdmVyIGluZm9ybWF0aW9uJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7RHJpdmVyIGluZm9ybWF0aW9uJnF1b3Q7IiAvPjwvZmlsdGVycz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Driver visual attention and in-vehicle touchscreen: the role of short training session</title>
      <link>https://trid.trb.org/View/2706362</link>
      <description><![CDATA[The growing integration of in-vehicle centre stack touchscreens has enhanced driver access to information and control systems but raised significant safety concerns due to increased visual distraction. This study investigates whether a short pre-drive training session can mitigate distraction and improve driver interaction with in-vehicle touchscreen. Using a driving simulator and eye-tracking technology, 60 licensed Norwegian drivers were assigned to trained and untrained groups to compare visual attention patterns during secondary tasks involving touchscreen use. Results showed that while all participants exhibited high visual demand on the touchscreen, trained drivers demonstrated slightly lower fixation counts, shorter durations, and reduced self-transition probabilities within the touchscreen area, suggesting more efficient and potentially safer interactions. However, these differences were not statistically significant, indicating a limited effect of the short training provided. The findings highlight the complexity of the touchscreen interface and potential of pre-drive touchscreen familiarization in improving visual attention.]]></description>
      <pubDate>Wed, 10 Jun 2026 09:05:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706362</guid>
    </item>
    <item>
      <title>Provision of Information to Encourage Driver Preparing Behavior toward Potential Risks</title>
      <link>https://trid.trb.org/View/2674988</link>
      <description><![CDATA[In this study, we investigate a method to make safe driving habits by making drivers take preparing behavior for potential risks by providing information corresponding to driving behavior when approaching an intersection. By the effect verification in the driving simulator, we confirmed the increase of the expected action, and it was indicated to be effective for the induction to the safe driving.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674988</guid>
    </item>
    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://trid.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709247</guid>
    </item>
    <item>
      <title>Real-time traffic information influences on motorbike route choice behaviour: A link-based analysis</title>
      <link>https://trid.trb.org/View/2703765</link>
      <description><![CDATA[This study investigates how traffic information influences motorbike riders’ route choices using a link-based analysis framework, addressing a gap in the literature. Estimating a Recursive Logit (RL) model explores the dynamic decision-making process when selecting routes. By analysing the reactions of riders in Bandung City, Indonesia, to Variable Message Signs (VMS) in stated preference surveys, the study identifies key attributes affecting their decisions for outgoing road sections, including distance, traffic-flow levels, travel time, and ramp-metering delays. The latter introduces a novel traffic-management measure for controlling motorbike proportions in mixed conditions. The RL model, which considers sequential link choices, provides unique insights into the adaptability and flexibility of motorbike riders to VMS, contrasting with traditional path-based static models. The findings underscore the importance of extending VMS access beyond toll roads and highways, especially in Southeast Asia, underlining the potential to improve regional traffic management significantly.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703765</guid>
    </item>
    <item>
      <title>Hierarchical control of plug-in hybrid electric vehicle platoon with DQL optimized SOC reference trajectories incorporating traffic information prediction working condition</title>
      <link>https://trid.trb.org/View/2701181</link>
      <description><![CDATA[To address the problem of the traditional linear state-of-charge (SOC) reference trajectory cannot adapt to the driving conditions, a hierarchical control of plug-in hybrid electric vehicle (PHEV) platoon with deep Q-learning (DQL) optimized SOC reference trajectories incorporating traffic information prediction working conditions is proposed. Based on the traffic information, the upper-level controller uses a long short-term memory (LSTM) neural network to achieve global speed prediction for the lead vehicle, and a nonlinear model predictive control (NMPC) algorithm to achieve longitudinal control of the platoon and obtain the optimal demand torque for the vehicle. The lower-level uses principal component analysis (PCA) and a K-means clustering algorithm to construct four typical working conditions. The radial basis function (RBF) neural network is used to predict the vehicle speed in a short time, and then the SOC reference trajectory in the prediction time domain is generated by the coupled DQL network. Under the framework of MPC, real-time energy management of the platoon is achieved through rolling optimization using the dynamic programing (DP) algorithm. The results show that the strategy can significantly improve the fuel economy of the platoon. Compared with the time-based and distance-based SOC reference trajectories, the platoon’s fuel consumption is reduced by 7.94% and 3.72%, respectively. Compared with the SOC reference trajectory based on DP, the platoon’s fuel consumption is roughly the same. However, the strategy can not only adapt to the change of driving conditions but also be applied online.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701181</guid>
    </item>
    <item>
      <title>Driver Takeover and Shared-Control Collaboration in Automated Driving Systems under Rural Conditions: Evaluating Real-Time Cognitive Responses in Field and Simulated Settings</title>
      <link>https://trid.trb.org/View/2703691</link>
      <description><![CDATA[This proposal outlines a multidisciplinary research initiative to assess drivers'
physiological and cognitive workload, stress levels, emotional states, and trust during takeover
performance in human-machine co-driving systems using an automated driving simulator. In specific,
three research objectives are to (1) Designing realistic and validated driving scenarios in simulators;
(2) Validating physiological sensors for measuring driver physiological responses; and (3)
Developing cognitive and situational awareness-based decision-making framework. To achieve these
objectives, simulator-based data will be collected, along with test drivers incorporating physiological
sensors while in the driving tests. Existing open-source data will be applied to expanding traffic
scenarios.
]]></description>
      <pubDate>Fri, 15 May 2026 14:19:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703691</guid>
    </item>
    <item>
      <title>Traveler Information for Rural Maryland</title>
      <link>https://trid.trb.org/View/2701236</link>
      <description><![CDATA[Disseminating traveler information in rural Maryland has long been challenging due to the limited deployment of Intelligent Transportation System (ITS) devices, such as dynamic message signs (DMS) and highway advisory radios (HARs). This challenge becomes particularly acute during major events, such as hurricanes or large-scale evacuations, when clear and accessible communication is critical. HARs, which operate on AM radio frequencies, have been a key tool for disseminating detailed information, but their reliance on outdated technology has made maintenance costly and increasingly unfeasible as spare parts become unavailable. The Maryland Department of Transportation State Highway Administration (MDOT SHA) has already retired half of its HARs and faces difficulty maintaining the remaining units, which are still vital in certain areas. ]]></description>
      <pubDate>Wed, 13 May 2026 09:12:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701236</guid>
    </item>
    <item>
      <title>Do you trust what an automated vehicle shows you? The effects of presenting dynamic system certainty information on driver behavior</title>
      <link>https://trid.trb.org/View/2652430</link>
      <description><![CDATA[As automated vehicle (AV) systems become increasingly more intelligent and self-aware of their capabilities, understanding drivers' interactions with AVs is paramount for successful integration of these vehicles into the broader transportation landscape. One area that needs more attention is understanding the effects of displaying AV self-assessed system certainty – regarding its navigability around roadway obstacles – on drivers' trust, decision-making, and behavioral responses. To contribute to the existing body of work, the current study evaluated a set of dynamic and continuous human-machine interfaces (HMIs) that present 2-dimensional AV system certainty information to drivers. A simulated driving study was conducted wherein participants were exposed to four different linear and curvilinear system certainty patterns (Linear Up, Linear Down, Convex, and Concave) on an HMI that represented an AV's confidence in its ability to safely avoid a construction zone ahead in its lane. Using this information, drivers decided whether or not (and when) to take over from the vehicle. The AV's true reliability and system certainty were not directly proportional to one-another. Trust, workload, takeover decisions and performance, eye movement behavior, and heart rate measures were captured during the study to understand drivers' responses to the vehicle certainty information. Overall, system certainty information had a significant effect on drivers' takeover response times and eye gaze behavior but did not affect their trust nor workload. In 24 % of all cases, participants either voluntarily took control of the AV when it was reliable or did not take over when the AV was unreliable. Trust was higher for participants who did not take over. The results of this work can be used to inform the design of in-vehicle interfaces in future autonomous vehicles, aiming to enhance decision-making and safety during driving.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:20:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652430</guid>
    </item>
    <item>
      <title>Insight Into Safety Challenges of Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2581845</link>
      <description><![CDATA[In the modern computational age, enormous amounts of information are being produced every single second. Once these piece of information in terms of data are properly fed, then this has the potential to expand the boundaries of computers. The current world is gradually transitioning to an automatic era, in which every entity and item are automated to carry out desired activities without requiring human participation. People’s lives are now easier and enjoyable due to this automation. Every aspect of computing, including those outside of it, has been automated. One such automation is smart mobility, which provides users with actual information about traffic patterns as well as advice for alternate routes in the event of traffic jams. Any business’s foundation is thought to be its transportation system. The automated intelligent transportation system (ITS), which has totally changed how products, people, and services are delivered, is crucial for achieving sustainability. This paper gives a general overview of the current ITS system, the idea of smart mobility, and current weaknesses in these systems. Their security worries and potential outcomes are also examined. Additionally, future ITS developments are discussed, as well as the significance and necessity of safeguarding these intelligent systems.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581845</guid>
    </item>
    <item>
      <title>Traffic Congestion and Drivers Behavior Analysis in India</title>
      <link>https://trid.trb.org/View/2658023</link>
      <description><![CDATA[This study examines how professional and general drivers adapt their behavior when receiving smartphone-based traffic information under real congestion. A randomized field experiment involving 300 drivers was conducted in Bengaluru, India, where four types of information content were delivered via a mobile application. Behavioral responses were analyzed using a Cluster-and-Synthesize (C&S) framework that integrates k-means clustering and principal component analysis (PCA). The C&S analysis identified distinct adaptation patterns: professional drivers showed stable and proactive reactions, while general drivers displayed delayed or reactive responses to informational cues. These differences were reflected in acceleration and steering stability indicators. The findings demonstrate that tailored information content can effectively influence driver behavior and improve traffic flow in urban networks. This data-driven framework contributes to the development of Intelligent Mobility Systems. And this data-driven framework contributes to the development of Intelligent Mobility Systems and provides practical insight for smart infrastructure design and adaptive mobility management toward safer and more sustainable transport systems.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658023</guid>
    </item>
    <item>
      <title>Standardized Framework for Winter Weather Road Condition Indices</title>
      <link>https://trid.trb.org/View/2693715</link>
      <description><![CDATA[State and local agencies across the United States have developed winter weather road condition indices (WWRCIs) to support decisions related to roadway operations, public information, road closures, and winter maintenance responses based on prevailing conditions. However, the absence of a standardized national framework for WWRCIs has resulted in substantial variation in how road conditions are defined, assessed, and communicated. These inconsistencies can create confusion for travelers and limit the ability of transportation agencies to compare performance, share best practices, and benchmark winter operations effectively. The objective of this project was to develop a standardized national framework for WWRCIs that reflects both operational realities and safety impacts across diverse climatic and geographic contexts in the United States. The framework is informed by a comprehensive assessment of existing practices, stakeholder input, and advances in data availability, including traditional weather and roadway sensors as well as emerging connected and autonomous vehicle (CAV) data sources. By promoting consistent definitions, indicators, and measurement principles, the proposed framework aims to advance the accuracy, reliability, and usefulness of winter road condition information provided to transportation agencies, policymakers, and the traveling public. Ultimately, this effort supports improved driver safety, reduced crashes and congestion, and more effective and coordinated winter weather response strategies nationwide.]]></description>
      <pubDate>Fri, 17 Apr 2026 08:55:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693715</guid>
    </item>
    <item>
      <title>Reinforcement Learning for Robust Advisories Under Driving Compliance Errors</title>
      <link>https://trid.trb.org/View/2561842</link>
      <description><![CDATA[There has been considerable interest in recent years regarding how a small fraction of autonomous vehicles (AVs) can mitigate traffic congestion. However, the reality of vehicle-based congestion mitigation remains elusive, due to challenges of cost, technology maturity, and regulation. As a result, recent works have investigated the necessity of autonomy by exploring driving advisory systems. Such early works have made simplifying assumptions such as perfect driver compliance. This work relaxes this assumption, focusing on compliance errors caused by physical limitations of human drivers, in particular, response delay and speed deviation. These compliance errors introduce significant unpredictability into traffic systems, complicating the design of real-time driving advisories aimed at stabilizing traffic flow. Our analysis reveals that performance degradation increases sharply under compliance errors, highlighting the associated difficulties. To address this challenge, we develop a reinforcement learning (RL) framework based on an action-persistent Markov decision process (MDP) combined with domain randomization, designed for robust coarse-grained driving policies. This approach allows driving policies to effectively manage the cumulative impacts of compliance errors by generating various scenarios and corresponding traffic conditions during training. We show that in comparison to prior RL-based work which did not consider compliance errors, our policies achieve up to 2.2 times improvement in average speed over non-robust training. In addition, analytical results validate the experiment results, highlighting the benefits of the proposed framework. Overall, this paper advocates the necessity of incorporating human driver compliance errors in the development of RL-based advisory systems, achieving more effective and resilient traffic management solutions.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561842</guid>
    </item>
    <item>
      <title>Adaptive electric vehicle routing and charging with deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2656336</link>
      <description><![CDATA[As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate remaining travel time and reliability scores for route planning. DQN learns optimal charging policies by determining when to charge, how much to charge (partial charging at 25%, 50%, 75%, or 100% levels), and which route to take based on battery state, traffic patterns, and available stationary charging stations (SCSs) and mobile charging infrastructure—including Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). DQN training uses simulation-based learning from actual traffic patterns of the Washington, DC metropolitan region, allowing the agent to explore charging-route pairs and discover efficient solutions through trial and error. To accommodate heterogeneous user preferences, the system calculates multiple Pareto-optimal solutions that trade off travel time, charging cost, battery safety, and route reliability, enabling users to select alternatives that match their current priorities without specifying preference weights in advance.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:59:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656336</guid>
    </item>
    <item>
      <title>Human-Centric Design for Next-Generation Infotainment Systems</title>
      <link>https://trid.trb.org/View/2580261</link>
      <description><![CDATA[Voice interaction is a valuable method for in-vehicle interactions, especially while driving. However, voice user interfaces (UIs) exhibit drawbacks, such as insufficient feedback, which negatively impact user experience. They also lack key usability heuristics, including visibility of system status and user control. Despite the long-standing presence of head-up displays (HUDs) in vehicles, there is still significant potential for such displays to enhance user experience and contribute to the development of next-generation infotainment systems. This paper proposes a multimodal approach to enhance the usability of voice UIs in vehicles by integrating visual interfaces on the HUD. The authors designed three visual UIs to support voice assistants. Furthermore, the authors conducted an expert evaluation based on usability heuristics to assess the three UIs effectiveness. The first UI, the Baseline UI, is the simplest. The second, the Flat Fusion UI, is a conventional design that uses a scrolling method to display additional information. The third, the AR Fusion UI, is a novel interface that employs a futuristic approach, leveraging real-world depth. The findings indicate that while usability varies among the three designs, they show promising results for future research. A user study is suggested to validate this paper’s findings and to thoroughly evaluate the three UI designs.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580261</guid>
    </item>
    <item>
      <title>Vehicular Edge Computing in Satellite-Terrestrial Integrated Networks</title>
      <link>https://trid.trb.org/View/2553365</link>
      <description><![CDATA[Internet of Vehicles (IoV) supported by terrestrial networks can satisfy the necessities of multiple computation-intensive applications. However, current terrestrial networks and resource management mechanisms may only partially guarantee vehicle and in-vehicle user equipment (VUE)’s quality of service due to the limited coverage of roadside units (RSU), especially in remote areas. This paper investigates vehicular edge computing (VEC) in satellite-terrestrial integrated networks with multiple low-earth orbit (LEO) satellites, ground RSUs, and VUEs. In remote areas without RSU coverage, VUEs can offload their partial tasks to satellites to save energy and guarantee latency. We aim to minimize VUEs’ weighted sum energy consumption by jointly optimizing VUEs’ association, data partition, computing resource allocation, power control, and bandwidth assignment under the constraints of maximum tolerant latency, maximum number of outage time slots, computation capacity at each satellite and each RSU, and maximum allowable transmission power at VUEs. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems. We efficiently solve each sub-problem by utilizing variable substitutions, the difference of convex functions algorithms, the Lagrangian dual method, and the Karush-Kuhn-Tucke conditions. Simulation results show that the introduced satellite-terrestrial integrated networks-enabled VEC scheme significantly reduces VUEs’ energy consumption compared to other schemes.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:21:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553365</guid>
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