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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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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>Dynamic Vehicle Routing Optimization for Urban Distribution Under Real-Time Demand Fluctuations</title>
      <link>https://trid.trb.org/View/2685746</link>
      <description><![CDATA[With the rapid rise of e-commerce, the logistics and distribution industry is experiencing unprecedented growth. In particular, intra-city distribution is the crucial “last mile” of logistics and plays a decisive role in determining overall customer satisfaction. This study improves an inclusive vehicle routing optimization framework for intra-city distribution under dynamic demand. The initiative of a novel memetic algorithm that efficiently solves the NP-hard dynamic vehicle routing problem while guaranteeing high service quality and cost reduction. However, modern intercity distribution systems often struggle with low information, unpredictable demand patterns, and high operational costs due to scattered customer locations and dynamic order information. Addressing these challenges, this study suggests a comprehensive and intelligent vehicle routing optimization framework tailored for intracity distribution under dynamic demand conditions. The proposed system begins with a grey prediction model for short-term demand forecasting across many distribution regions, permitting differentiated vehicle loading methods to optimize transportation costs and improve operational effectiveness. Building upon this, a dynamic vehicle routing optimization model is formulated to reduce costs while assuring high levels of customer satisfaction within strict delivery time windows. To competently manage fluctuating demand, a dynamic information processing approach is introduced; prioritizing customer needs based on their urgency and importance, thereby guaranteeing the timely delivery of critical orders with minimal computational overhead. Moreover, a novel memetic algorithm is considered to solve the complex NP-hard dynamic vehicle routing problem. This algorithm integrates an adaptive elite genetic algorithm for global search with improved crossover and mutation operators, improved by local search methods such as 2-opt and swap methods to refine solutions. Numerical experiments validate the feasibility and performance of the proposed method, indicating significant improvements over conventional fully loaded vehicle schemes and regular route update methods. The results highlight the practical value of the system in attractive intra-city logistics efficiency, reducing costs, and inspiring customer service standards.]]></description>
      <pubDate>Mon, 17 Aug 2026 08:27:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685746</guid>
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    <item>
      <title>On-Site Demonstrations of C-ITS Architecture and Autonomous Vehicle Operation in the City of Trikala, Greece</title>
      <link>https://trid.trb.org/View/2579496</link>
      <description><![CDATA[This paper delves into the Cooperative Intelligent Transport Systems (C-ITS) architecture of the on-site pilot conducted in the city of Trikala, Greece, as part of the EU-funded project, IN2CCAM. The pilot tests and demonstrates innovative Cooperative, Connected, and Automated Mobility (CCAM) services through the deployment of autonomous vehicles (AVs) and their integration with a C-ITS platform. Featuring a fleet of autonomous electric minivans on a predefined route equipped with advanced smart digital infrastructure, this initiative aligns with the city’s vision to tackle congestion, promote the use of shared transport services, and enhance the equity of the local transport system. Furthermore, a Mobility as a Service (MaaS) mobile application enables the public to plan multimodal trips, integrating on-demand shared passenger transport services with AVs, public transport services, shared micromobility solutions, and active mobility, such as walking or cycling. The proposed C-ITS architecture includes: a) AV fleet management and monitoring, b) a Green Light Optimal Speed Advisory (GLOSA) functionality to achieve smoother and more fuel-efficient journeys, c) a traffic-based green wave system to contribute to high traffic efficiency by adjusting traffic signals according to real-time congestion levels, and d) C-ITS messages between the infrastructure and the AVs to alert about uncontrolled crossings of Vulnerable Road Users (VRUs). This paper aims to outline the architectural components of this pilot and discuss future work, which will include evaluating the feasibility, effectiveness, and societal impact of the proposed innovations in the local ecosystem of Trikala.]]></description>
      <pubDate>Wed, 12 Aug 2026 17:07:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579496</guid>
    </item>
    <item>
      <title>Intelligent Speed Assistance Guide for State Highway Safety Offices</title>
      <link>https://trid.trb.org/View/2720300</link>
      <description><![CDATA[Speeding remains one of the most persistent and deadly threats on U.S. roadways, accounting for more than 11,000 deaths in 2024, and 125,000 fatalities over the last decade. One promising countermeasure to help address speeding behavior is Intelligent Speed Assistance (ISA) technology, which uses real-time Global Positioning System (GPS) data to detect the speed limit and proactively alert (or limit) the driver if they are speeding, can reduce speeding and help promote long-term safe driving behaviors.
The Governors Highway Safety Association (GHSA) recently documented a growing number of examples of ISA’s effectiveness at the local level. In New York City, a pilot program involving 500 fleet vehicles saw a 64% reduction in speeds substantially above speed limits. A District of Columbia school bus pilot logged 10,000 miles with zero speeding events.
European research has shown that ISA can reduce crash risk and lessen the severity of injuries, particularly in areas with changing speed limits or heavy pedestrian activity. A 2019 policy report from the European Transport Safety Council estimated that ISA could cut road deaths across Europe by approximately 20%. Another study projected that equipping all vehicles with mandatory active ISA could reduce injury and fatal crashes by 20% and 37%, respectively.
Given the potential for wide adoption of ISA to substantially reduce speeding-related fatalities and serious injuries, research is needed to identify ways for state highway safety offices (SHSOs) to advance the use of this technology.

OBJECTIVE: The objective of this research is to develop a guide that supports efforts by SHSOs to: 1) Conduct comprehensive stakeholder assessment to identify which groups have the greatest need for education and which hold the most influence over its adoption. 2) Develop a core set of educational active ISA materials or leveraging materials available from other sources. 3) Ensure that SHSO staff have a strong foundational understanding of active ISA. Staff training should cover how active ISA works, its effectiveness as demonstrated in peer-reviewed research, relevant policy considerations and communication strategies tailored to different audiences. 4) Establish clear metrics and evaluation processes allows SHSOs to measure the effectiveness of educational initiatives and outreach efforts. 5) Engage with key stakeholders to advance pilot programs. 6.) Develop guidelines and implementation frameworks for pilot projects that help SHSOs evaluate and demonstrate effective strategies for modifying speeding behavior through ISA technologies, including  recommendations on target driver populations, stakeholder coordination, public communication, data collection, performance measures, privacy considerations, and evaluation methodologies to support broad adoption.
]]></description>
      <pubDate>Thu, 02 Jul 2026 20:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720300</guid>
    </item>
    <item>
      <title>How do drivers interact with in-vehicle information systems in everyday driving? Evidence from large-scale naturalistic data</title>
      <link>https://trid.trb.org/View/2713735</link>
      <description><![CDATA[How do drivers interact with in-vehicle Information Systems (IVIS)? Driver distraction is one of the major contributors to road crashes, and IVIS interactions are a potential source of such distraction. Yet, little is known about when, how, and under which driving contexts drivers engage with IVIS in everyday driving. However, for safe system design and evidence-based regulation, the authors must understand which context factors shape IVIS engagement. To close this gap, the authors leverage large-scale naturalistic data collected from 669,493 customer trips across 79 countries, to characterize everyday IVIS interactions. Using mixed-effects models, the authors examine how environmental conditions, vehicle dynamics, road infrastructure, driving automation, and vehicle mileage affect how drivers engage with IVISs. The results show that drivers interact approximately every five minutes, mostly at the start of their trip, and more frequently via physical buttons than touchscreen inputs. The authors further observe significant regional differences in how IVISs are used and show that engagement decreases with vehicle mileage. In partially automated conditions, drivers are more likely to initiate interactions, while increased driving dynamics lead to fewer and less complex engagements. Together, these findings extend existing evidence by demonstrating how multiple contextual factors simultaneously shape real-world IVIS engagement. They provide an empirical basis for context-aware IVIS design, evaluation, and regulation that more accurately reflects the diversity and variability of everyday driving behavior.]]></description>
      <pubDate>Fri, 26 Jun 2026 13:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713735</guid>
    </item>
    <item>
      <title>Regional route guidance with multi-party perspective under disinformation attacks: An MFD-based approach</title>
      <link>https://trid.trb.org/View/2681774</link>
      <description><![CDATA[With the rapid development of wireless communication technology, the threat of cyberattacks to Intelligent Transportation Systems (ITS) is growing. Some cases have demonstrated that disinformation attacks targeting route guidance (RG) systems can have a significant impact on traffic flow of the urban road network. Currently, there is a lack of model that describe traffic dynamics under disinformation attacks, as well as a clear understanding of the mechanisms through which disinformation impacts road traffic network. To address this issue, the behavioral characteristics of all parties involved in the context of disinformation attacks are first summarized from both the perspectives of the manipulator and the attacked, utilizing the Macroscopic Fundamental Diagram (MFD) model. Moreover, the indifference band is introduced to simulate the traveler’s trust level under disinformation attacks. Secondly, a two-layer RG model under disinformation attack is developed. The upper layer consists of a Model Predictive Control (MPC) route guidance decision model, which is based on aggregated traffic dynamic model. The lower layer is a discrete-event, trip-based plant model, designed to realistically simulate the updates in traffic dynamics. Then, a decision algorithm for heterogeneous route selection behavior is incorporated, enabling the simulation of diverse responses from travelers when confronted with misleading guidance information. Finally, the proposed RG strategy is compared with other strategies in the road network. The results show that under disinformation attacks, compared with other Logit-based RG strategies, the proposed RG strategy is able to reduce the negative impact (such as increased TTS) caused by the attack, especially in optimizing the travel time distribution of travelers, and reducing the maximum accumulation across the entire road network by 40%.]]></description>
      <pubDate>Thu, 25 Jun 2026 15:57:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681774</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>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>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>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>Dynamic coordinated routing and charging navigation system for traffic congestion mitigation: A game theoretical modelling and asynchronous distributed optimization approach</title>
      <link>https://trid.trb.org/View/2599182</link>
      <description><![CDATA[As the world embraces sustainable transportation, electric vehicles (EVs) have emerged as a promising solution. However, their increasing popularity brings forth new challenges, particularly concerning congestion at charging stations and on roads. Recent advancements in wireless communication and sensing technologies enable travelers to access real-time traffic and charging station availability information via navigation systems, allowing them to make informed routing and charging decisions to avoid congestion. However, this real-time information can sometimes be counterproductive. If travelers react independently and selfishly to similar traffic information, it can exacerbate congestion due to the flash crowd effect. This study aims to alleviate such problems by developing a navigation system based on dynamic coordinated joint routing and en-route charging mechanism (DcRC) to provide coordinated routing and charging guidance for both EVs and internal combustion engine (ICE) vehicles, thereby reducing congestion on roads and at charging stations. Specifically, by incorporating traffic flow and charging station queue dynamics, the DcRC is formulated as a mixed strategy congestion game with an equivalent mathematical programming model, which generates equilibrium routing and charging decisions to mitigate the flash crowd effect and reduce traffic congestion without violating each vehicle’s self-interest. To integrate the DcRC into online navigation services, this study develops an asynchronous distributed ADMM-aided Branch-and-Bound (DAB) solution algorithm to efficiently solve the DcRC with hundreds of participants. The DAB draws upon a customized branch and bound algorithm to decompose the complex mathematical program into manageable sub-problems, and utilizes an asynchronous distributed ADMM to solve each sub-problem efficiently with privacy protection, leveraging individual vehicles’ computing resources while sustaining robustness against their unstable and stochastic computing and communication performance. Numerical experiments confirm the DcRC’s effectiveness in reducing congestion and system costs, as well as the DAB’s efficiency in supporting real-time navigation services for hundreds of participants with unstable computation and communication performance.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2599182</guid>
    </item>
    <item>
      <title>Balancing transparency and control: The impact of AI explanation detail on user perception in automated vehicles</title>
      <link>https://trid.trb.org/View/2626060</link>
      <description><![CDATA[As automated vehicle technology advances, explainable AI has emerged as a critical tool to enable users to understand and predict the behavior of AI systems, particularly in safety-critical applications such as automated driving. However, increased transparency in AI explanations may inadvertently contribute to an “illusion of control”, a cognitive bias in which drivers overestimate their influence or understanding of the AI’s actions. The authors aim to better understand how the level of detail in AI explanations affects users of automated vehicles. In a virtual reality study, N=44 participants experienced different explanation levels (low, medium, high) in an automated ride (SAE L4) compared to a baseline condition with no explanations. The results show a significant improvement in participants’ user experience, acceptance, and explanation satisfaction, with more detailed explanations. The findings also indicate that as AI explanations become more detailed, users’ perceived level of control increases significantly, although this perception does not correlate with actual control capabilities. At the same time, it decreased their desire to take control, indicating users’ susceptibility to the ’illusion of control’ bias in the context of automated driving. Overall, this suggests that the design of explanation interfaces should strive for a balanced level of detail that promotes AI transparency without causing cognitive overload. At the same time, explainable AI can be utilized to decrease users’ desire to intervene in the AI’s actions.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:57:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2626060</guid>
    </item>
    <item>
      <title>Design constraints of a forerunner UAV in safety improvement of first responders</title>
      <link>https://trid.trb.org/View/2609271</link>
      <description><![CDATA[Unmanned Aerial Vehicles (UAVs) are already in use by emergency services. Drones are becoming faster and more reliable, making them suitable for safety-critical tasks. A forerunner UAV can fly ahead of an emergency ground vehicle (EGV). It can make a decision on the traffic situation at the next intersection to notify the EGV driver if other vehicles have given the right of way or not. This notification service can increase the speed of the EGV and prevent crashes that may occur due to the driver’s obstructed view. A forerunner drone for slow-speed EGV (10 m/s) has already been developed and successfully tested; however, the applicability of current drone technology to the forerunner task at higher speeds was not analyzed. This paper identifies the key parameters of the task itself and the forerunner system in a general case, and gives the main tradeoff inequalities of these design parameters. The requirements and limitations of a forerunner drone are investigated within a reasonable parameter space, and the most relevant configurations are tested in dynamic simulation using optimized velocity profiles for the drone. Software-in-the-loop tests were also performed in a complete city simulation. The results indicate that the forerunner task is solvable with current fastest or near-future drone technology if the route of the EGV is known to the UAV control.]]></description>
      <pubDate>Tue, 18 Nov 2025 09:30:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2609271</guid>
    </item>
    <item>
      <title>In-vehicle notifications to drivers during emergency road events: A conceptual design for Rubberneck and Sinkhole emergencies</title>
      <link>https://trid.trb.org/View/2604863</link>
      <description><![CDATA[The authors introduce a conceptual design for an internal Human-Machine Interface (iHMI) that allows highway operators to communicate directly with drivers during emergency events. The system adapts the notifications to the driver’s demographics and reported stress tendencies based on the RESIST module. Two emergency scenarios—Rubbernecking and Sinkhole—are used to illustrate the design. A Sinkhole emergency involves a partial road collapse that blocks traffic, while a Rubberneck emergency refers to traffic congestion caused by drivers slowing down to observe an incident outside their lane. Drawing on literature and expert interviews, the authors developed the emergency scenarios in a driving simulator, designed the iHMI notifications, and filmed videos from a driver’s perspective. A two-part online study was developed. It was distributed via social media and received responses from 108 drivers. First, respondents were categorized into support groups: “Need Support” (n = 20), “Would Like Support” (n = 49), and “Cool” (n = 39). Then, they viewed six short videos, varying by the event and the proximity to it (two events × three proximity levels) according to their assigned support group. Notification frequency differed among the support groups, with more frequent updates provided to those requiring greater support. After each video, participants rated the notifications they received. Results indicate an overall satisfaction with the frequency and detail level of the notifications for all support groups, with some variation by event type. Respondents were receptive to the concept of in-vehicle notifications suggesting that road operators should consider implementing adaptive iHMI systems to inform and support drivers during emergency events.]]></description>
      <pubDate>Fri, 24 Oct 2025 14:25:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604863</guid>
    </item>
    <item>
      <title>Route guidance attacks in cyber transportation networks: A user-centered study of behavioral sensitivity</title>
      <link>https://trid.trb.org/View/2602774</link>
      <description><![CDATA[The rapid adoption of digital navigation systems and connected vehicles has introduced new vulnerabilities in transportation networks, including the risk of Route Guidance Attacks (RGAs) that disseminate distorted travel times. Drawing on traveler psychology and decision theory, this study investigates how RGAs propagate congestion through three behavioral models: perfect rationality, logit-based stochastic choice, and bounded rationality with an indifference threshold. By systematically varying attack intensity and traveler indifference levels, the authors simulate user responses in Sioux Falls, Anaheim, and Chicago networks. The results show that user behavior and network structure jointly determine the severity and spatial distribution of congestion: dense networks can initially absorb low-intensity misinformation but undergo sharp overload once critical thresholds are crossed, whereas sparser networks succumb to traffic disruptions even under modest falsifications. Perfectly rational users exhibit collective behavior toward the shortest route, logit-based users disperse but remain susceptible at high intensities, and boundedly rational travelers alter their routes when a strategically induced benefit surpasses their indifference level. These findings underscore the necessity of coupling cybersecurity measures with interventions that account for trust, risk perception, and the cognitive heuristics travelers use to evaluate route choices. In particular, user-interface designs featuring reliability scores, timely alerts, or partial verifications can reduce blind compliance and mitigate the sudden mass switching that amplifies adversarial manipulation. This study enhances the interaction between traveler behavior models and network topology under RGAs, contributing to transportation psychology, informing safer route guidance design, and highlighting strategies for improving network resilience.]]></description>
      <pubDate>Mon, 13 Oct 2025 08:48:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602774</guid>
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