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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>
    <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>Redesigning Trip-Based Travel Demand Models for the Connected Automated Vehicle Era</title>
      <link>https://trid.trb.org/View/2775075</link>
      <description><![CDATA[This study introduces a systematic redesign of trip-based travel demand models to explicitly incorporate connected automated vehicles (CAVs). The framework models auto ownership of CAVs and human-driven households separately, accounts for zero-occupancy vehicle (ZOV) trips, and modifies trip distribution, mode choice, and temporal patterns to reflect anticipated behavioral and operational changes because of CAV presence. The framework is then applied to the Triangle Region of North Carolina, using Triangle Regional Model Generation 2, and reveals that CAV adoption increases vehicle miles traveled (VMT) and encourages longer trips, with average discretionary and work trips rising by 28%–31% under a 70% CAV adoption rate. Although VMT increases, effective capacity gains reduce total network delay by as much as 60% relative to the 2050 baseline. At the facility level, demand-to-capacity ratios decrease, indicating that some roadway expansion projects could potentially be deferred. Sensitivity analysis reveals that system performance is highly dependent on realized capacity improvements and cautious assumptions, producing nearly 40% more delay than the expected scenario. The proposed redesign framework provides transportation agencies with a scalable and practical approach to incorporating CAVs into long-range planning, project prioritization, and investment decision-making.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:49:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775075</guid>
    </item>
    <item>
      <title>Airside Automated Ground Vehicle Systems at U.S. Airports: Adoption Framework</title>
      <link>https://trid.trb.org/View/2775404</link>
      <description><![CDATA[Automated ground vehicle systems (AGVS) at airports represent a growing segment of the automated and connected vehicle industry. Many airports are exploring ways to incorporate these emerging technologies to realize operational, safety, cost, and environmental benefits. As part of these efforts, airports are increasingly testing and demonstrating AGVS in airside applications. However, clear guidance and best practices are not currently available to help airports evaluate their unique needs and operating environments.  ACRP Research Report 283: Airside Automated Ground Vehicle Systems at U.S. Airports: Adoption Framework, produced by TRB's Airport Cooperative Research Program, identifies the high-level activities airports should undertake when planning for airside AGVS adoption. These activities include understanding the airport environment and the key factors that influence implementation before developing a Concept of Operations, conducting safety risk management, planning performance metrics and data collection, and establishing training and knowledge management practices. A key challenge facing airports is the lack of consistent implementation approaches and industry standards for airside AGVS. To help address this challenge, the report identifies best practices and highlights applicable robotics and autonomous vehicle standards.  The project that produced ACRP Research Report 283 also produced ACRP Web-Only Document 70: Airside Automated Ground Vehicle Systems at U.S. Airports: Implementation Playbook. The companion publication addresses additional challenges and barriers to implementing airside AGVS, including knowledge gaps, safety and risk concerns, and technology readiness.]]></description>
      <pubDate>Tue, 08 Sep 2026 16:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775404</guid>
    </item>
    <item>
      <title>Airside Automated Ground Vehicle Systems at U.S. Airports: Implementation Playbook</title>
      <link>https://trid.trb.org/View/2775403</link>
      <description><![CDATA[Automated ground vehicle systems (AGVS) at airports represent a growing segment of the automated and connected vehicle industry. Many airports are exploring ways to incorporate these emerging technologies to realize operational, safety, cost, and environmental benefits. As part of these efforts, airports are increasingly testing and demonstrating AGVS in airside applications. However, clear guidance and best practices are not currently available to help airports evaluate their unique needs and operating environments.  ACRP Web-Only Document 70: Airside Automated Ground Vehicle Systems at U.S. Airports: Implementation Playbook, produced by TRB’s Airport Cooperative Research Program, addresses additional challenges and barriers to implementing airside AGVS, including knowledge gaps, safety and risk concerns, and technology readiness. It is a supplement to ACRP Research Report 283: Airside Automated Ground Vehicle Systems at U.S. Airports: Adoption Framework, which identifies the high-level activities airports should undertake when planning for airside AGVS adoption.]]></description>
      <pubDate>Tue, 08 Sep 2026 16:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775403</guid>
    </item>
    <item>
      <title>Cooperative control of heterogeneous vehicle platoons under communication time–varying delays and intermittent observations</title>
      <link>https://trid.trb.org/View/2636182</link>
      <description><![CDATA[This article discusses the cooperative control problem for heterogeneous vehicle platoons subject to non-ideal factors, specifically communication time-varying delays, system noises and intermittent observations. The main idea is to construct the internal reference models to generate common signals for all vehicles. First, in ideal situations under a directed acyclic topology (DAT), a distributed controller is proposed based on the properties of lower triangular matrices and solving an algebraic Riccati equation (ARE); Second, for non–ideal situations under uniformly quasi-strongly connected topology, optimal states are estimated using intermittent observations, and a distributed controller is designed to maintain the platoon’s mean square stability (MSS). Compared to the control methods in existing literatures, the proposed control approaches in this paper, relying on the system’s output information rather than state information, can effectively suppress the impacts of vehicle heterogeneity and the aforementioned non–ideal factors on platoon stability. Simulations are conducted to demonstrate a superior convergence speed compared to those in the literatures.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636182</guid>
    </item>
    <item>
      <title>Joint Lane Selection and Coordinated Signal Control for Mixed Traffic Arterials with CAV-Dedicated Lanes</title>
      <link>https://trid.trb.org/View/2714312</link>
      <description><![CDATA[Although prior studies have demonstrated the potential of connected and automated vehicle–dedicated lanes (CAV-DLs) to enhance traffic efficiency, their corridor-level impacts under mixed traffic environments have not been sufficiently quantified. A primary challenge in existing literature is the restriction of CAVs to dedicated lanes, a constraint that frequently triggers saturation imbalance by overloading CAV-DLs while leaving adjacent mixed lanes underutilized. To address these issues, this study develops a hierarchical control framework for mixed traffic arterials that integrates CAV lane selection with coordinated signal control. This framework utilizes a lane-selection mechanism to dynamically reallocate through-moving CAVs between dedicated and mixed lanes, complemented by a platoon-control policy in no-lane-change zones to exploit the benefits of reduced headways. Building on these mechanisms, a lane-selection-based multiagent proximal policy optimization (LS-MAPPO) controller is developed using the centralized training and decentralized execution (CTDE) paradigm. Extensive simulation results show that under high traffic demand, the LS-MAPPO controller reduces average vehicle delay by 18.6% to 40.3% compared with benchmark methods while simultaneously shortening queue lengths. The analysis further indicates that these benefits are highly sensitive to both traffic demands and CAV penetration rates (PRs). Specifically, under heavy demand with a 40% to 60% CAV PR, a single CAV-DL can reduce delay by 13.8% to 21.1%. In contrast, at low penetration levels, the deployment of CAV-DLs can be counterproductive, increasing delays by 31.7% to nearly 85.9% in extreme scenarios. These results provide quantitative evidence for the deployment of CAV-DLs across varying traffic demands and CAV PRs.]]></description>
      <pubDate>Tue, 01 Sep 2026 09:10:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714312</guid>
    </item>
    <item>
      <title>Evaluating the Impact of CAV-Dedicated Lanes on Freeway Capacity and Safety: Findings from Simulation Experiments</title>
      <link>https://trid.trb.org/View/2714340</link>
      <description><![CDATA[Mixed traffic of connected and automated vehicles (CAV) and human-driven vehicles (HDV) can lead to potential traffic conflicts, dramatically reducing highway throughput. Dedicated lanes (DL) for autonomous vehicles could be a key solution to this issue. This study builds a modified simulation environment for optimizing the assignment and management policies of CAV DL in real freeway scenarios based on the third-party software SUMO. First, a continuous freeway network is constructed according to the 102 km bidirectional eight-lane Beijing-Xiong’an Freeway. Multiple vehicle types and heterogeneous driving behavior models are integrated to the simulation environment as well as the traffic demand distribution being generated with the LandScan population density data. Next, a multidimensional evaluation framework incorporating traffic efficiency, capacity, and safety is created. Further, a ranking model based on ideal solution similarity is used to evaluate the performance of the optimized CAV DL. Finally, five groups of simulation experiments are conducted to quantify the impacts of different assignment modes and management policies on CAV DL deployment in various scenarios, and the traffic evolutions in emergency situation are also examined. The experimental results show that, when the CAV penetration rate is below 20%, one DL is enough for the isolated operation of CAV; simultaneously, traffic origin-destination (OD) distribution significantly affects the efficiency of DL. When direct routes account for less than 60% of total demand, the DL should be placed as the most outside lane to mitigate the adverse impacts induced by the frequent on-ramp and off-ramp maneuvers of CAV; in emergency situations, compared to the outermost side, placing DL on the innermost side leads to greater disruption, as the congestion induced by the accident spreads more easily, with an average speed reduction of 30.77%. Above all, the proposed simulation environment can conveniently provide effective decision support for planning and designing CAV DL in freeway networks.]]></description>
      <pubDate>Tue, 01 Sep 2026 09:10:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714340</guid>
    </item>
    <item>
      <title>Human–Machine Cooperative Control of Intelligent Vehicles Considering the Trajectory Tracking Accuracy and Driver Load</title>
      <link>https://trid.trb.org/View/2685976</link>
      <description><![CDATA[Aiming at improving the trajectory tracking accuracy and reducing the driver load and human–machine conflict during human–machine driving, this article designs an intelligent vehicle human–machine cooperative controller, considering the trajectory tracking accuracy and driver load. A human–machine cooperative controller is designed based on the human–machine coupled control. The controller can respond to the driver in real time, and a parallel indirect cooperative control structure is formed with the controller and the driver model. To solve the flexibility of weight allocation for human–machine driving, a weight distributor is designed based on the fuzzy theory, considering the fuzzy inputs of the driver, lateral deviation, and lateral velocity. The proposed human–machine cooperative method is verified by the CarSim/Simulink platform and on-road tests. The results show that the cooperative controller and weight allocation method can effectively improve the trajectory tracking accuracy and reduce the driver load and human–machine conflict.]]></description>
      <pubDate>Mon, 31 Aug 2026 16:43:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685976</guid>
    </item>
    <item>
      <title>Strategyproof Mechanism for Resource Allocation and Cost-Sharing in Edge-Assisted Vehicle Computing</title>
      <link>https://trid.trb.org/View/2761385</link>
      <description><![CDATA[Mobile edge computing (MEC) has powerful computing capabilities, and intelligent vehicles have abundant sensing resources; thus, integrating them can leverage their respective advantages to provide broader services. Motivated by this, we develop the edge-assisted vehicle computing system, in which MEC servers and vehicles are responsible for data processing and collection, respectively. Since the sensor data collected by the same sensor device in the vehicle is identical at any given time, we propose a resource-sharing model in which multiple users share the sensing resources, with the vehicle’s cost shared among the serviced users. However, the strategic operations by a single user or group of users may harm the interests of the system and other participants. Therefore, we aim to design a strategyproof mechanism to drive the system into an equilibrium. We propose a cost-sharing mechanism and show that it achieves both strategyproofness and group strategyproofness. A design method for the strategyproof mechanism is proposed. We show that the proposed mechanism achieves individual rationality, budget balance, and consumer sovereignty. Furthermore, we analyze the approximate ratio of the proposed mechanism. Experimental results demonstrated that the proposed mechanism performs well across different scenarios.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761385</guid>
    </item>
    <item>
      <title>Application of Digital Twins for Testing Connectivity, Automation, and Cooperation Applications</title>
      <link>https://trid.trb.org/View/2761021</link>
      <description><![CDATA[This study investigated the use of co-simulation for evaluating cooperative driving automation (CDA) applications within a work zone environment. The examined use case focused on the merging maneuvers of a single CDA-equipped vehicle into either a CDA platoon or a traffic stream consisting of human-driven vehicles near a work zone with a one-lane blockage. The findings demonstrate that co-simulation can serve as an effective component of automated vehicle and CDA testing, particularly in complex and challenging environments such as work zones. The study further demonstrated that cooperative lane-changing behavior near work zones can be systematically assessed within a controlled simulation environment, and that the resulting performance measures can help identify limitations and guide improvements to existing automated and cooperative driving algorithms and control logic.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:38:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761021</guid>
    </item>
    <item>
      <title>Occlusion-Aware Diffusion Model for Pedestrian Intention Prediction</title>
      <link>https://trid.trb.org/View/2685899</link>
      <description><![CDATA[Predicting pedestrian crossing intentions is crucial for the navigation of mobile robots and intelligent vehicles. Although recent deep learning-based models have shown significant success in forecasting intentions, few consider incomplete observation under occlusion scenarios. To tackle this challenge, we propose an Occlusion-Aware Diffusion Model (ODM) that reconstructs occluded motion patterns and leverages them to guide future intention prediction. During the denoising stage, we introduce an occlusion-aware diffusion transformer architecture to estimate noise features associated with occluded patterns, thereby enhancing the model’s ability to capture contextual relationships in occluded semantic scenarios. Furthermore, an occlusion mask-guided reverse process is introduced to effectively utilize observation information, reducing the accumulation of prediction errors and enhancing the accuracy of reconstructed motion features. The performance of the proposed method under various occlusion scenarios is comprehensively evaluated and compared with existing methods on popular benchmarks, namely PIE and JAAD. Extensive experimental results demonstrate that the proposed method achieves more robust performance than existing methods in the literature. To benefit the community, we open-source our code at https://github.com/AISLAB-sustech/ODM]]></description>
      <pubDate>Fri, 28 Aug 2026 16:25:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685899</guid>
    </item>
    <item>
      <title>Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making</title>
      <link>https://trid.trb.org/View/2685886</link>
      <description><![CDATA[The inflexible human-autonomy relationship within autonomous driving scenarios still has not realized synergetic intelligence, therefore unable to provide adaptive and context-sensitive decision-making and sometimes leading to violation of human pReferences or even hazards. In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. The proposed algorithm uses the result of driving risk reasoning as one input of reinforcement learning combining fNIRS-based risk and driving safety field model-based risk, realizing integrating human brain activity into the reinforcement learning scheme, then overcoming the disadvantage of machine-oriented intelligence that could violate human intentions. To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. The experiment involving twenty participants has been conducted, and the results show that in continuously high-risk driving scenarios, compared to traditional reinforcement learning algorithms without human participation, the proposed algorithm can maintain a cautious driving policy and avoid potential collisions, validated with both proximal surrogate indicators and success rates.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685886</guid>
    </item>
    <item>
      <title>Machine intelligence-based security system to protect smart vehicles from relay attacks</title>
      <link>https://trid.trb.org/View/2703772</link>
      <description><![CDATA[The advancement of technology and the expansion of IoT have reduced the gap between humans and machines in recent years. IoT systems are always vulnerable to attacks from devilish minds on Earth, and these might be sufficient to bring down a whole company in the marketplace. There are incidents of smart car thefts that occur covertly due to a well-known exploit called the relay attack. It's crucial to properly neutralise the relay attack since there are situations where multiple users are operating an automobile at once. By leveraging the current innovation, we aim to mitigate the method that cybercriminals use to easily break into vehicles. The most serious issue is the 'Relay Attack', which could use a latent keyless entry system weakness to start a Passive Keyless Entry System vehicle. An adversarial attacker gains entry to a mechanised vehicle by intercepting radio messages and relaying them back to the vehicle. The effect of this assault and the misfortune is huge. In this work, an amalgamation of network security, the Internet of Things and machine intelligence is considered to formulate a countermeasure for relay attacks. The proposed system ensures the security of vehicles that work on a passive keyless entry system by authenticating multiple drivers through the CART algorithm.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703772</guid>
    </item>
    <item>
      <title>A tale of three cities: A process tracing analysis of policy instrument choice when governing shared mobility</title>
      <link>https://trid.trb.org/View/2701474</link>
      <description><![CDATA[During the past 15 years, shared mobility services such as ride-hailing and scooter-sharing have been deployed in cities worldwide. These deployments, typically done by private companies, have led to a wide range of policy responses by city governments, which have started to gain more attention in the academic literature. In this paper, we identify and classify the policy instruments used by three city governments to steer the direction of shared mobility services. We also identify and analyse the motivations and constraints of local policymakers in selecting these instruments over others. Finally, we also employ a process-tracing methodology to assess whether existing theories of policy instrument selection fit the shared mobility policy space. To do this, we conducted three case studies of cities where private companies had introduced shared mobility services: Bogotá, Paris, and Los Angeles. We selected these cases since each had a distinct policy response to the deployment of shared mobility services. For each city, we conducted a documentary analysis complemented by interviews with policymakers and practitioners. Overall, we analysed 571 documents and carried out 16 interviews. Through this process, we identified a total of 52 unique instruments used collectively by the three cities, of which 30 were substantive (directly affecting policy outcomes), 23 were procedural (affecting policy processes), and one was classified as both. We also documented and classified new instruments that did not exist before shared mobility and were developed specifically to address the challenges of private companies using technology to deploy new mobility services. We identified that instrument selection at the local level can be constrained by national structures, but when these change, local governments adapt quickly and change their instrument selection strategy. We also found that when no national policy constraints exist, policymakers try to select instruments that balance the efficacy of the instrument against its legitimacy. We identified other motivations for instrument selection that do not fit within existing theories of instrument selection; these include the desire to reclaim some of the power lost as the transport policy space has moved towards networked governance. Our study is relevant to both city and national governments worldwide, which are facing the challenge of governing the deployment of new and ‘smart’ mobility services by private-sector companies, by providing them with a better understanding of existing tools and motivations for policy instrument choice in this policy space. It also provides researchers in this space with new evidence to support instrument selection theories of legitimacy and efficacy. It also identifies new areas for theory development, showing how existing theories do not have explanatory power for some of our findings.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701474</guid>
    </item>
    <item>
      <title>Cloud-Based Eco-Driving Predictive Cruise Control for Connected and Automated Electric Vehicles</title>
      <link>https://trid.trb.org/View/2735036</link>
      <description><![CDATA[The vehicle-road-cloud integration system (VRICS) offers enhanced information acquisition and computational processing capabilities, significantly advancing the development of connected and automated vehicles (CAVs). Leveraging the VRICS, this study proposes a cloud-based predictive eco-driving strategy (CPES) to improve energy efficiency and reduce intersection traversal time for CAVs. By integrating real-time roadside sensing and cloud-based historical data, the CPES enables CAVs to globally plan optimal lanes and driving speeds with receding horizon optimization, while explicitly accounting for the dynamic behaviors of surrounding vehicle queues. The CPES begins by developing a prediction model to estimate the dynamic queue dissipation time at signalized intersections, based on the characterization of both lateral and longitudinal movements of surrounding vehicles. Subsequently, an improved gray wolf optimization algorithm is used to determine the optimal driving lane and speed of CAV under dynamic traffic conditions, incorporating the predicted queue dissipation into the optimization process. Finally, the CPES is validated through stochastic simulations and real-world experiments. Compared with the existing advanced methods, the results show that CPES achieves an average energy saving of up to 5.79% and reduces the average travel time by 4.65%. Furthermore, empirical vehicle testing demonstrates that CPES possesses practical deployment capabilities, maintaining robust performance even under complex and dynamic traffic conditions.]]></description>
      <pubDate>Thu, 27 Aug 2026 10:04:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735036</guid>
    </item>
    <item>
      <title>Macro-Micro Hierarchical Reinforcement Learning for Cooperative Control of Intelligent Connected Vehicles at Unsignalized Intersections</title>
      <link>https://trid.trb.org/View/2761482</link>
      <description><![CDATA[Unsignalized intersections are critical bottlenecks in urban traffic systems. Although multi-intersection coordination has been explored, existing methods often suffer from high state dimensionality and the lack of hierarchical coordination mechanisms, which limit convergence efficiency and system scalability. To address these issues, this paper proposes CoRL-MM, a Macro-Micro Hierarchical Reinforcement Learning (HRL) framework. At the macro-control layer, Twin Delayed Deep Deterministic Policy Gradient (TD3) is employed for global traffic optimization, while at the micro-control layer, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used for real-time vehicle coordination. A three-dimensional state matrix with virtual lane mapping is further introduced to compress the state space and improve learning efficiency. Simulation results show that CoRL-MM reduces average travel time by 59% and limits cumulative collisions to fewer than 15 within 50,000 steps, significantly outperforming traditional traffic signal control. These results demonstrate CoRL-MM's strong performance in the evaluated road networks and offer a promising, efficient approach for managing unsignalized intersections in future intelligent transportation systems.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:05:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761482</guid>
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