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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=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" 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>
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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>Spatial-temporal planning of road traffic speed management mobile resources: Enhancing road traffic safety by optimizing resource utilization</title>
      <link>https://trid.trb.org/View/2680648</link>
      <description><![CDATA[Traffic safety on rural roads in various countries, particularly in developing countries, is a pressing concern, with speeding being a major contributing factor to traffic safety issues and crashes. This study introduces a framework for improving rural road traffic safety through the spatial-temporal planning of mobile traffic safety resources with consideration of fixed ones as part of speed management programs. The framework involves converting traffic data into inputs for an optimization model, which serves as a safety tool for traffic safety decision-makers. This tool indicates the time for mobile resources to visit each location. First, potential locations and their relative shares are determined based on a comprehensive analysis of road crash records, road properties, and fixed speed management resource locations, using the location-allocation model in ArcGIS. Then, the optimization model allocates traffic safety resources, considering both distance and time halo effects, which increase the unpredictability of these resources for drivers. A mathematical tool within the proposed framework is introduced to use mobile traffic safety resources in rural areas. This framework is particularly beneficial for developing countries, where resource allocations are planned solely based on the expertise of local professionals, rather than analytical methods. The results demonstrated through a case study on the Arak-Salafchegan Road show effective allocation and relocation of these resources to hotspot locations over time, considering real-world limitations, halo effects, and resource distribution, to improve rural road traffic safety. The framework offers a novel tool to tackle rural road traffic safety challenges. By integrating historical data analysis, integer programming, and real-world insights, the approach provides a robust solution that can be adopted by traffic authorities to make roadways safer.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680648</guid>
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    <item>
      <title>Driving behavior performance from tunnel main road to underground merging area: A real vehicle study based on speed and lateral offset</title>
      <link>https://trid.trb.org/View/2680637</link>
      <description><![CDATA[Multi-entry underpass road tunnels feature long entrance downhill sections and underground merging areas where main and secondary roads converge. These complex driving environments can lead to variations in driver speed and lateral offset, increasing the risk of traffic accidents. Therefore, this study aims to analyze the speed and lateral offset characteristics in different tunnel sections and their impact on traffic safety, providing support for traffic control and safety improvements in multi-entry underpass tunnels. This study conducted real-vehicle natural driving tests using test vehicles equipped with an inertial navigation system and Mobileye. Based on changes in tunnel alignment and road parameters, the study divided the test sections into five segments: tunnel external section, entrance downhill section, entrance internal section, underground merging section, and tunnel internal section. By analyzing the speed variation trends, lateral offset characteristics, and their interrelationships across these sections, a standardized relative deviation fraction was introduced to quantitatively compare driving behavior in key sections, revealing differences in driving patterns and potential safety risks across different road segments. The speed growth rate in the entrance downhill section was the highest at 15.09%. In contrast, drivers in the underground merging section had the lowest average speed at 54.057 km/h and the highest speed dispersion. The underground merging section had the lowest rate of lateral offset change but the highest dispersion in lane offset within this section. Conversely, the entrance downhill section showed the smallest dispersion, with a standard deviation of only 0.111. In addition, research found that the driving distance in each road section is positively correlated with vehicle speed and negatively correlated with lane offset. Through real-vehicle tests, this study analyzed the speed, lateral offset, and driving safety characteristics of different sections in multi-entry tunnels. The results indicate that the entrance downhill section and underground merging section pose higher driving risks, as fluctuations in speed and lateral offset contribute to driving instability. These findings reveal the driving risks associated with specific sections of multi-entry underpass road tunnels and provide important references for tunnel traffic management and safety optimization.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680637</guid>
    </item>
    <item>
      <title>Coordinated Control of Urban Rail Train Skip-Stopping and Inbound Passenger Flow Based on Deep Q-network</title>
      <link>https://trid.trb.org/View/2113874</link>
      <description><![CDATA[In the urban rail transit system, in order to deal with the problems of station congestion and excessive waiting time for passengers caused by train delays, it is necessary to adopt strategies such as skip-stopping and inbound passenger flow control. This paper proposes a train skip-stopping and inbound passenger flow control model based on reinforcement learning deep Q network, which optimizes the amount of inbound and train stops at the station in various time periods. So as to minimize the comprehensive benefits of station passenger platform overrun, average waiting time and passenger flow control intensity. Taking an example of postponement delays during rail transit operation, the deep Q network based on reinforcement learning is used to optimize the solution, which verifies the effectiveness of the method. The simulation results of using train skip-stopping and inbound passenger flow control are good. It can effectively reduce passenger waiting time under the condition of low passenger flow control intensity, improve passenger travel efficiency, and help alleviate passenger congestion at stations and gradually restore train operation order.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113874</guid>
    </item>
    <item>
      <title>Research on Automated Modeling Technology of Parking Lane Parameters in Railway Yards</title>
      <link>https://trid.trb.org/View/2113872</link>
      <description><![CDATA[The core technical support to realize the automatic design and modeling of railway yards is to establish a parameter-driven parking lane track deployment mathematical model, which is the main purpose of this research. According to design and engineering needs, aiming at the two main connection modes of parking lanes, three interdependent key control parameters (parking lane length, intersecting line length and junction line length) are reasonably proposed to drive the track distribution model. Through complex numerical calculations, high-precision closed conversion between various control parameters is realized, and the reliability is verified through experiments and actual modeling effects. According to the line position relationship constructed by the mathematical distribution model, each parking track can be easily output and three-dimensional modeling can be carried out on it. This creates important implementation conditions for the further establishment of platform automation modeling.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113872</guid>
    </item>
    <item>
      <title>A Comparison of One-Way Passenger Flow in Subway Channels Based on DaSiam-RPN and Deep-Sort Algorithm</title>
      <link>https://trid.trb.org/View/2113845</link>
      <description><![CDATA[The subway has become the main commuting tool in most cities, and its large passenger flow has become the norm in urban rail transit. Accurate and real-time statistics of passenger flow in stations can provide scientific basis for subway management and control. Currently, it is difficult to count the one-way passenger flow in the two-way passenger flow video in the current statistical methods commonly used in the field. In this paper, two algorithms, DaSiam-RPN and Deep-Sort, which are effective in target tracking, are selected for subway passenger transportation. Real-time one-way passenger flow statistics are carried out on the two-way passenger flow in the channel scene in the station, and the accuracy, real-time and performance of the two algorithms are compared.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113845</guid>
    </item>
    <item>
      <title>Understanding replication environments – a systems research approach to smart city replication of autonomous delivery robots</title>
      <link>https://trid.trb.org/View/2656969</link>
      <description><![CDATA[European policymakers chose the systematic funding of smart city initiatives to incentivize and accelerate innovation and sustainability transitions. To ensure these initiatives’ broader effectiveness, smart city replication has been incorporated in funding calls for research projects as a policy instrument for innovation diffusion, information dissemination and mutual learning. With a growing theoretical and empirical base for these replication activities, there is an increased awareness that integrating and transferring new ideas and solutions into the urban context requires a holistic perspective and includes various endogenous and exogenous influencing factors. This article proposes a systemic view by presenting a method for analysing the replication environment for autonomous delivery robots based on a causal loop diagram. The method is applied conceptually to a district in Munich. The developed approach, which is called Replication Causal Loop Diagram, serves as an analytical tool to generate and provide relevant contextual knowledge and information about the replication environment to facilitate the operational planning and implementation of replicable initiatives, solutions, and practices. In further development steps and in particular settings, the approach can also be a valuable addition to the replication portfolio for stakeholder engagement and consensus building.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656969</guid>
    </item>
    <item>
      <title>Quantifying Highway Flow Improvements via CACC-Based Truck Platooning: A Statistical Modeling Approach</title>
      <link>https://trid.trb.org/View/2692246</link>
      <description><![CDATA[The advancement of Cooperative Adaptive Cruise Control (CACC) technology enables vehicle platooning on public roads, offering significant potential to enhance urban mobility, driving safety, and energy efficiency. Among various applications, truck platooning has become a promising strategy to increase highway flow rates by reducing vehicle headways, improving coordination, and optimizing space utilization. This paper presents a quantitative assessment of a CACC-based truck platooning system, focusing on its effectiveness in enhancing highway mobility under varying traffic conditions. A statistical regression model is developed and calibrated using simulations of real-world highway networks to identify key influencing factors and evaluate the resulting improvements in traffic flow. The analysis considers five primary variables: desired platoon speed, platoon size, space headway, percentage of platooning trucks, and non-platoon traffic flow. The study systematically examines the impact of each parameter on overall traffic throughput. Results indicate that truck platooning can increase highway flow rates by up to 200%, particularly under conditions of high truck volumes and larger platoon sizes. Both platoon size and the percentage of platooning trucks show a positive correlation with flow rates, suggesting that greater coordination among vehicles enhances overall mobility. Conversely, higher desired speeds and larger space headways tend to diminish the benefits of platooning by reducing traffic density. Overall, this paper provides a comprehensive quantitative evaluation of the mobility benefits of truck platooning and highlights its potential to significantly improve highway operations. Future work will extend these findings to assess the energy and emission benefits of platooning and to evaluate the performance of large-scale platooning deployment strategies.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692246</guid>
    </item>
    <item>
      <title>MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing</title>
      <link>https://trid.trb.org/View/2692237</link>
      <description><![CDATA[With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high-fidelity vehicle and powertrain models in Autonomie. The MBSE backbone in AMBER organizes requirements, interfaces, plant and controller models, and test scenarios into a single set of models that is maintained across pure simulation, Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and VIL stages. Each stage has a clear role: simulation enables rapid development and validation of advanced models or controls across a large number of scenarios; SIL supports standalone algorithm verification and scenario down-selection; PIL validates real-time execution, inputs/outputs, and timing on the target processor; and VIL provides closed-loop evaluation with a real vehicle under controlled laboratory conditions. AMBER’s automated build and configuration enable rapid retargeting across platforms and repeatable scenario reproduction, making validation fast and cost-effective. To demonstrate its practical application, the workflow is used to validate the functionality and quantify the energy savings of an eco-driving control against a calibrated human driver model. Experiments show strong repeatability and consistent energy gains for the eco-driving strategy while preserving trip time, yielding average energy savings of 7.8% across the evaluated scenarios. Overall, the MBSE-guided XIL workflow shortens development time and reduces test cost by limiting on-road testing and lowering integration risk before track evaluation, while producing credible, closed-loop energy assessments traceable from requirements to test evidence.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692237</guid>
    </item>
    <item>
      <title>Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions</title>
      <link>https://trid.trb.org/View/2692128</link>
      <description><![CDATA[Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated an additional 5 collisions and did not mitigate 1 collision. In these 16 conflicts entered, 93% of serious injury risk was reduced by the Waymo Driver, whereas 84% of serious injury risk was reduced by the NIEON model. Further, in a case-by-case evaluation, the Waymo Driver’s collision avoidance led to reduced serious injury risk when compared to the NIEON model in every simulated event. The results of this paper demonstrate that a reference model like NIEON can be used to benchmark ADS responder performance in response to high-risk initiating behaviors performed by the current driving population.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692128</guid>
    </item>
    <item>
      <title>Potential Regulatory Approaches for Occupant Safety in Automated Vehicles with Unique Seating Configurations – Part 2: Interior Safety Sensing and Messaging Proposals</title>
      <link>https://trid.trb.org/View/2692052</link>
      <description><![CDATA[This paper contains Part 2 of a two-part paper series proposing potential regulatory approaches for occupant safety in Automated / Autonomous Vehicles (AVs) with unique seating configurations (stagecoach and campfire seating). Part 2 focuses on interior safety sensing, associated messaging, and ride control approaches both prior to and during a ride. Assessments are also proposed after significant vehicle braking and crash events.The proposed conditions are to be assessed in a static vehicle environment with humans segmented by occupant size and an infant dummy. On the vehicle seat and on the vehicle floor occupant detection conditions are proposed along with restraint usage detection conditions for vehicle seat belt usage, Child Restraint Seat (CRS) usage, CRS seat belt usage, and Lower Anchors and Tethers for Children (LATCH) system usage. These conditions may be detected by sensors / computer algorithms and human monitoring and thus are technology agnostic. The topics of animal detection and cargo detection are also discussed.Part 1 of this paper series proposed using interior safety sensing as an alternative / a replacement for the National Highway Traffic Safety Administration (NHTSA) Federal Motor Vehicle Safety Standard 208 (FMVSS-208) Occupant Crash Protection unbelted in-position occupant compliance conditions. This paper proposes conditions involving occupant and seat belt restraint usage detection. This evaluation approach strives to prevent unbelted occupants and is an improvement over restraint countermeasures for unbelted occupants.This paper also discusses and proposes visual and audible safety messaging for prior to the ride occupant education and for occupant and restraint usage detection outcomes. Vehicle level ride control actions are suggested such as preventing a ride when improperly restrained occupants are detected.These approaches can be used in industry-wide regulatory next step contemplation for unique interior seating arrangement AVs. When adopted, these approaches would likely reside in an expanded version of FMVSS-208.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692052</guid>
    </item>
    <item>
      <title>Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications</title>
      <link>https://trid.trb.org/View/2692003</link>
      <description><![CDATA[Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy efficiency and driving comfort. The improvements are evaluated by objective performance indicators. In particular, it was found that the controller recovers more kinetic energy during braking maneuvers and lowers driver stress by up to over 90 % fewer mandatory pedal changes compared to already industrialized approaches.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692003</guid>
    </item>
    <item>
      <title>Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization</title>
      <link>https://trid.trb.org/View/2691917</link>
      <description><![CDATA[Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback strategies through continuous interaction and evaluates the impact of specific guidance actions, such as but not limited to “release accelerator pedal”, “brake” and “recuperate”, on immediate energy efficiency and long-term driver adaptation patterns. Feedback intensity and modality are dynamically tailored to individual driver profiles based on observed reaction patterns and feedback adherence. This approach encourages drivers to prioritize energy efficiency while aiming to minimize cognitive distraction and discomfort. The algorithm is implemented and validated within a driving simulation environment that replicates diverse and realistic conditions. Virtual driving tests conducted in various scenarios, such as congested urban areas, suburban routes, mountain roads and highways demonstrate that the proposed PPO-based eco-driving assistance system can reduce energy losses by about 28% compared to conventional driving behavior.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691917</guid>
    </item>
    <item>
      <title>A Dynamic Driver Comfort Index for Stabilizing Mixed Traffic Behavior</title>
      <link>https://trid.trb.org/View/2691877</link>
      <description><![CDATA[Despite remarkable advances in vehicle technology - enhancing comfort, safety, and automation – productivity of transportation over the road continues to decline. Stop-and-go driving remains one of the most persistent inefficiencies in modern mobility systems, leading to greater travel delays, energy waste, emissions, and accident risk. As vehicle volumes rise, these effects compound into systemic challenges, including driver frustration, unstable flow dynamics, and elevated greenhouse gas (GHG) emissions. To address these issues, an extensive data-driven evaluation was performed characterizing the underlying causes of traffic instability and uncovering hidden behavioral parameters influencing traffic flow. This research led to the identification of a previously unrecognized metric - the Driver Comfort Index (DCI) - which quantifies an inter-vehicle spacing behavior that reflects intrinsic human driving behavior. Building on this discovery, mixed traffic is explored to identify its phenomena, where human-driven and machine-controlled vehicles coexist and share the road. It appears that adaptive cruise control (ACC) and connected autonomous vehicles (CAV) are controlled by a non-intrinsic parameter so that traffic mix suffers from a mismatch of vehicle dynamics. This mismatch is explored, and it is proposed to harmonize traffic dynamics by adopting the natural DCI parameter as the single control mechanism. Analytical studies demonstrate that DCI-based traffic flow orchestration, applied integrally to human- and machine-controlled vehicles, enhances traffic flow stability, mitigates stop-and-go oscillations, and significantly improves network efficiency, safety, and environmental performance.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691877</guid>
    </item>
    <item>
      <title>Look-Ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks</title>
      <link>https://trid.trb.org/View/2691836</link>
      <description><![CDATA[Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the efficiency of the traction electric machine (EM), gear losses, and battery dynamics, which are essential for optimizing overall energy consumption and improving vehicle performance. This research paper shows the impact of powertrain efficiencies on the optimal speed profile generation with a predictive cruise controller (PCC). The PCC optimizes electric truck operation by focusing on three primary factors in its cost function: 1) battery energy consumption, 2) total trip time, and 3) battery state of charge (SOC). To achieve the optimal speed profile, Sequential Quadratic Programming (SQP) is used. A comparison was made with a conventional cruise controller, which simplifies the vehicle model by minimizing the required energy at wheels and ignores powertrain losses in its energy calculations. The results show that the proposed PCC offers a 12.85% improvement in battery SOC and 10.83 % improvement in energy consumption as compared to baseline.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691836</guid>
    </item>
    <item>
      <title>Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2691832</link>
      <description><![CDATA[This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating realistic traffic signals, road gradients, and vehicle interactions. RL agents are trained to interpret vehicle states, road attributes, and traffic light information to adjust control parameters in real time. This integration enables the controller to anticipate and respond to dynamic driving scenarios, thereby improving both energy efficiency and operational robustness. Simulation experiments across multiple driving scenarios demonstrate that the RL-enhanced eco-driving controller achieves substantial energy savings without compromising travel time. On average, our approach surpasses a baseline eco-driving controller without RL by 12% and outperforms a high-fidelity human driver model by 24.2% in terms of energy consumption reduction. These results highlight the potential of continuous action space RL to advance real-time eco-driving control in CAVs. Overall, this work provides a pathway toward more intelligent, adaptive, and sustainable vehicle control systems that can accelerate the deployment of energy-efficient mobility solutions.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691832</guid>
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