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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Deep Reinforcement Learning for Hybrid Traffic Control: Coordinating AI and Fixed-Time Signal Controllers in Urban Networks</title>
      <link>https://trid.trb.org/View/2696101</link>
      <description><![CDATA[Recently, adaptive intersection signal control methods based on artificial intelligence (AI) have garnered substantial research interest. As these studies progress, the practical feasibility of AI-driven intersection control has been increasingly validated, with several algorithms already implemented in pilot scenarios. Consequently, urban road networks will likely feature a hybrid configuration of traditional and AI-based intersection controllers in the foreseeable future. Nevertheless, existing studies remain insufficient regarding AI control strategies operating within such mixed-control environments. Motivated by this gap, this paper investigates two critical issues: (1) the influence of an AI-controlled intersection on the signal control effectiveness of its neighboring intersections, and (2) the surrounding control environment prerequisites required for successful AI intersection deployment. To deal with these problems, this study first proposes a novel adaptive intersection signal control algorithm for individual intersections, termed downstream green-wave aware intersection controller (DGWAIC), which integrates mechanisms recognizing and rewarding upstream and downstream green-wave conditions into the proximal policy optimization framework. Subsequently, to validate the proposed methodology, a canonical experiment featuring a central AI-controlled intersection surrounded by four traditionally controlled intersections (a “1+4” experimental scenario) was designed. Comparative analyses between the proposed method and an individual reward-oriented AI control strategy were conducted under both coordinated and uncoordinated control scenarios to explore control effectiveness across varying application contexts. The experimental results underscore two key insights. First, individually reward-driven AI intersection control strategies exhibit inherently selfish and aggressive behaviors, necessitating algorithmic consideration of the propagation effects on adjacent traditional intersections to prevent localized optimization from compromising overall network efficiency. Second, the adaptability and coordination capabilities of AI-controlled intersections remain inherently limited, indicating that deploying AI controllers within environments characterized by coordinated peripheral intersection controls further leverages the advantages of AI-based traffic control.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696101</guid>
    </item>
    <item>
      <title>MARL-based cooperative transit signal priority for the arterial road to reduce schedule delay</title>
      <link>https://trid.trb.org/View/2643295</link>
      <description><![CDATA[Transit signal priority (TSP) is an effective strategy to reduce transit delays and improve intersection efficiency. This paper introduces a Cooperative TSP strategy of Variable phase (CTSPV) using multi-agent reinforcement learning (MARL) to minimize transit schedule delays on arterial roads. The agents adjust phase sequences and durations based on real-time traffic, balancing transit and non-transit vehicle needs, resolving conflicting bus requests, and ensuring agent cooperation. Invalid action masking ensures compliance with green time and phase-skipping rules. Simulation results show CTSPV reduces person delay, queue lengths, and lateness by 8.7%, 31.6%, and 17.0%, respectively, compared to fixed-time signals. Testing different green time constraints highlights the importance of proper restrictions for efficient learning. Analysis of CTSPV's signal timing reveals agents prioritize phases with high traffic demand and bus priority, skipping phases with lower demand. Evaluation results of generalized rule-based strategies based on those RL-derived patterns demonstrate the good performance of RL-learned knowledge.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643295</guid>
    </item>
    <item>
      <title>Bus priority: lane-based pre-timed signal timing with buses in different directions at isolated intersections</title>
      <link>https://trid.trb.org/View/2649629</link>
      <description><![CDATA[The existing research on bus signal priority (BSP) with exclusive bus lane (EBL) often assumes that all buses travel straight through, neglecting the presence of turning buses. This oversight can lead to the issues such as BSP green time waste and traffic conflicts with buses turning, resulting in delays and safety concerns. This research investigates the applicability of BSP and EBL combined with lane-based pre-timed signal timing at isolated intersections to accommodate bus and car movements to various directions. By considering the constraints related to lane markings, signal timing, and traffic flow saturation, we develop the models aimed at maximizing passenger throughput and minimizing passenger delay, specifically through mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP), respectively. The branch-and-bound method is adopted to solve the MILP, while the extended cutting plane method is employed to solve the MINLP with convexity validation against specified lane assignment. To enhance computational efficiency, three acceleration strategies of surrogate model, branch pruning, and parallel computation are implemented for the MINLP. Case studies reveal that EBL is not adopted even bus ratio is high up to 56.5%, different from the previous research on BSP and EBL that assumes all buses travel straight. In contrast, when left- or right- turning cars are prohibited at the target intersection, BSP and EBL are always adopted to enhance passenger throughput by 6% to 15%, or reduce passenger delay by 9% to 21%. This study contributes to the redesign of BSP and EBL for intersection efficiency maximization when buses are allowed to make turns.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2649629</guid>
    </item>
    <item>
      <title>Inference of signal phase and timing with low penetration rate vehicle trajectories</title>
      <link>https://trid.trb.org/View/2597133</link>
      <description><![CDATA[Traffic signals are a crucial component of urban traffic networks, and signal phase and timing (SPaT) information serves as an essential input for various urban traffic operational applications. Obtaining SPaT information on a large scale is challenging due to the diversity of traffic signal controllers from different manufacturers and jurisdictions. With the advent of broadly defined connected vehicles, vehicle trajectories can be leveraged to estimate SPaT information since they are directly controlled by traffic signals. Although some existing studies have proposed methods for estimating SPaT information using vehicle trajectory data, most are limited to fixed-time traffic signals. To address this limitation, this paper proposes a suite of SPaT inference algorithms applicable to both fixed-time and responsive signals. With only low penetration rate vehicle trajectory data as input, the inference program can estimate the complete SPaT information for traffic signals with fixed cycle lengths and the average cycle/splits for those with time-varying cycle lengths. The proposed method is validated through case studies at real-world intersections.]]></description>
      <pubDate>Mon, 24 Nov 2025 15:30:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597133</guid>
    </item>
    <item>
      <title>Convexity and global optimisation of lane-based fixed-time signal model for delay minimisation at an isolated intersection</title>
      <link>https://trid.trb.org/View/2556758</link>
      <description><![CDATA[Lane-based fixed-time signal is basic to various signal control strategies. It performs well in maximizing road capacity, but is faced with significant challenge in minimizing traffic delay. This study validates the convexity of lane-based fixed-time signal model for delay minimization, when lane markings are determined as well as flow factors. Thus Breadth first search algorithm is developed to enumerate the feasible lane markings, which are then screened with flow factors. Cutting plane algorithm is applied to the CMINLP for each feasible lane markings, where the non-linear delay function is converted to a series of linear ones, until the relaxed delay converges to the actual delay. Branch pruning strategy is established for efficiency, to eliminate the lane markings with uncompetitive delay. Numerical analyses follow to validate the proposed algorithm. This research promotes the redesign of lane-based fixed-time signal control.]]></description>
      <pubDate>Wed, 01 Oct 2025 11:36:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556758</guid>
    </item>
    <item>
      <title>Optimization Model and Simulation Analysis for Pre-Timed Signals at Isolated Intersections</title>
      <link>https://trid.trb.org/View/2283121</link>
      <description><![CDATA[This paper establish a model for optimization of pre-timed signals at isolated intersections which are signal group-based, considering pedestrian and bicycle inter-green time minimum cycle, maximum cycle, minimum split and maximum split constrains, using the MOE's of VISSIM Micro-Simulation as optimization objectives. And the optimization arithmetic is also put forward. Then a series of simulation test are conducted on an isolated pre-timed four-leg intersection under both under-saturated and saturated conditions. Finally, the availability and efficiency of the model and the some valuable conclusions are given at last.]]></description>
      <pubDate>Thu, 17 Oct 2024 09:15:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2283121</guid>
    </item>
    <item>
      <title>A New Logical Control Model of Traffic Lights</title>
      <link>https://trid.trb.org/View/2283040</link>
      <description><![CDATA[Based on Pan-Boolean algebra with an illustration of crossroads in the urban trunk roads, a new logical control model of the traffic light is designed in this paper. It breaks down the conventional pattern of fixed time distribution, realizes a flexible control and takes into account the combinations of various traffic lanes. Characterized by simple presentation and convenient operation, it can be clearly shown after the simulation that the model is preferable to the conventional pattern of fixed time distribution in promoting the efficiency of traveling vehicles. Since it can make the vehicles travel more efficiently, it may be employed in the near future.]]></description>
      <pubDate>Thu, 17 Oct 2024 09:15:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2283040</guid>
    </item>
    <item>
      <title>Distributed optimization for multi-commodity urban traffic control</title>
      <link>https://trid.trb.org/View/2420380</link>
      <description><![CDATA[A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed-gradient projection algorithm. The intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, the authors demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.]]></description>
      <pubDate>Mon, 16 Sep 2024 09:00:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2420380</guid>
    </item>
    <item>
      <title>The Performance of Connected and Autonomous Vehicles with Trajectory Planning in a Fixed Signal Controlled Intersection</title>
      <link>https://trid.trb.org/View/2370935</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) are recognised as a technology trend in the transportation engineering arena. As one of the most popular capabilities of CAVs, trajectory planning attracts extensive attention and interest from both academia and the industry. Segmented trajectory planning is gaining popularity for its simplicity and robustness in computation and deployment. Constructive recommendations and guidelines can be provided by exploring the effects of segmented trajectories in different settings of CAVs and intersections. This research proposes a control strategy for segmented trajectory planning in a fixed signal timing environment. To test the effects of this control strategy, this research designs simplified fixed signalised intersection scenarios and implements segmented trajectory planning features of CAVs with different traffic demand scenarios, distances and speed limits. The results show that the proposed control strategy has stable superior performances in different traffic scenarios especially when the traffic volume is near capacity.]]></description>
      <pubDate>Wed, 22 May 2024 10:28:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2370935</guid>
    </item>
    <item>
      <title>Fixed-Time Consensus for Multiple Tractor-Trailer Vehicles With Dynamics Control: A Distributed Internal Model Approach</title>
      <link>https://trid.trb.org/View/2369354</link>
      <description><![CDATA[Recently, the fixed-time consensus problem for multiple nonholonomic robot systems has gained much attention due to its practical value, high accuracy and strong robustness, and almost all of the existing studies have focused on kinematics control. However, the actual system is essentially dynamic, and the generalized force or torque driven by the design meets the actual requirements. Given this consideration, the fixed-time posture and velocity leader-following consensus problem is investigated for a class of nonholonomic robots, namely, the tractor-trailer vehicles with dynamics control. A nonlinear leader system capable of generating reference postures and velocities applicable to tractor-trailer vehicles is first formulated. Utilizing the newly proposed distributed observer capable of estimating the state and output matrix of the leader system in fixed time and the distributed internal model capable of reconstructing information in fixed time, all the tractor-trailer vehicles can acquire and construct the necessary reference information through the communication network in fixed time. Then, by combining the dynamic gain and state constraint methods, a fixed-time velocity ideal controller capable of overcoming nonholonomic constraints, exerting the desired control over postures, and with first-order derivability capable of being tracked by the actual velocity through the design of the control torque is devised at the kinematic level, which ensures that the fixed-time dynamics controller designed around it accomplishes the control objectives while sidestepping potential singularity issues that may arise from the use of existing non-smooth fixed-time control strategies.]]></description>
      <pubDate>Thu, 16 May 2024 16:37:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2369354</guid>
    </item>
    <item>
      <title>Dynamic Vehicular Delay Analysis between A Roundabout And A Pre-Timed Traffic Signal</title>
      <link>https://trid.trb.org/View/2281594</link>
      <description><![CDATA[This study deals with a dynamic delay analysis of roundabouts and pre-timed traffic signals. The main objective is to establish a relationship between vehicular delay, as the vehicle joins the queue until it leaves the intersection, and vehicle location as it joins the queue. A math model is proposed to predict the relationship. Another objective, in terms of vehicular delays, is to test the validity of two major hypotheses. Firstly, roundabouts perform better than pre-timed traffic signals at intersections with moderate queue lengths. Secondly, as the queues build up excessively pre-timed traffic signals perform more efficiently than roundabouts. A numerical experiment is established to test the validity of the model. The study will assist the concerned authorities to operate the existing conditions, particularly the roundabouts, more efficiently. It will also be beneficial to the traffic planners in making judicious decisions regarding control type at intersections.]]></description>
      <pubDate>Wed, 13 Mar 2024 09:27:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2281594</guid>
    </item>
    <item>
      <title>Combined flexible lane assignment and reservation-based intersection control in field-like traffic conditions</title>
      <link>https://trid.trb.org/View/2317436</link>
      <description><![CDATA[A concept called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC) was recently proposed for better management of directionally unrestricted traffic flows in a Connected and Autonomous Vehicles (CAVs) environment. In CADLARIC, vehicles must position themselves in a proper lane before they reach the downstream intersection, which enables the resolution of vehicular conflicts both between intersections and, as traditionally, within the intersection boxes. CADLARIC has shown very promising results, but it is quite infrastructurally demanding, requiring six lanes per intersection approach. To overcome this problem, we propose here Combined Flexible Lane Assignment and Reservation-based Intersection Control (CFLARIC), a more robust concept that offers a full spectrum of lane assignment possibilities in combination with the appropriate reservation-based intersection control. Three distinctive CFLARIC strategies are tested on a simulated three-intersection corridor from West Valley City, Utah. The efficiency and safety performance of the proposed CFLARIC scenarios are evaluated through a comparison with Fixed-Time Control (FTC) and Full Reservation-based Intersection Control (FRIC), both with conventional lane assignments. The results illustrate that CFLARIC scenarios: (i) outperform FTC and FRIC in terms of efficiency (delay and number of stops), and (ii) improve overall safety (by reducing a number of conflicting situations) when compared to FRIC. The findings of this study prove that flexible control concepts such as CFLARIC have a great potential to improve safety and efficiency in future CAV environment. Future research needs to define under which conditions and how the proposed concept could be partially implemented in field operations.]]></description>
      <pubDate>Fri, 26 Jan 2024 10:02:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2317436</guid>
    </item>
    <item>
      <title>A bi-Level programming method for SPaT estimation at fixed-time controlled intersections using license plate recognition data</title>
      <link>https://trid.trb.org/View/2289705</link>
      <description><![CDATA[Signal phase and timing (SPaT) information is a necessary input for traffic performance evaluation. However, current SPaT estimation studies mainly focus on estimation of cycle length or green time of a certain movement, and are realized mostly by floating car data whose data quality significantly affects the estimation accuracy. As license plate recognition (LPR) systems are becoming a widely implemented and reliable data source in China, in this study, a SPaT estimation method is proposed using the LPR data for fixed-time controlled intersections. The SPaT estimation problem is formulated as a bi-level programming model to find the optimal match between the phase boundaries and the LPR passing time series in the study period. Evaluation is done with an empirical case and compared with an existing method, results show that the estimation accuracies of the phase duration can reach 90.0%, outperforming the existing method and demonstrating great potential for practical application.]]></description>
      <pubDate>Thu, 30 Nov 2023 10:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2289705</guid>
    </item>
    <item>
      <title>An FTSA Trajectory Elliptical Homotopy for Unmanned Vehicles Path Planning With Multi-Objective Constraints</title>
      <link>https://trid.trb.org/View/2186720</link>
      <description><![CDATA[This article investigates a fixed-time simultaneous arrival (FTSA) problem in terms of the equilibrium of path lengths of unmanned vehicles. A novel trajectory elliptical homotopy method (TEHM) is designed to solve the FTSA problem of unmanned vehicles in a multi-objective constrained environment. Considering the constraints of obstacle avoidance and kinematics of unmanned vehicles, the trajectories elliptical homotopy is selected for path planning. The obtained trajectory homotopy guarantees obstacle avoidance and motion stability at the same time. To handle the non-cooperative and dynamic obstacle avoidance, a trajectory elliptical homotopy decomposition (TEHD) algorithm is proposed with an FTSA constraint. Based on the TEHM and TEHD, a multiple unmanned vehicle fixed-time regular-triangle formation algorithm is designed and implemented on real vehicles. Simulations and experiments validate the performance of the proposed methods and show how fixed-time arrival formation under obstacles and kinematic constraints was obtained.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:12:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2186720</guid>
    </item>
    <item>
      <title>Predictor-Based Fixed-Time LOS Path Following Control of Underactuated USV With Unknown Disturbances</title>
      <link>https://trid.trb.org/View/2186694</link>
      <description><![CDATA[This paper investigates the path following problem of underactuated unmanned surface vehicle systems suffering unknown disturbances. A fixed-time predictor is proposed to approximate the sideslip caused by disturbances, by which the prediction error can converge to zero in a fixed time. By selecting appropriate controller parameters, the upper bound of settling time is identified. And it's independent of initial conditions, compared with the finite-time predictor. In addition, a fixed-time line-of-sight (LOS) guidance law and a fixed-time heading controller are proposed. The cascade system composed of the guidance law and heading controller is fixed-time stable. All states of the Unmanned surface vehicles (USV) system can be stabilized globally in a fixed time, and USVs can follow the desired path accurately. Finally, numerical experiments are given to illustrate the effectiveness and feasibility of the proposed algorithm.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:12:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2186694</guid>
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