<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <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" />
    <description></description>
    <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>Coordinated transit signal priority control with queue length prediction in V2X environments</title>
      <link>https://trid.trb.org/View/2676121</link>
      <description><![CDATA[Transit Signal Priority (TSP) is a control strategy that can reduce the delay of transit systems. However, upon a high traffic volume, TSP may significantly increase delays for non-prioritized vehicles. Furthermore, designing TSP without considering signal coordination may result in ineffective progression for transit vehicles. In recent years, V2X (Vehicle-to-Everything) communications have been built and tested to collect vehicles’ locations and speeds. This study seeks to develop a coordinated TSP control logic in a V2X environment, where additional delays caused by TSP control are calculated with queue length prediction. By adopting a dynamic programming algorithm, the waiting time of transit vehicles and overall delays along the transit route are accounted for to determine the optimal TSP action. The simulated experiment based on a Light Rail Transit (LRT) system shows that the V2X coordinated TSP effectively mitigates the negative impact on general vehicles in non-priority directions upon heavy traffic flows.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676121</guid>
    </item>
    <item>
      <title>An AI-Based Traffic Light Control System Using Yolov10 with Emergency and Public Bus Prioritization: A Case Study in Baghdad</title>
      <link>https://trid.trb.org/View/2676037</link>
      <description><![CDATA[The increasing desire for people to own personal cars, combined with their reluctance to use public transportation, has led to traffic jams and delays in emergency vehicle arrivals. Traffic lights in densely populated cities pose a significant challenge because they rely on fixed or variable timings, yet are not particularly effective. As a result, they can worsen congestion or cause traffic jams instead of alleviating it. For example, a city like Baghdad faces severe traffic congestion, requiring intervention from traffic police. Additionally, there is no specific system in place for emergency vehicle passage, and public transportation remains ineffective, as people are hesitant to use buses due to longer congestion times and the difficulty in navigating, which is exacerbated by their larger size compared to private small cars. Unlike previous YOLO-based systems, our system integrates emergency vehicle and public transport buses prioritization. It adjusts timing based on vehicle type, number, and estimated speed, showing a 31.11% improvement in flow efficiency and reducing queue delays by 21.64% compared to fixed-time signal systems. The improved algorithm can recognize all four vehicle classes (fire trucks, ambulances, public transport buses, and cars) with an accuracy of 85-99%, depending on vehicle density and complex lighting conditions.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676037</guid>
    </item>
    <item>
      <title>Coordinated Multi-Agent Reinforcement Learning Method for Integrating Transit Signal Priority and Speed Guidance Control</title>
      <link>https://trid.trb.org/View/2659016</link>
      <description><![CDATA[Transit signal priority (TSP) is an effective approach to improve the service quality of public transit and increase its modal share. However, prioritizing transit vehicles can compromise overall traffic efficiency. To address this trade-off, speed guidance can be integrated to help non-transit vehicles pass through intersections smoothly. This paper proposes a novel multi-agent reinforcement learning framework, termed Co-ST, to coordinate TSP and speed guidance at the network level. Specifically, Co-ST features a joint policy architecture with dedicated actors for TSP and speed guidance, alongside a unified critic network that jointly evaluates and coordinates the dynamic interaction between the two control strategies. Co-ST incorporates a TSP-oriented multi-objective reward function balancing transit-specific goals, such as schedule deviation and bus priority, with general goals, including queue length, vehicle wait time, and travel speed. A novel co-optimization scheme is proposed to address the complexity inherent in multi-objective optimization. The effectiveness of Co-ST is validated through simulations on a real-world urban traffic network. Experimental results demonstrate that Co-ST consistently outperforms state-of-the-art benchmarks regarding key metrics such as intersection delays, queue lengths, and average vehicle speed, ultimately enhancing traffic efficiency for both transit buses and general traffic.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659016</guid>
    </item>
    <item>
      <title>Max-Pressure Signal Control with Transit Priority and Lane Blockage Mitigation: Considering Dwelling Buses and Permitted Left Turns</title>
      <link>https://trid.trb.org/View/2706338</link>
      <description><![CDATA[Urban traffic congestion remains a critical issue, intensifying with ongoing urbanization and increasing traffic volumes. While “max-pressure” (MP) control has emerged as a robust, decentralized strategy for optimizing intersection signals based on real-time queue dynamics, it traditionally overlooks transit vehicles and real-world complexities such as lane blockages (LB) because of buses dwelling at stops and permitted left-turn movements. This paper introduces an innovative extension of the MP paradigm for right-hand traffic systems, termed “MP-TSP-LB,” which explicitly integrates transit signal priority (TSP) through weighted priority schemes and accounts for LB caused by both dwelling buses and permitted left turns. The proposed approach modifies the conventional MP framework by introducing weight-based prioritization of buses without disrupting overall network stability. Additionally, the model incorporates effective lane capacities by estimating blockage probabilities, applying critical gap acceptance theory for permitted left turns and probabilistic dwell-time distributions for buses. The MP-TSP-LB model was rigorously tested through simulation on a 3 × 3 grid network using the Simulation of Urban Mobility simulation environment. Results indicate that the MP-TSP-LB model significantly enhances network performance across multiple metrics compared with baseline MP formulations. Sensitivity analysis demonstrates that the model reduces total waiting times and increases average travel speeds for both private vehicles and transit across various demand levels. Incorporating permitted left turns effects significantly improves performance under low-to-moderate demand levels, while modeling dwelling bus blockages becomes increasingly effective as transit service frequency intensifies.]]></description>
      <pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706338</guid>
    </item>
    <item>
      <title>Multimodal Signal Control in Coordinated and Free Transit Priority Corridor: SmartPGH Case Study on Pedestrian Timing Strategies</title>
      <link>https://trid.trb.org/View/2706172</link>
      <description><![CDATA[This study supports the City of Pittsburgh’s SmartPGH initiative by evaluating advanced pedestrian signal strategies designed to enhance safety and multimodal mobility on high-demand urban corridors. The objective is to identify the best trade-offs between pedestrian timing treatments and signal control modes (actuated free versus coordinated) for varying pedestrian and vehicular demand throughout the day. While prior research has implemented clearance extension logic in simple midblock environments, this study advances the field by applying and testing pedestrian strategies in complex, multiphase intersections using real-world controller logic and time-of-day profiles. The evaluated strategies include pedestrian protection logic (PPL), passive pedestrian detection (PPD), dynamic pedestrian-exclusive phases (DEP), and pedestrian recall (PR). A novel contribution is the implementation of PPL within a fully actuated controller, which selectively delays conflicting vehicle phases to protect pedestrians still crossing, without interrupting concurrent vehicle movements. Evaluation is conducted through a software-in-the-loop simulation framework that integrates microsimulation with Maxtime signal controllers. Results demonstrate that PPD scenarios consistently offer the lowest person delay across a.m., midday, and p.m. periods while maintaining strong vehicle and bus performance. The addition of PPL and DEP further improves pedestrian service, particularly under high pedestrian volumes, with moderate trade-offs in vehicle delay. These findings provide actionable insights for transportation agencies seeking to implement intelligent pedestrian signal strategies as part of broader smart city goals to promote safety and efficiency.]]></description>
      <pubDate>Wed, 27 May 2026 10:48:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706172</guid>
    </item>
    <item>
      <title>Analysis of bus dwell times from automated passenger count data and the impact of dwell-time variability on the performance of transit signal priority</title>
      <link>https://trid.trb.org/View/2692331</link>
      <description><![CDATA[The design of transit signal priority (TSP) systems requires knowledge of dwell-time distributions at bus stops within the block. Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson’s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692331</guid>
    </item>
    <item>
      <title>Optimizing corridor-level transit efficiency: multi-agent reinforcement learning with multi-discrete actions leveraging connected vehicle data for transit priority</title>
      <link>https://trid.trb.org/View/2672558</link>
      <description><![CDATA[Enhancing transportation system efficiency is a critical challenge, and transit signal priority (TSP) has been widely studied as a strategy to address it. However, its implementation remains difficult because simple rule-based approaches can negatively impact other traffic, while more sophisticated methods impose a significant computational burden for real-time decision-making. Advancements in connected vehicle (CV) technology and reinforcement learning (RL) provide opportunities to develop more adaptive and intelligent TSP strategies, yet existing research lacks corridor-level exploration and fails to fully utilize detailed CV data for optimized decision-making. This study introduces a multi-modal signal control framework designed for CV environments, aiming to optimize corridor-level transit efficiency while minimizing disruptions to regular traffic. To tackle the signal control optimization problem across multiple intersections, a multi-agent RL framework is employed. A multi-discrete action space is introduced to enable more flexible decision-making, and a reward function leveraging real-time CV data is utilized to grant priority to transit vehicles based on onboard passenger numbers. Extensive evaluations are conducted using both hypothetical and real-world corridor simulation testbeds. Results demonstrate that the proposed framework, particularly with the integration of the multi-discrete action space, achieves superior performance in bus services while maintaining satisfactory traffic conditions for regular vehicles. Furthermore, sensitivity analysis reveals that the framework effectively adapts to dynamic variations in passenger occupancy and bus arrival headways, making it a promising candidate for real-world deployment.]]></description>
      <pubDate>Fri, 15 May 2026 09:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672558</guid>
    </item>
    <item>
      <title>Multi-Bus-Line Joint Operation Strategy of Optimizing Bus Speed and Intersection Signal Priority to Minimize Passengers Waiting Time</title>
      <link>https://trid.trb.org/View/2686199</link>
      <description><![CDATA[The rapid increase in the number of vehicles reduces the efficiency of transportation networks in modern big cities. Thus, minimizing passengers waiting time by bus has become an inevitable approach. Through intelligent bus systems and Dedicated Bus Lanes (DBLs), jointly optimizing bus speed and intersection signal priority has become a feasible research objective for multi-bus-lines. Moreover, the length of Beijing (China) DBLs will be 1020 km in 2022. Considering the requirements of the Beijing Bus Group, a problem model is formulated, including multi-bus-lines, time-varying passenger flow, bus-speed-control only on DBLs, and intersection signal control. In this study, the real-time framework of the multi-bus-line joint operation strategy with the Transformable Salp Swarm Algorithm (TSSA) is proposed. Moreover, the small optimization interval effectively reduces the impact of bus-speed-control inaccuracy and the errors between the joint optimization scheme and actual operation states. In the real-time framework, only the speed of the bus traveling on DBLs could be guided in the form of real-number speed, and this bus-speed scheme is safe. Additionally, the strategy could compensate for the travel time in the non-priority direction after buses pass through intersections, and this is effective to avoid traffic congestion. As the online optimization algorithm, TSSA simulates the grouping activity of salp swarms. Based on actual data from Beijing Bus Group, 6 test problems are constructed, and the joint operation strategy outperforms others.]]></description>
      <pubDate>Tue, 12 May 2026 16:56:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686199</guid>
    </item>
    <item>
      <title>Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach</title>
      <link>https://trid.trb.org/View/2596544</link>
      <description><![CDATA[Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.]]></description>
      <pubDate>Wed, 06 May 2026 15:21:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596544</guid>
    </item>
    <item>
      <title>Mitigating General Transit Feed Specification Message Delays Using Time Series Prediction for Transit Signal Priority</title>
      <link>https://trid.trb.org/View/2696109</link>
      <description><![CDATA[As a preferential treatment at signalized intersections, Transit Signal Priority (TSP) remains a key technology for enhancing transit performance. Recently, TSP systems based on General Transit Feed Specification (GTFS) Realtime have gained traction in the market, mainly because of their low implementation and maintenance costs. However, leveraging GTFS Realtime messages for TSP presents significant challenges, particularly because of two types of message delays: (1) high latency; and (2) long update intervals. Building on previous work that introduced regression models to compensate for message latency, three new machine learning models are proposed to more accurately predict future vehicle locations while mitigating these delays. To overcome the limitations of earlier regression approaches, a long short-term memory architecture for single-step prediction was developed, a long short-term memory architecture for multistep prediction was developed, and a Transformer-based architecture for multistep time series prediction was developed, which can address interval updating issues. The experimental results show that all three proposed models significantly outperform both previous regression models and five baseline statistical methods. These advancements improve the reliability and accuracy of GTFS-based Automatic Vehicle Location, reinforcing its role as a dependable data source for cloud-based TSP systems.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:19:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696109</guid>
    </item>
    <item>
      <title>Urban Priority Pass: Fair signalised intersection management accounting for passenger needs through prioritisation</title>
      <link>https://trid.trb.org/View/2692489</link>
      <description><![CDATA[Over the past few decades, efforts of road traffic management and practice have predominantly focused on maximizing system efficiency and mitigating congestion from a system perspective. This efficiency-driven approach implies the equal treatment of all vehicles, which often overlooks individual user experiences, broader social impacts, the fact that users are heterogeneous in their urgency and that they experience different costs when being delayed. Even though they are the major bottleneck for traffic in cities, no dedicated instrument enables prioritization of individual drivers at intersections. The Priority Pass is a reservation-based, economic controller that expedites entitled vehicles at signalized intersections, without causing arbitrary delays for non-entitled vehicles and without affecting transportation efficiency de trop. Particularly applicable to large, congested cities with rich sensor infrastructure, the prioritization of vulnerable road users, emergency vehicles, commercial taxi and delivery drivers, or urgent individuals, this approach can enhance road safety, and achieve social, environmental, and economic goals. A case study of Manhattan demonstrates the feasibility of individual prioritization (up to 40% delay reduction), and quantifies the potential of the Priority Pass to gain social welfare benefits for the people. A market for prioritization could generate up to $ 1 million in daily revenue for Manhattan, and equitably allocate delay reductions to those in need. The findings provide a foundation for integrating user-centric prioritization mechanisms into emerging smart city traffic management systems, supporting data-driven policymaking and equitable mobility planning. Source code and material available on GitHub https://github.com/DerKevinRiehl/urban_priority_pass.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692489</guid>
    </item>
    <item>
      <title>Operational control approach for connected-autonomous-bus line in mixed public transit environment</title>
      <link>https://trid.trb.org/View/2643307</link>
      <description><![CDATA[The use of connected and autonomous buses (CABs) is growing, and surface public transit is being transformed into a mixture of manually driven buses (MDBs) and CABs. We aim to establish a hierarchical optimal control model to ensure the efficient operation of CABs in a mixed public transit environment. The first level outputs the planned speed to improve the headway uniformity. Considering the operational uncertainties of MDBs and the output parameters from the first level, the second level minimises the speed changes and the number of CABs entering the node queue and determines the speed control and signal priority schemes. We propose a solution algorithm based on rolling optimisation and establish simulation cases based on the bus lines in Beijing. Compared to both the uncontrolled scheme and the single-speed control scheme, this approach significantly reduces headway deviation and the number of CABs entering the node queue.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643307</guid>
    </item>
    <item>
      <title>Coordinated dual-objective transit signal priority: a deep reinforcement learning approach</title>
      <link>https://trid.trb.org/View/2643284</link>
      <description><![CDATA[Transit Signal Priority (TSP) has been widely used for reducing transit delays for decades. Since reliability is valued equally as travel time, a dual-objective coordinated (DC) TSP is developed to adaptively optimize transit headway adherence and travel time simultaneously over consecutive intersections. This is the first attempt at using a centralized agent deep reinforcement learning (RL) framework in solving a coordinated TSP optimization problem. Decentralized control algorithms using multi-agent RL are also developed as baseline scenarios. TSP algorithms are trained and tested in a stochastic microsimulation environment within Aimsun Next for a corridor segment in Toronto with a transit line experiencing high service variability. DC TSP demonstrates a clear promise in reducing headway variability and travel time at different traffic levels. It highlights the importance of coordinating TSP actions at consecutive intersections. It is also shown to be robust, providing effective control under various configurations of bus stop locations.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643284</guid>
    </item>
    <item>
      <title>Emergency Vehicle Priority Signal Control Based on SumTree DDQN Model</title>
      <link>https://trid.trb.org/View/2613016</link>
      <description><![CDATA[Traditional methods used to control traffic signals for emergency vehicles, like signal preemption and fuzzy logic, can cause problems for regular vehicles and may not work well when there are no emergency vehicles around. This paper proposes a SumTree DDQN deep reinforcement learning model that integrates Double Deep Q-Network (DDQN) with a SumTree experience replay buffer to achieve priority signal control for emergency vehicles. This method selects actions that maximize reward values based on the spatial states of emergency and non-emergency vehicles, optimizing traffic signal phase transitions to facilitate rapid passage for emergency vehicles. Simulations near Tongji Hospital in Wuhan demonstrated reduced emergency vehicle delays to 3.5, 3.1, and 2.6 s in various traffic scenarios, improving intersection performance significantly.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613016</guid>
    </item>
    <item>
      <title>Transit Reliability Improvement and Performance System (TRIPS) System Engineering Management Plan Concept of Operations (ConOps)</title>
      <link>https://trid.trb.org/View/2663122</link>
      <description><![CDATA[This document provides a rationale for the expected operations of a centralized Transit Signal Priority (TSP) system deployment along various corridors within Santa Clara County. It documents the outcome of stakeholder discussions and consensus building that has been undertaken to ensure the system implemented is operationally feasible and has stakeholder support. The intended audience of this document includes the system operators, administrators, decision-makers, nontechnical readers, and other participating stakeholders who will share the operation of the system or be affected by it.]]></description>
      <pubDate>Thu, 12 Feb 2026 08:52:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663122</guid>
    </item>
  </channel>
</rss>