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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <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>Portable Tool for Periodic Evaluations of Intersection Signal Timings</title>
      <link>https://trid.trb.org/View/2725460</link>
      <description><![CDATA[The safety and efficiency of traffic-signal-controlled intersections rely on having appropriate timing signals for intersection users. Yellow, all-red, and crosswalk timings that are too low for users create the potential for intersection conflicts. Similarly, effective green times that are too small for volumes create long queues and congestion. However, signal timings are typically only evaluated every 2-5 years when signals are retimed through an often time-consuming process. The proposed solution is a portable device containing low-cost off-the-shelf radar and camera sensors that will record the trajectories of intersection users differentiating between pedestrians, bicycles and vehicles.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:36:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725460</guid>
    </item>
    <item>
      <title>Traffic signal optimization using hierarchical reinforcement learning: incorporating pedestrian dynamics and flashing light mode</title>
      <link>https://trid.trb.org/View/2703947</link>
      <description><![CDATA[This study introduces a novel Hierarchical Reinforcement Learning (HRL) based traffic control system, employing a two-level RL approach to optimize signal timing at urban intersections. The primary RL agent adjusts green phase durations, while the secondary agent determines transitions to flashing light mode based on intersection conditions to alleviate traffic during low-traffic periods. This system effectively integrates pedestrian and vehicular dynamics and ensures adherence to practical constraints like phase sequence and green time limitations. Comparative analysis with conventional methods shows our approach significantly reduces waiting times, vehicle stops, and fuel consumption. By using both synthetic and real-world data, our results demonstrate a robust improvement in traffic flow efficiency, offering promising implications for urban traffic management.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703947</guid>
    </item>
    <item>
      <title>Dynamic Straight–Right Lane Signal Timing Optimization to Decrease Pedestrian–Vehicle Conflict</title>
      <link>https://trid.trb.org/View/2685570</link>
      <description><![CDATA[The intersection, as the place where different traffic participants intersect, is most prone to traffic conflicts. Conflicts between right-turning vehicles and pedestrians are more common at intersection inlets under the traditional straight–right lane (TSRL). In order to reduce the time and frequency of conflicts between right-turning vehicles and pedestrians, this study proposes a presignal timing optimization scheme based on the dynamic straight–right lane (DSRL). The optimization scheme is able to satisfy two different intersection demands, traffic safety or traffic efficiency. First, this paper proposes the DSRL strategy to reduce conflicts and improve traffic efficiency. Second, the conflict characteristics of pedestrians and right-turning vehicles are analyzed, and the conflict intensity is quantified. Finally, the signal timing of the DSRL is optimized to reduce the timing of the intensity of traffic conflicts. The test platform based on the real intersection environment is built to verify the application effect of the optimization strategy proposed in this paper. The experimental results show that the total vehicle congestion time was reduced by more than 16.96%, while the pedestrian–vehicle conflict time decreased by more than 40 s. This experimental result can show that the traffic efficiency and safety of pedestrians and vehicles under the optimized design are improved.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:19:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685570</guid>
    </item>
    <item>
      <title>Effects of Traffic Signal Coordination on Traffic- and Emission-Related Evaluation Parameters</title>
      <link>https://trid.trb.org/View/2581606</link>
      <description><![CDATA[A good coordination of traffic signals can reduce waiting times, the number of stops and accelerations, and thus increase travel speeds, which can lead to a reduction in fuel consumption and air pollutant emissions. To investigate the effects of traffic signal coordination on traffic and emission parameters, simulation studies are advantageous over time-consuming field tests to quantify and evaluate quickly, safely and cost-effectively a large number of different input variables, such as intersection distances, traffic volumes and different signal control configurations. This paper describes the findings of an extensive simulation study in which a microscopic traffic flow simulation model was coupled with an emission model. Through the simulation of a range of scenarios, the model is used to investigate the influence of inter alia traffic volume, signal coordination schemes and signal parameters on carbon dioxide, nitrogen oxides and particulate matter emissions along an arterial road equipped with a series of traffic lights. Results showed that shorter distances between the signalized intersections led to about 20% higher emission values. Furthermore, different cycle times and their effects on emissions were investigated, whereby higher cycle times led to lower values of about 14% less emissions.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581606</guid>
    </item>
    <item>
      <title>Guidance for Selecting Pedestrian Safety Treatments at Signalized Intersections to Address Permitted Turn Conflicts</title>
      <link>https://trid.trb.org/View/2712202</link>
      <description><![CDATA[State departments of transportation (DOTs) and local agencies work to improve pedestrian safety at signalized intersections while maintaining efficient vehicle operations. Traditional signal timing practices often allow pedestrians to cross concurrently with permitted turning vehicles, which can create unsafe or stressful conditions for pedestrians, particularly at intersections with high turning volumes and speed or complex geometries. Agencies have implemented treatments such as leading pedestrian intervals (LPIs), delayed-turn strategies, and protected-only turn phases, but these applications are often applied without nationally consistent, data-driven guidance.

Existing resources identify available pedestrian safety treatments but provide limited guidance on when specific strategies are most appropriate. This can lead to inconsistent practices and difficulty balancing pedestrian safety improvements with operational impacts to vehicles.

The objective of this research is to develop a data-driven guide for selecting pedestrian safety treatments at signalized intersections to address conflicts with permitted turning vehicles. Using field-collected data, the research will evaluate treatments under varying traffic, geometric, and signal timing conditions, with results to help agencies identify appropriate strategies.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:11:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712202</guid>
    </item>
    <item>
      <title>Recent Advancements and Future Perspectives of Dynamic Fuzzy Controllers for Smart Traffic Signaling</title>
      <link>https://trid.trb.org/View/2581785</link>
      <description><![CDATA[Urbanisation has significantly changed people’s living standards recently, especially regarding mobility and transportation. As cities develop and expand, reliable transit has become necessary, increasing car ownership. Although increased private and public transportation provides convenience and flexibility to city dwellers, it also adds to substantial traffic congestion and environmental pollution. Therefore, more sophisticated traffic management systems are required to manage high traffic density, optimised signal timing and frequent congestion during peak times. Intelligent transportation systems include diverse technologies to enhance urban transportation networks’ safety, sustainability and quality. Adaptive traffic signal systems play a crucial role in the framework of intelligent transportation systems. Adaptability to changing traffic conditions such as traffic volume, waiting time, delay, congestion level and pedestrians’ movement can result in more efficient traffic management for the urban network. Much work has been done on adaptive traffic signal management. Fuzzy controllers are distinguished among the existing adaptive systems due to their exceptional ability to handle uncertainty, facilitate intuitive rule-based decision-making, and excel in human-like reasoning and adaptability, which is crucial in managing the complexities of urban traffic networks. This study explores designing adaptive rule-based fuzzy controllers for isolated traffic intersections and a city-wide road network, integrating dynamic controllers with smart city infrastructure.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581785</guid>
    </item>
    <item>
      <title>A Connected Vehicle-Based Contextual Stochastic Optimization Model for Real-Time Traffic Signal Timing</title>
      <link>https://trid.trb.org/View/2658866</link>
      <description><![CDATA[The emergence of connected vehicle (CV) technology has prompted research on leveraging real-time CV data for more effective traffic signal control. However, existing studies tend to i) utilize traffic parameters estimated by past CVs for signal optimization, resulting in a lag in signal provision, and ii) ignore the impact of parameter estimation/prediction errors inevitably introduced by the limited availability of CV data, which can significantly degrade the performance of signal control models that rely on these parameters. To fill these research gaps, this study proposes a CV-based contextual stochastic optimization (CV-CSO) model for real-time traffic signal timing, which combines a rolling-horizon optimization scheme with a sequential learning and optimization (SLO) paradigm to incorporate updated CV observations and explicitly handle potential parameter errors. The rolling-horizon optimization scheme optimizes two consecutive future cycles at each decision step and re-optimizes every half-cycle. The SLO paradigm, on the other hand, comprises two components: a Gaussian process regression that predicts the conditional distribution of the arrival rate and a contextual two-stage stochastic optimization model that handles the uncertainty of the arrival rate. Evaluation results demonstrate that the proposed CV-CSO model and its simplified deterministic model (denoted as CV-DO) outperform the traditional actuated control approach in terms of average vehicle delay, even with low-penetration-rate CVs. Notably, the consideration of arrival rate uncertainties in the CV-CSO model yields superior performance compared to the deterministic CV-DO model in various scenarios.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658866</guid>
    </item>
    <item>
      <title>Evaluating the Effect of Signal Adjustment on Rear-End Conflicts Using High-Resolution Event-Based Data</title>
      <link>https://trid.trb.org/View/2658802</link>
      <description><![CDATA[Signalized intersections are a critical component of road safety, yet rear-end crashes due to the yellow and red clearance phases remain a persistent problem. Traditional methods for setting these phases rely on kinematic models and video-based trajectory analysis, which suffer from high costs, weather/lighting limitations, and an inability to isolate signal timing impacts from confounding factors. This study introduces a novel framework for optimizing yellow and red clearance durations using Automated Traffic Signal Performance Measures (ATSPM). Analyzing over 446,000 traffic signal cycles across multiple intersections, the study examines rear-end conflicts during these phases and estimates the impact of signal timing adjustments using a causal forest. The results show that even small changes in yellow or red clearance durations can significantly affect rear-end conflict rates, with variations depending on intersection characteristics, traffic direction, and time of day. Unlike current standards, the proposed framework identifies context-specific optima, demonstrating how adaptive signal timing strategies can enhance safety without compromising traffic flow. This research expands the application of ATSPM beyond mobility optimization, offering a scalable and data-driven framework to improve intersection safety.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658802</guid>
    </item>
    <item>
      <title>A Multi-Objective Model for Traffic Signal Coordination Control With Queue Profile Estimation</title>
      <link>https://trid.trb.org/View/2659087</link>
      <description><![CDATA[The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659087</guid>
    </item>
    <item>
      <title>A Dynamic Zoning Cooperative Control Method for Urban Road Traffic Flow in a Connected Traffic Environment</title>
      <link>https://trid.trb.org/View/2659054</link>
      <description><![CDATA[The existing zoning collaborative control method cannot efficiently adapt to dynamic changes in traffic flow due to the fixed functional zoning of road segments. This study proposes the Dynamic Zoning COllaborative COntrol Method (DZCOM) for urban road traffic flow in a connected traffic environment. First, DZCOM dynamically divides the road segment into two functional zones, vehicle lane-changing and speed-adjustment zones, to integrate different driving behaviors. Second, to achieve rapid lane changes for multiple vehicles, a Multi-vehicle Cooperative Lane-changing Strategy based on Separate Lane-Speed Guidance (MCLS-SLSG) is designed, which rearranges the vehicles entering from the upstream intersection in longitudinal space, creating conditions for left- and right-turn vehicles to change lanes quickly. Finally, the trajectory of connected vehicles and the signal timing of intersections are jointly optimized based on the dynamic programming method to minimize average delay and ride comfort. The simulation results show that optimizing only the longitudinal trajectory of vehicles can reduce the average delay at intersections by 7.9% under moderate traffic volume. The joint optimization of vehicle trajectory and signal timing further reduced the average delay by 21.6%, demonstrating the effectiveness of DZCOM. Further research has shown that higher connected and automated vehicle penetration rates and speed limits can help reduce the average delay. Simultaneously, intersection spacing is another key factor restricting the operational effectiveness of the DZCOM; the method works best in the range of 400–600 m.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659054</guid>
    </item>
    <item>
      <title>Connected Vehicle Data-Driven Robust Optimization for Traffic Signal Timing: Modeling Traffic Flow Variability and Errors</title>
      <link>https://trid.trb.org/View/2658964</link>
      <description><![CDATA[Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. However, existing studies on CV-based signal control share a common shortcoming in that they all ignore traffic flow estimation errors in their modeling process, which is inevitable due to the sampling observation nature of CVs. This study proposes a CV data-driven robust optimization framework for traffic signal timing, accounting for both traffic flow variability and estimation errors. First, we propose a general CV data-driven deterministic optimization model (CV-DO) that can be widely applied to various scenarios, including under-/over-saturated and fixed-/real-time signalized intersections. Then, we propose a novel CV data-driven uncertainty set of arrival rates, circumventing the error-prone estimation process and accounting for both traffic flow variability errors. Finally, a CV data-driven robust optimization model (CV-RO) is formulated to explicitly handle arrival rate uncertainties. Employing the robust counterpart approach, this robust optimization problem can be converted to deterministic mixed-integer linear programming problems that can be solved efficiently with exact solutions. The evaluation results at a real-world intersection highlight the superior performance of the CV-RO model compared to the deterministic model and traditional methods across various scenarios. At different levels of traffic flow fluctuations, CV-RO can reduce delays by 5-26% compared to CV-DO at fixed-time signalized intersections with 0.1 CV penetration rate. The results on a real-time signalized network show that CV-RO can reduce 5% delays compared to the CV-DO model and 35.5% delays compared to actuated control at a 0.3 penetration rate.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658964</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>Macroscopic Flow Control of Connected and Automated Vehicles at Signalized Intersections</title>
      <link>https://trid.trb.org/View/2646689</link>
      <description><![CDATA[To fully leverage connected automated vehicle (CAV) technology for improving traffic flow at signalized intersections, this paper addresses the scalability limitations of traditional microscopic control methods. We propose a macroscopic connected automated flow control (CAFC) framework based on the cell transmission model (CTM), which formulates the vehicle sorting problem as a computationally efficient Mixed-Integer Quadratically Constrained Program (MIQCP). Numerical experiments, comparing our CAFC strategy against a traditional dedicated-lane benchmark, demonstrate a throughput improvement of approximately 63%. The framework also shows strong robustness in dynamic scenarios with mismatched traffic demand and signal timings, consistently outperforming a stronger, demand-responsive baseline. The results indicate that macroscopic flow control offers a scalable and highly effective alternative to microscopic methods for real-time traffic management in pure CAV environments.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646689</guid>
    </item>
    <item>
      <title>Real-Time Network-Level Traffic Signal and Trajectory Optimization with Connected Automated and Human-Driven Vehicles</title>
      <link>https://trid.trb.org/View/2646687</link>
      <description><![CDATA[This paper introduces a real-time framework designed to optimize intersection signal timing and vehicles’ trajectories across a network of intersections in a mixed environment of human-driven and automated fleets. The network-level optimization model is decomposed into intersection-level sub-models, whose decisions are coordinated through information exchange, aiming to push them toward the network model's optimal solutions. At each intersection, a bi-level framework addresses both the signal timing and trajectory optimization models. A specialized greedy heuristic algorithm is developed for the lower-level problem where optimal connected and automated vehicles (CAVs) trajectories are constructed for a given signal timing plan. At the upper level, all the feasible signal timing plans are created, and the system selects the most effective one to implement. The study integrates the entire solution process into a receding horizon framework to ensure efficient handling throughout the study period. A case study demonstrated the system's capability to adjust signals and trajectories effectively under various traffic demands and CAV market shares. Results showed a reduction in overall arterial delay correlating with higher proportions of CAVs. The proposed system delivered solutions in less than 70 ms, which is significantly faster than the half-second solving time steps, ensuring decisions were made quicker than in real-time.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646687</guid>
    </item>
    <item>
      <title>A Secure and Cost-Effective System for Acquiring Traffic Signal Data to Facilitate CAV Operations and Other Use Cases</title>
      <link>https://trid.trb.org/View/2696954</link>
      <description><![CDATA[Harvesting real-time data from traffic signal controllers supports connected and automated vehicle (CAV) operations. These benefits, however, require strong end-to-end security and a deployment model that adds minimal cost at each traffic intersection. In this paper, we present a secure, low- cost system that retrieves signal phase and timing (SPaT) data from traffic-signal controllers (TSCs). The system uses two microcontrollers connected through a unidirectional universal asynchronous receiver/transmitter (UART) link, establishing a “data-diode” channel. The first microcontroller interfaces with the TSC, extracts SPaT data, encodes it in base64, and sends it over the one-way UART link. The second microcontroller forwards the encoded data to a remote server via a cellular modem. The data-diode link enforces one-way communication, ensuring security of the TSC. Further, initial estimates suggest that the system achieves a 90% cost reduction per intersection compared to traditional dedicated short-range communication solutions, while assuring reliable data transmission.]]></description>
      <pubDate>Mon, 18 May 2026 10:59:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696954</guid>
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