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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Integrated Freeway Traffic Control Using Q-Learning With Adjacent Arterial Traffic Considerations</title>
      <link>https://trid.trb.org/View/2561833</link>
      <description><![CDATA[Numerous studies have shown the effectiveness of intelligent transportation system techniques such as variable speed limit (VSL), lane change (LC) control, and ramp metering (RM) in freeway traffic flow control. The integration of these techniques has the potential to further enhance the traffic operation efficiency of both freeway and adjacent arterial networks. In this regard, we propose a freeway traffic control (FTC) strategy that coordinates VSL, LC, RM actions using a Q-learning (QL) framework which takes into account arterial traffic characteristics. The signal timing and demands of adjacent arterial intersections are incorporated as state variables of the FTC agent. The FTC agent is initially trained offline using a single-section road network, and subsequently deployed online in a connected freeway and arterial simulation network for continuous learning. The arterial network is assumed to be regulated by a traffic-responsive signal control strategy based on a cycle length model. Microscopic simulations demonstrate that the fully-trained FTC agent provides significant reductions in freeway travel time and the number of stops in scenarios with traffic congestion. It clearly outperforms an uncoordinated FTC and a decentralized feedback control strategy. Even though the FTC agent does not control the arterial traffic signals, it leads to shorter average queue lengths at arterial intersections by taking into account the arterial traffic conditions in controlling freeway traffic. These results motivate a future research where the QL framework will also include the control of arterial traffic signals.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561833</guid>
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    <item>
      <title>Simulation Verification of Dynamic Speed Limit Control Method for Continuous Downhill Sections of Expressway</title>
      <link>https://trid.trb.org/View/2613309</link>
      <description><![CDATA[Focusing on the issue of how to calculate reasonable speed limits based on real-time changing traffic flow and intervene in traffic operation status when traffic accidents or reduced capacity occur on continuous downhill sections of highways, taking into account factors such as road alignment and meteorological environment, a dynamic speed limit model for continuous downhill sections of highways is proposed. This model is of great significance for improving the safety and traffic efficiency of highways under adverse conditions, helping to alleviate traffic congestion and reduce accident risks. The novelty of this study lies in the comprehensive consideration of multidimensional factors such as road alignment and meteorological environment, achieving dynamic adjustment of speed limit values. Analyze the VISSIM simulation data of continuous downhill traffic flow operation on the Chongqing section of the G65 Baomao Expressway, compare the analysis results of travel time, delay time, average speed, and other indicators in fixed speed limit and dynamic speed limit scenarios, and verify the effectiveness of the dynamic speed limit control method. The results show that in a rainy and foggy environment, when traffic accidents occur upstream of the slope bottom section of the continuous downhill section of the expressway, the dynamic speed limit on the continuous downhill section of the expressway reduces the total travel time of vehicles by 11.39%, the total delay time reduces by 30.51%, and the average speed increases by 12.97% compared to the fixed speed limit. The dynamic speed limit control method can better reduce traffic congestion and evacuation time and improve the traffic efficiency of the section. This study fills the gap in the current traffic management field regarding speed limit strategies for continuous long downhill sections, providing strong theoretical support and practical guidance for the safety and efficiency management of future highways.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613309</guid>
    </item>
    <item>
      <title>Meta-Reinforcement Learning with Hypernetworks for Variable Speed Limit Control under Adverse Weather and Work Zones</title>
      <link>https://trid.trb.org/View/2675553</link>
      <description><![CDATA[Variable speed limits (VSL) have been widely implemented to alleviate highway congestion and enhance operational efficiency. However, most existing studies focus on fixed traffic scenarios, making them inadequate when addressing uncertainties such as fluctuating traffic flows, extreme weather conditions, and construction-induced closures. Consequently, traditional VSL control strategies exhibit limited adaptability and generalization capability in unfamiliar scenarios. To overcome these limitations, this paper proposes a VSL control strategy based on Meta-Reinforcement Learning (Meta-RL) and Multi-Agent Proximal Policy Optimization (MAPPO) (Meta-MAPPO). This method leverages the meta-learning mechanism of Meta-RL and integrates a Hypernetwork module to dynamically adjust the network parameters of the control policy. By doing so, it adapts to diverse traffic scenarios and environmental disturbances, facilitating rapid policy transfer across scenarios and enhancing control performance. The training results demonstrate that Meta-MAPPO achieves faster convergence and superior model performance than MAPPO and Meta Multi-Agent Soft Actor-Critic (Meta-MASAC). Simulation experiments reveal that, compared with traditional feedback control methods and conventional multi-agent RL approaches, Meta-MAPPO exhibits significant advantages in unseen scenarios: it effectively mitigates traffic congestion and substantially reduces total travel time. The findings provide a more applicable solution for the practical implementation of VSL and offer valuable insights for further exploration of multi-agent methodologies in intelligent transportation systems.]]></description>
      <pubDate>Mon, 02 Mar 2026 13:29:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675553</guid>
    </item>
    <item>
      <title>Multi-Objective Optimisation of a Variable Speed Limit Control Strategy in a Tunnel Maintenance Work Zone of the Mountain Highway</title>
      <link>https://trid.trb.org/View/2643350</link>
      <description><![CDATA[Variable Speed Limit (VSL) control is essential for managing highway tunnel maintenance work, as it adjusts speed limits based on road conditions to regulate traffic flow. Developing a VSL control strategy that balances traffic efficiency and safety during maintenance can be challenging. This paper addresses this issue by proposing a VSL control strategy based on Model Predictive Control (MPC) that considers the spatial characteristics of traffic flow in a tunnel maintenance work zone. The strategy aims to minimise total travel time, reduce speed variance, and maximise traffic flow through a multi-objective optimisation approach using a Non-dominated Sorting Genetic Algorithm II (NSGA-II). With the Qinling Tiantai Mountain Tunnel selected as the experimental object, a simulation section is constructed based on the SUMO model with the measured data, and a comparative experiment of different speed limit control cycles in the maintenance work zone is designed. The results show that the method of this paper can effectively reduce the total travel time under the influence of maintenance operations by more than 17.5%, reduce the standard deviation of speed by about 22.1%, and enhance the traffic volume by about 7.8%, which can effectively improve the efficiency of road access and safety level.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643350</guid>
    </item>
    <item>
      <title>Revealed Tactical Driving Behaviour from Panel Floating Car Trajectory Data in an Extended Motorway Corridor</title>
      <link>https://trid.trb.org/View/2647778</link>
      <description><![CDATA[This study investigates revealed tactical driving behaviour along an extended motorway corridor using a unique four-week panel of floating-car trajectories comprising over 212,400 trips from 72,300 individual drivers. The analysis focuses on how drivers adapt their lane-changing and speed-selection strategies under fixed and variable speed limits (VSLs) and different congestion states. High-resolution 1 Hz trajectories are reconstructed to extract detailed time series of longitudinal and lateral movements, lane-change events (direction, timing, and duration), and speed stability indicators, all georeferenced to precise motorway segments and detector-derived traffic states. To uncover latent driving styles without prior labels, a bi-directional LSTM autoencoder with a Dirichlet Process Gaussian-Mixture (DP-GMM) prior is trained on segmented trajectory sequences, enabling unsupervised identification of recurring behavioural modes and automatic estimation of the optimal number of style clusters. The model architecture incorporates explicit masking to handle partial observations and integrates loop-detector flow and VMS control data to account for local traffic conditions and signage compliance. Results reveal consistent cross-driver heterogeneity: drivers exhibiting higher lane-change frequencies and stronger acceleration smoothness achieve shorter trip times but larger speed variability, while those maintaining steadier speeds display lower temporal efficiency yet greater positional stability. The derived style clusters demonstrate high internal consistency across weeks, indicating persistent, individual-specific tactical preferences rather than transient responses to congestion. By linking unsupervised style representations to control-context indicators, the study bridges microscopic trajectory modelling with behavioural heterogeneity in real traffic, providing a scalable framework for risk assessment, behaviour-adaptive traffic management, and calibration of next-generation trajectory-based safety models.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647778</guid>
    </item>
    <item>
      <title>Performance Investigation of the Variable Speed Limits on Highways by Considering the Driver Compliance in Mixed Traffic Flow</title>
      <link>https://trid.trb.org/View/2613268</link>
      <description><![CDATA[Variable speed limit (VSL) control is an effective traffic management strategy influenced by strategy choice and driver compliance (CL). With the rise of connected and autonomous vehicles (CAVs), the impact of VSL has shifted, necessitating consideration of CAV penetration and human-driven vehicle compliance. This study developed a highway capacity simulation platform to assess these factors across various compliance levels (20%, 45%, 80%, 100%) and CAV penetration rates (0%, 10%, 20%, 30%, 90%). Results indicate that high compliance improves VSL effectiveness, while low compliance destabilizes control. CAV penetration, especially at 90%, enhances vehicle volume and reduces travel time. Optimization-based VSL strategies consume more resources and emissions and are more sensitive to scenario changes than feedback-based approaches.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613268</guid>
    </item>
    <item>
      <title>Differential Variable Speed Limit Control for Temporal Traffic Flow States Using Multi-Agent Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2613049</link>
      <description><![CDATA[Variable Speed Limit (VSL) is an effective highway traffic management strategy, playing a crucial role in enhancing traffic efficiency and improving safety. However, accurately modeling and controlling traffic flow presents significant challenges due to its complexity and dynamism, affecting the implementation of VSL. This paper proposed a Differentiated VSL (DVSL) cooperative control method based on an improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to address this issue. In this approach, the speed limit controller for each lane is modeled as an agent utilizing an Actor-Critic architecture. The agents share the parameters of a shared policy network and employ a centralized training and decentralized execution strategy, enabling each agent to infer effective control policies based on its own observation collaboratively. A Transformer encoder is also introduced to perform temporal encoding (TE) of traffic flow states, enhancing the algorithm’s ability to capture and understand complex traffic environments. The effectiveness of the proposed method is validated through traffic bottleneck simulations conducted on the SUMO platform. The simulation results demonstrate that the proposed TE-MAPPO-DVSL control strategy significantly outperforms both the standard MAPPO-VSL control strategy and the no-control scenario. These findings highlight the potential of the TE-MAPPO-DVSL control strategy in optimizing variable speed limit systems and improving traffic efficiency, specifically in bottleneck areas.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613049</guid>
    </item>
    <item>
      <title>Variable Speed Limit Control Strategy for Hard Shoulder Running Sections Based on Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2612975</link>
      <description><![CDATA[Integrating different active traffic management measures has become one of the measures to alleviate bottleneck congestion in a mixed traffic flow environment, including human-driven vehicles (HVs) and connected and automated vehicles (CAVs). This paper proposes a variable speed limit (VSL) control strategy for hard shoulder running sections in a mixed traffic flow environment to address the bottleneck of highway entrance ramps. First, the rules for CAVs utilizing hard shoulder are proposed. The motivation and safety conditions are defined considering the use of emergency vehicles. Next, a VSL control strategy based on deep deterministic policy gradient (DDPG) is proposed, allowing setting different speed limits for different lanes. Finally, the effectiveness of the proposed strategy is verified through traffic simulation. The simulation results show that compared to the uncontrolled situation, the proposed strategy can reduce the average travel time and carbon dioxide emissions.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612975</guid>
    </item>
    <item>
      <title>Selection of meteorological and road surface features with highest impact on speed limit compliance</title>
      <link>https://trid.trb.org/View/2627773</link>
      <description><![CDATA[This study is based on the data collected by various traffic, road surface, and meteorological sensors at the Zagreb-Karlovac section of the A1 highway. The main goal of this study is to determine which number and combination of the features have the highest impact on speed limit compliance. A univariate feature selection method is used to select the meteorological and road surface conditions that are considered precursor features with the highest impact on traffic state where more vehicles comply with the speed limit. Furthermore, the models based on machine learning such as Random Forest are used to make predictions for each selected configuration of features. This introduces the causation evaluation of behavioral patterns within collected datasets defined by specific combinations of meteorological and road condition features. Results have shown that the selected configuration which includes 4 features achieves the most accurate predictions regarding speed limit compliance. Those features are the freezing points of the road surface, air temperature, water layer thickness, and road surface temperature.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627773</guid>
    </item>
    <item>
      <title>Coordinating Variable Speed Limits and Managed Lanes at On-ramp Bottlenecks: Policy Design, Performance, and Implementation Implications</title>
      <link>https://trid.trb.org/View/2643980</link>
      <description><![CDATA[Recurrent on-ramp bottlenecks remain a major source of delay and unreliability on urban freeways. We evaluate a coordinated variable speed limit (VSL) and managed lane (ML) policy that (i) meters mainline vehicle speeds by section and (ii) dynamically selects ML termini upstream and downstream so that ML remains outside the merge influence area to preserve weaving capacity. The research is established for fully connected and automated vehicles, where vehicles are expected to adapt to VSL and ML consistently. The model is solved with an option-critic controller of deep reinforcement learning, which selects VSL values for each mainline cell and on-ramp cell as well as ML termini at each control step, using measured road occupancy and vehicle speed states, where a comprehensive reward is developed to balance person delay and ramp queue dissipation. In microsimulation, coordinated VSL and ML reduces average vehicle delay, passenger delay, stop time, and CO2 by up to 57%, 48%, 42%, and 37%, respectively, compared with no-control baselines; gains are most robust at moderate flows between 4,500 to 5,500 veh/h and priority shares 20 to 60%, while passenger delay improves most at 40∼50% priority share. Lane-placement experiments show that ML performs best on the outside (ramp-adjacent) lane with its termini offset from the merge area. Findings may translate into design guidance: keep ML openings out of the merge influence area and coordinate VSL value to tune inflow into the weaving zone.]]></description>
      <pubDate>Tue, 06 Jan 2026 09:16:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643980</guid>
    </item>
    <item>
      <title>Integrating virtual obstacle into variable speed limit control for highways in connected traffic environments</title>
      <link>https://trid.trb.org/View/2606561</link>
      <description><![CDATA[Variable speed limits (VSL), as a global control mechanism, present challenges in accurately regulating traffic flow on highway local road segments. This paper incorporates a novel virtual obstacle (VO) control into VSL with time-varying control sections in a connected environment to balance global and local traffic management. VO control dynamically creates, adjusts, and removes virtual obstacles to form controllable artificial bottlenecks. A highway traffic flow network model is first developed based on a link queue model, considering the effects of VSL and VO. Variable link length describes the time-varying VSL control section and VO position. Then, a multi-objective function is introduced within the model predictive control framework. An improved genetic algorithm is employed to optimize speed limits and control sections of the VSL, as well as the position and width of the VO. Experimental results indicate that the proposed coordinated control method effectively alleviates congestion compared to the VSL control.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606561</guid>
    </item>
    <item>
      <title>Short-Term driving speed prediction under consecutive Variable speed Limits: An interpretable deep learning approach using Wide-Area trajectory data</title>
      <link>https://trid.trb.org/View/2625942</link>
      <description><![CDATA[Existing research on driver behavior under Variable Speed Limits (VSLs) primarily relies on simulations and loop detector-based cross-sectional traffic data, with limited studies using real-world microscopic vehicle trajectory data. This study proposes an interpretable deep learning framework for short-term driving speed prediction under consecutive VSLs control. Using wide-area trajectory data from a 2.2 km segment of the Shanxi Wuyu Freeway with two successive VSL signs, driver behavior was quantitatively analyzed. A Convolutional Neural Network − Bidirectional Long Short-Term Memory (CNN-BiLSTM) model enhanced with a Multi-View Spatio-Temporal Attention Mechanism (MSTAM) was developed to predict short-term speeds and assess the influence of spatiotemporal features on driver responses. Results show that heavy vehicles consistently decelerate under all VSL strategies, while light vehicles display minimal adjustment at 100 km/h and 80 km/h limits but respond more significantly at 60 km/h, with greater inter-driver variability. Drivers in the left lane respond more promptly and decisively than those in the right lane, with shorter response distances and greater speed reductions under all VSL conditions. Additionally, the second VSL sign generally exhibited superior regulatory effectiveness compared to the first VSL sign. Compared to the baseline CNN-BiLSTM, the proposed MSTAM model reduces MAE by 17.2 % and RMSE by 23.5 %. The MSTAM model further captures cognitively consistent spatiotemporal attention patterns, focusing on regulatory zones near VSL signs, sustaining elevated attention in the left lane, and selectively recalling past behaviors to simulate adaptive driver responses. These findings offer a scientific foundation for enhanced VSL deployment and lane-specific speed control strategies.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:57:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625942</guid>
    </item>
    <item>
      <title>A first-order link-based flow model with variable speed limits and capacity drops for freeway networks</title>
      <link>https://trid.trb.org/View/2601649</link>
      <description><![CDATA[First-order link-based traffic flow models are computationally efficient in simulating freeway networks. However, the standard link transmission models fall short of reproducing traffic phenomena such as capacity drop (CD). Moreover, traffic control measures such as variable speed limits (VSLs) control may change the fundamental diagram and should be captured by traffic flow models. This study proposes a first-order link-based flow model incorporating VSL and CD for freeway simulation. In the proposed model, the vehicle flow through each link is characterized by cumulative inflow and outflow, which are influenced by the time-varying free flow speed caused by the VSL at the link's upstream boundary. CD is modeled by incorporating the traffic state-dependent capacity at the freeway lane-drop positions. A node model is then developed to determine and regulate the flow propagation between adjacent links. Simulation experiments were conducted on freeways to evaluate the model's effectiveness. The results demonstrate its ability to accurately predict traffic operations under VSL and CD while maintaining a computationally tractable representation of flow propagation.]]></description>
      <pubDate>Fri, 14 Nov 2025 08:44:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601649</guid>
    </item>
    <item>
      <title>Decision-Support Tools: Development and Tutorial</title>
      <link>https://trid.trb.org/View/2611040</link>
      <description><![CDATA[This document describes the spreadsheet tools implementing the regression models developed in the main report (0-6859-1). The tools offer a user-friendly interface to evaluate the effectiveness of active traffic management (ATM) strategies for a given network. Three ATM strategies are considered: ramp metering, variable speed limit (VSL), and dynamic lane use control (DLUC). For each ATM strategy, the spreadsheet tool provides three cases based on the level of data availability: (1) The no-data case assumes that the agency has no data available to build or calibrate either a microsimulation or a dynamic traffic assignment (DTA) model. (2) The microsimulation-only case assumes that the agency has real-time data available to build and calibrate the microsimulation model but has no data available to develop a DTA model. (3) The DTA-only case assumes that the agency has access to strategic data to build a DTA model but only limited access to real-time data to build a microsimulation model. The framework used in developing the tools provides an approximate analysis and is useful in answering initial, planning level-questions. For example, a potential use of the tool could be in determining whether installing ramp metering on a particular on-ramp on IH 35 can help relieve congestion over the corridor in the long term. However, for assessing the precise impacts of an ATM strategy before its deployment, it is recommended that the agency develop a more detailed microsimulation model of the corridor if the tools developed in this project indicate potential benefits.]]></description>
      <pubDate>Wed, 05 Nov 2025 17:17:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611040</guid>
    </item>
    <item>
      <title>Evaluating safety effects of variable speed limit systems via joint modeling</title>
      <link>https://trid.trb.org/View/2604806</link>
      <description><![CDATA[Variable Speed Limits (VSL) systems are key components in Active Traffic Management System (ATMS). They dynamically and coordinately adjust speed limits to harmonize traffic flow thereby enhancing travel safety and reliability. The objective of this study is to evaluate the safety impacts of the VSL deployed on the I-24 Smart Corridor, Nashville, Tennessee, which went online in June of 2023. Safety indicators were measured by various crash outcomes, and they were typically modeled separately in previous studies. The potential correlations between collision type and its consequence represented by severity were often overlooked, leading to the underestimation of treatment effects. Hence, this study attempts to jointly model the rear-end, injury, and Property Damage Only (PDO) under the copula framework. The treatment effect, which is also known as the Crash Modification Factor (CMF) in before-after studies, is estimated by the Difference-in-Differences estimator in the marginal Negative Binomial (NB) model. Gaussian, Frank, and Clayton copulas were compared, and the best-fitting copula was used to estimate the model parameters. The results indicate that the copula models significantly outperform the separate NB models. The CMFs of rear-end and injury crashes resulting from VSL implementation are 0.677 and 0.686 respectively. Their scale-invariant correlation is very high (i.e., 0.91 out of 1), which suggests that the reduction in injury crashes may be attributed to the reduction of rear-end crashes. However, the change in PDO crashes was not statistically significant, possibly due to the shift from injury crashes to PDO crashes after traffic slowing down in adverse traffic conditions. Finally, the study results confirm the positive impact of implementing VSL systems and help justify future investments for candidate corridors.]]></description>
      <pubDate>Mon, 27 Oct 2025 09:34:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604806</guid>
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