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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>Variational Autoencoder–Generative Adversarial Network Traffic Prediction with Ramps</title>
      <link>https://trid.trb.org/View/2706294</link>
      <description><![CDATA[Accurate traffic flow prediction is fundamental for effective traffic management and the optimization of highway systems, particularly in complex scenarios involving highway ramps. This paper proposes a novel deep-learning method that integrates variational autoencoders (VAE) and generative adversarial networks (GAN) to enhance prediction performance and model interpretability. Traditional deep-learning approaches, such as CNNs and LSTMs, exhibit limitations in effectively capturing the inherent stochastic characteristics and spatial-temporal dependencies present in highway traffic data, especially under the influence of entry and exit ramps. To address these limitations, this research treats traffic prediction as a probabilistic distribution-fitting problem, utilizing the generative capabilities of GANs coupled with the regularizing effects of VAEs to stabilize and improve model training. The developed hybrid VAE-GAN framework was systematically evaluated using real-world data collected from the California performance measurement system (PeMS), which comprises traffic volume and speed information across multiple roadway sections. Comparisons with conventional deep-learning methods demonstrated that the VAE-GAN model achieved superior accuracy, particularly in predicting traffic conditions in scenarios involving multiple roadway segments and ramps. Specifically, results indicated that predictions leveraging multiple upstream sensor inputs significantly outperformed single-sensor predictions, and the presence of ramps notably reduced the accuracy of traditional CNN-based models. In contrast, the proposed VAE-GAN approach maintains robustness and accuracy in predictions across all tested conditions, effectively capturing the impact of ramps on traffic patterns. The findings underscore the importance of considering spatial-temporal distributions and probabilistic modeling in traffic forecasting, providing valuable insights for real-time traffic management strategies and intelligent transportation system design.]]></description>
      <pubDate>Tue, 18 Aug 2026 14:11:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706294</guid>
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    <item>
      <title>Marginal infrastructure costs and pricing in road and rail transport</title>
      <link>https://trid.trb.org/View/2752028</link>
      <description><![CDATA[This licentiate thesis examines marginal cost estimation and pricing in transport infrastructure, with empirical applications to Swedish roads and railways. The thesis consists of three papers addressing how infrastructure charges can be aligned with short-run marginal social costs. The first paper estimates marginal road renewal costs per heavy vehicle kilometre using panel data on approximately 1.3 million 100 meter road segments (1999-2022.. The second paper estimates short-run marginal road maintenance costs per heavy vehicle kilometre using administrative maintenance data at the Maintenance District Unit (MDU) level (2015-2024.. The third paper analyses optimal rail access charges in a vertically separated railway market, where marginal capacity costs reflect track congestion.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752028</guid>
    </item>
    <item>
      <title>Data-Driven Approach to Designing Safety Service-Patrol Vehicles in Florida</title>
      <link>https://trid.trb.org/View/2720601</link>
      <description><![CDATA[Florida’s Road Ranger program plays a critical role in incident management but lacks a standardized vehicle design, resulting in inconsistent visibility and public recognition. This study presents a stakeholder-informed, data-driven approach to developing consistent design principles for safety service-patrol (SSP) vehicles in Florida. The methodology included a nationwide survey of 44 agencies, a review of national and international practices, and a series of stakeholder workshops. Based on these inputs, three key design principles were established: enhancing safety, improving recognition, and strengthening branding. Three vehicle-design prototypes were created and evaluated through post-design surveys of district program managers. The preferred design featured a fluorescent yellow-green base with blue accenting and received broad support for its visibility and clarity. This methodological framework and study findings offer actionable guidance for agencies seeking to implement cohesive SSP vehicle designs that improve responder safety and reduce motorist perceptual ambiguity.]]></description>
      <pubDate>Sat, 04 Jul 2026 16:35:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720601</guid>
    </item>
    <item>
      <title>Decentralized Human-Like Ramp Merging Decision-Making and Control Based on a Stochastic Potential Game</title>
      <link>https://trid.trb.org/View/2617815</link>
      <description><![CDATA[Freeway ramp merging control in the mixed traffic consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) is one of the bottlenecks in the development of autonomous driving (AD) technologies due to complex multi-vehicle interactions. To address this, we propose a decentralized human-like control framework to help CAVs merge smoothly and interact more effectively with HDVs in a mixed-traffic environment. First, a stochastic potential game model is proposed to characterize the uncertainty of HDVs’ actions and optimize individual actions while considering their impact on the global system. A model predictive controller (MPC) is designed to minimize the accumulated cost of the potential function over a time horizon. Next, a distributed algorithm is developed to solve the proposed optimization problem in parallel while reducing the computational burden. To capture the characteristics of human driving behavior and help CAVs take more human-like actions, we calibrate the parameters in the proposed model using a real-world trajectory dataset. The performance of the proposed method is tested in a realistic two-lane merging zone scenario. Experimental results show that the proposed method enables CAVs to merge smoothly and ultimately improves traffic efficiency in merging zones. Additionally, the solution quickly converges to the optimal result using the proposed distributed algorithm, supporting its application in decentralized AD systems.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617815</guid>
    </item>
    <item>
      <title>Study on Vehicle Driving Stability under the Coupling of Ramp Circular Curve Radius and Pavement Wetness State</title>
      <link>https://trid.trb.org/View/2720230</link>
      <description><![CDATA[To clarify the coupled effects of ramp circular curve radius and pavement wetness state on vehicle driving stability, 26 ramp models with circular curve radii ranging from 30 to 280 m were constructed using Carsim software, and pavement friction coefficients were set as 0.85 (dry), 0.5 (wet), and 0.2 (rainfall) to simulate different wetness states. Lateral offset distance, lateral acceleration, and yaw rate were selected as key evaluation indicators to analyze vehicle dynamic response characteristics, and critical speed calculation models under different limit states were fitted. Two quantitative evaluation indicators, safety speed margin percentage (SSMP) and comfort speed margin percentage (CSMP), were proposed to characterize speed redundancy, and accident-prone zones of ramps were identified. Finally, the driving safety of four typical ramps at the Kunming Northwest Ring Expressway Hub Interchange was comprehensively evaluated. The results show that the pavement friction coefficient is the core external factor determining the ramp safe speed; when the friction coefficient decreases from 0.85 to 0.5 and 0.2, the allowable maximum speed (AMS) decreases by an average of 22% and 50%, respectively. Small-radius ramps (R < 100 m) and low friction coefficients (µ = 0.2) are dual risk factors, leading to severe vehicle yaw motion and increased skid/rollover risks. The main accident-prone zones are the transition sections from transition curves to circular curves, with narrower speed tolerance ranges on rainy days. For the four typical ramps, under wet condition (µ = 0.5), each has a certain safety speed margin, but under heavy rain (µ = 0.2), the safety speed of small-radius ramps (A, B) is lower than the design speed. The proposed SSMP and CSMP indicators and research results provide a theoretical basis and engineering reference for interchange ramp linear design, differentiated speed limit setting, and operational safety management.]]></description>
      <pubDate>Tue, 30 Jun 2026 08:56:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720230</guid>
    </item>
    <item>
      <title>Implementing Human-AI Collaboration for Enhancing TMC Freeway Operations</title>
      <link>https://trid.trb.org/View/2709250</link>
      <description><![CDATA[The rapid proliferation of artificial intelligence (AI) is fundamentally transforming real-time operations and decision-making across industries, including transportation. Human operators, while skilled and experienced, are inherently limited in their ability to process and interpret vast streams of data, especially under time pressure and uncertainty. These limitations can lead to overconfidence in judgment, susceptibility to cognitive biases, and challenges in maintaining situational awareness during complex or high-stress events. In contrast, AI excels at analyzing large datasets, identifying patterns, and providing objective, data-driven decision support, making it a powerful tool for augmenting human capabilities.

The concept of Intelligence Augmentation (IA) centers on leveraging AI not to replace human decision makers, but to enhance and amplify their reasoning, problem solving, and decision-making. IA emphasizes collaborative partnership, where AI systems handle computationally intensive tasks and humans contribute strategic oversight, contextual understanding, and ethical judgment. This approach preserves human agency while unlocking new levels of operational performance.

Traffic Management Centers (TMCs) serve as the central command hubs for monitoring and managing regional transportation networks, including freeways. TMCs rely on a diverse workforce to monitor, detect, and manage traffic incidents, congestion, and emergencies. As transportation systems become more complex and data-rich, the opportunity to integrate AI into TMC operations grows. AI can support TMC staff by automating routine analysis, predicting incidents, optimizing response strategies, and enabling proactive management of traffic flows. However, realizing these benefits requires a thoughtful framework for human-AI collaboration that addresses technical, organizational, and human factors.

The objective of this research is to develop a comprehensive technical guide for state departments of transportation (DOTs) and other transportation agencies to effectively incorporate human–AI collaboration into TMC freeway operations. This guide will provide actionable strategies, best practices, and implementation pathways to optimize decision-making, operational efficiency, and safety through the integration of AI technologies alongside human expertise.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:32:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709250</guid>
    </item>
    <item>
      <title>Lane‐change trajectory planning method for connected autonomous vehicles in freeway ramp merging areas</title>
      <link>https://trid.trb.org/View/2646697</link>
      <description><![CDATA[Freeway entrance ramp merging areas are often prone to traffic congestion and accidents, primarily due to the complex interactions between vehicles. The development of connected autonomous vehicles (CAVs) offers significant potential to mitigate these issues. This paper proposes a motion trajectory planning strategy that simultaneously enhances trajectory quality, computational efficiency, and driving comfort. The proposed method comprises three core modules: a hierarchical finite state machine-based lane-change decision model that accelerates decision-making by quantifying risks and opportunities in the S–T diagram; a lateral trajectory planning module that generates lateral paths using an improved grid sampling approach; and a longitudinal velocity planning module that employs a pruned dynamic programming algorithm to speed up the search process. The resulting lateral and longitudinal trajectories are then coupled to form an initial solution, which is further optimized by an iterative linear quadratic regulator under multi-objective constraints. Working in concert, these modules enable CAVs to perform real-time, smooth, and safe lane-change maneuvers. The experimental results indicate that the optimized lane-change method reduces time cost by approximately 20%, compared to the five-polynomial trajectory planning method. Moreover, the velocity and acceleration profiles are smoother than those produced by conventional dynamic programming algorithms, ensuring both trajectory smoothness and control precision. These improvements significantly enhance the safety and efficiency of the lane-change process.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646697</guid>
    </item>
    <item>
      <title>The Effect of Slower Managed Lanes on Future Lane Choice</title>
      <link>https://trid.trb.org/View/2691697</link>
      <description><![CDATA[Priced managed lanes (MLs) have been widely implemented in metropolitan areas to provide faster and more reliable travel while generating revenue to fund transportation improvements. Successful implementation of MLs relies on the careful selection of roadways on which to build MLs. This, in turn, relies on accurate travel demand models. Although existing models have been effective in forecasting aggregate travel demand and toll revenues, they often fall short in capturing individual lane decisions. Notably, MLs do not always deliver faster travel compared with adjacent toll-free lanes, called general-purpose lanes. Instances in which MLs have resulted in longer travel times can lead to user distrust and dissatisfaction. Despite this, there is no prior research investigating how negative ML experiences affect future lane choice behavior. Therefore, this study employs a logit regression analysis using 30 months of data from the Katy Freeway, Houston, TX to model individual lane choice decisions and examine the impact of slower ML events on subsequent travel behavior. The results indicated that drivers tend to pay tolls for using MLs when toll rates and speeds are higher. This indicates that travelers are generally willing to pay for faster travel. Conversely, travelers who experienced or observed slower ML events were less likely to use MLs in the future, revealing risk-averse behavior. Although this effect diminished over time as drivers tended to forget the event, repeated exposure to similar incidents reinforced the memory and prolonged the deterrent effect.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:26:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691697</guid>
    </item>
    <item>
      <title>Impact of Dynamic Ramp Closures on Urban Expressway Traffic: A Field Study</title>
      <link>https://trid.trb.org/View/2655533</link>
      <description><![CDATA[Ramp controls, such as ramp metering and closure, have been known as a promising strategy for alleviating traffic congestion in urban expressway networks. While ramp metering is a widely adopted practice globally, dynamic ramp closure has received less attention. The key distinction between the two lies in their signaling and effects on queue formation: ramp metering utilizes short-cycled signals to manage entering traffic, potentially causing long queues during peak arrival times, whereas dynamic ramp closure conveys a noncycled no-entry signal, prompting drivers to plan alternative routes in advance, thereby effectively preventing queues. This difference suggests that dynamic ramp closure has potential to avoid the gridlock issues at adjacent intersections due to spilled queues. In this study, we have developed and implemented the world’s first city-wide dynamic ramp closure system on urban expressways in Changchun, China, leveraging data from both fixed-point detectors and connected and automated vehicles (CAVs). Our field analysis revealed that dynamic ramp closure yields significant improvements, with expressway traffic speed increasing by over 20% and throughput by 10% compared to scenarios without control. Additionally, our research indicated that travel speeds on parallel ground streets remain relatively unaffected, suggesting an overall improvement over the entire road network. We also examined driver noncompliance with dynamic ramp closures and outlined how traffic departments educated the public and mitigated noncompliance. The successful implementation of this dynamic ramp closure system serves as a compelling demonstration of its effectiveness, providing valuable insights and potential solutions for other cities grappling with similar traffic congestion issues.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655533</guid>
    </item>
    <item>
      <title>A shared strategy of dedicated autonomous truck lanes for enhancing sustainability and efficiency in mixed freeway traffic</title>
      <link>https://trid.trb.org/View/2643237</link>
      <description><![CDATA[This paper presents the shared dedicated lane (SDL) strategy, designed to optimize dedicated lane utilization and enhance traffic flow in mixed environments with connected automated trucks (CATs) and human-driven vehicles (HDVs). The strategy consists of two components: the Platoon Optimal Formation (POF) model, which minimizes fuel consumption for CATs by determining the most efficient platoon formations, and the Two-Lane Cellular Automaton (TCA) model, which simulates vehicle movements, introduces lane-changing rules, and establishes CAT priority conditions to ensure efficient SDL utilization by HDVs. Numerical experiments were conducted on a multi-lane freeway to evaluate the SDL strategy under various traffic scenarios with different CAT demand ratios. The results show that the SDL strategy outperforms traditional approaches by improving traffic flow, fuel efficiency, and overall performance in mixed conditions. Specifically, it reduces fuel consumption by up to 10% under high CAT demand ratios and alleviates congestion while increasing HDV speeds during low CAT demand ratios.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643237</guid>
    </item>
    <item>
      <title>Control Strategy of Hard Shoulder Running at Intelligent Freeway Merging Areas Based on Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2674305</link>
      <description><![CDATA[The hard shoulder running (HSR) strategy has proven to be an effective measure to alleviate freeway traffic congestion. This paper proposes a smart HSR strategy based on deep reinforcement learning (DRL), referred to as DRL-S-HSR, designed to improve traffic efficiency in freeway merging areas within a connected environment. We propose the relevant assumptions and specific implementation rules for scenarios involving hard shoulder running in merging areas, along with the lane-changing motivations and safety conditions for connected and autonomous vehicles (CAVs) when temporarily using the hard shoulder. Furthermore, we also propose a vehicle collision avoidance method in merging areas based on the ECR-DQN algorithm, to avoid conflicts between vehicles on the hard shoulder and those on the on-ramp. A three-lane freeway in China is used as a case study. The study analyzes the overall traffic impact in terms of travel time, congestion patterns, carbon emissions, and driving comfort. Two key traffic conditions are also tested, including different CAV penetration rates and ramp inflows. The results show that the DRL-S-HSR strategy significantly alleviates traffic congestion on the freeway mainline under medium to high-density traffic flow conditions. In terms of carbon emissions, exhaust emissions throughout the entire traffic scenario are greatly reduced. Additionally, this strategy improves the driving comfort of merging areas. Overall, the proposed strategy demonstrates significant control effect and good applicability when the CAV penetration rate is between 10% and 20%, and the ramp inflow is less than 600 veh/h, providing a reference for future traffic management measures.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674305</guid>
    </item>
    <item>
      <title>Factors influencing lane choice behavior on Addis Ababa–Adama Expressway: Multinomial logit modeling evidence from Ethiopia</title>
      <link>https://trid.trb.org/View/2646864</link>
      <description><![CDATA[Purpose. This study investigates factors influencing lane choice behavior on the Addis Ababa–Adama Expressway, Ethiopia’s first controlled-access highway, to provide empirical evidence for transportation planning and highway design in sub-Saharan Africa, where such research is absent. Methodology. Video-based observational data were collected at five strategically selected sites across the 80-kilometer expressway corridor. A total of 45,924 vehicle observations were extracted through frame-by-frame manual analysis capturing lane choice, vehicle characteristics, traffic flow parameters, and lane-changing behavior. Multinomial logit models were developed for each site-direction combination (10 models in total) to quantify the relationships between explanatory variables and lane-choice probability, with Lane-3 serving as the reference category. Results. Analysis revealed a pronounced middle-lane bias, with 55.5% of traffic concentrated in Lane-2, while Lane-1 and Lane-3 received 24.7% and 19.1%, respectively. Average Speed Ratio exhibited consistently positive associations with outer lane selection (odds ratios: 3.01–66.19). Passenger cars demonstrated 3.00–60.14 times higher odds of selecting outer lanes compared to trucks, reflecting systematic vehicle stratification. Lane position of preceding vehicles showed negative associations (odds ratios: 0.12–0.36), indicating platoon avoidance behavior rather than following tendencies. Lane Utilization Factor demonstrated self-reinforcing effects exclusively for middle-lane selection. Theoretical contribution. This research provides the first empirical validation of utility-maximizing lane choice theory in sub-Saharan African expressway contexts, documenting platoon avoidance behavior and self-reinforcing lane utilization patterns with implications for traffic simulation model calibration. Practical implications. Findings inform lane-specific pavement design standards, capacity analysis methods that incorporate vehicle stratification effects, and traffic management strategies, including variable message signs and targeted enforcement, to improve operational efficiency and safety on Ethiopian expressways.]]></description>
      <pubDate>Tue, 24 Mar 2026 09:10:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646864</guid>
    </item>
    <item>
      <title>A method for evaluating the design consistency of heavy truck ramp based on high-frequency GPS data</title>
      <link>https://trid.trb.org/View/2675062</link>
      <description><![CDATA[In order to more scientifically and objectively reflect the actual operating conditions of heavy trucks on interchange ramps and evaluate their driving safety from the perspective of design consistency, this study employed full subset regression and stepwise multivariate regression methods to develop a heavy-truck operating speed model based on high-frequency GPS data collected on interchange ramps. Building upon the speed model and vehicle dynamics, lateral acceleration prediction models for heavy trucks were developed, and a lateral acceleration interference model for ramp curve sections was constructed based on acceleration interference theory. Accordingly, a safety evaluation method for heavy trucks on interchange ramps was proposed. This method was applied in a case analysis using actual geometric parameters from four interchange ramps. The findings offer theoretical support for alignment consistency design, safety evaluation, and the planning of traffic safety facilities on interchange ramps.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675062</guid>
    </item>
    <item>
      <title>Gardiner Expressway Strategic Sustainable Substructure Rehabilitation using Galvanic Protection</title>
      <link>https://trid.trb.org/View/2663605</link>
      <description><![CDATA[Due to aging, heavy daily usage, weather and de-icing-salt induced corrosion, the City of Toronto decided to undertake a major multi-year, multi-phased rehabilitation of the F.G. Gardiner Expressway to keep it operational for the future. The expressway was built between 1955 and 1966 across several areas of the City, including established neighbourhoods, two river mouths and the City’s downtown core. a Strategic Rehabilitation Plan was created by the City to deal with this large, complex and important project. This strategic plan divides the rehabilitation into 6 sections.  In 5 out of the 6 sections, depending on the conditions, either the superstructure or the deck of bridge structures will be replaced while substructure will be rehabilitated and re-used. To minimize disruption to commuters, the substructures were structurally repaired using a galvanic protection that will provide corrosion protection for the next 30 to 40 years without major repairs.  This presentation will first introduce the background of Gardiner Expressway and the strategic rehabilitation plan, present galvanic protection and discuss its application in the long-term rehabilitation and re-use of substructures.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663605</guid>
    </item>
    <item>
      <title>Evaluation and Correlation of the Dynamic Friction Tester on Iowa Pavement Surfaces</title>
      <link>https://trid.trb.org/View/2636168</link>
      <description><![CDATA[Frictional properties of pavement surface materials on roadway shoulders and highway ramps are of concern to state and county pavement engineers. Currently, the frictional properties of pavement surfaces are obtained via testing with a locked‐wheel skid trailer. Operational limitations prohibit the use of a locked‐wheel skid trailer on roadway shoulders and highway ramps. A Dynamic Friction Tester (DFT) can be used in these areas to test for frictional quality and characteristics of the pavement surface.]]></description>
      <pubDate>Mon, 02 Mar 2026 16:12:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636168</guid>
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