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    <title>Transport Research International Documentation (TRID)</title>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <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>
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    <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>Influence of Wrong-Way Driving (WWD)-Related Dynamic Message Signs (DMS) on Traffic Behavior</title>
      <link>https://trid.trb.org/View/2562240</link>
      <description><![CDATA[Wrong-way driving (WWD) crashes, which occur when vehicles travel against traffic, often result in severe injuries or fatalities. Despite full access control, freeways remain vulnerable to these incidents. Transportation agencies increasingly use Dynamic Message Signs (DMS) to alert right-way drivers of real-time WWD events, yet their effectiveness remains underexplored. This study analyzes driver reactions to WWD-related DMS activations on freeways using traffic speed data from the Regional Integrated Transportation Information System (RITIS) and SunGuide incident data. Statistical methods, including Z statistics and linear regression, were used to evaluate speed changes near DMSs during WWD events. Additionally, the study examined roadway and environmental factors associated with these speed changes. Results show traffic speed reductions in 74.3% of WWD events, with significant drops in 36.5% of cases at a 95% confidence interval (CI). Mean traffic speed consistently decreased during DMS activations, highlighting their effectiveness in influencing driver behavior. Factors such as the number of lanes and seasonal variations also influenced speed change patterns. Recommendations are provided to enhance real-time responses to WWD incidents on freeways.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562240</guid>
    </item>
    <item>
      <title>Connected Vehicle Data for Freeway Congestion Analysis: Necessity versus Luxury for Transportation Engineers</title>
      <link>https://trid.trb.org/View/2625271</link>
      <description><![CDATA[This study evaluates the utility of disaggregated connected vehicle probe (DCVP) data in augmenting existing incident detection workflows in freeway congestion analysis by comparing it with Bluetooth and aggregated probe (AP) data. Using a year-long dataset from a 60 km segment of I-20/59 in Alabama, more than 80 verified congestion events were analyzed to assess differences in congestion onset detection, queue-length estimation, and shockwave behavior. Shockwave analysis revealed that DCVP data captured queue dissipation more dynamically, indicating greater sensitivity during recovery phases. While AP data produced smoother estimates of shockwave speeds, they often failed to capture short-duration or spatially limited congestion events, especially those that resolved quickly or did not propagate far upstream. Bluetooth data, although more stable, tended to smooth out severe speed drops during congestion, offering a less sensitive view of traffic variability. In validating regional traffic management center (RTMC) incidents logs, DCVP data detected congestion earlier in 77% of events, with an average lead time of approximately 5 min. They also showed better spatial alignment with reported incident locations, with a mean deviation of 0.9 km compared to 1.3 km for AP data. These findings demonstrate that DCVP data can serve as a valuable resource for enabling RTMC operators to detect disruptions more quickly and accurately, reducing reliance on external alerts. Their ability to deliver high-frequency, real-time insights also positions them as a potential enabler for future automation in incident detection and response.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625271</guid>
    </item>
    <item>
      <title>Stop-and-go wave super-resolution reconstruction via iterative refinement</title>
      <link>https://trid.trb.org/View/2602029</link>
      <description><![CDATA[Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, WaveX (Ji et al., 2025a), comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic state refinement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic state refinement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-source our dataset, trained model and code to enable further research and applications.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602029</guid>
    </item>
    <item>
      <title>Guidelines for Applying Managed Lane Strategies to Ramps</title>
      <link>https://trid.trb.org/View/2582255</link>
      <description><![CDATA[Current funding constraints and difficulty in gaining environmental and public approval for large-scale construction projects has forced the Texas Department of Transportation (TxDOT) to continue considering alternative solutions to roadway widening to mitigate congestion. One area for potentially improving freeway performance is ramp locations. Current ramp treatments only address point demand. Applying managed lane operational strategies to ramps could maximize existing capacity, manage demand, offer choices, improve safety, and generate revenue. This project will investigate the application of these demand management strategies to mainlane ramps and managed lane ramp operations during the peak period: i.e., “managed ramps.” Such strategies could include peak-period use of both mainlane or managed lanes entrance and exit ramps by user group, possibly influencing mode choice, enhancing mobility, improving safety in a freeway corridor, and helping ensure the integrity and free-flow operations of a managed lanes facility. This document provides guidance on identifying when to consider managed ramps based on relevant factors including target users in the corridor, congestion level, ramp spacing/density, ramp volumes, accident history, etc.]]></description>
      <pubDate>Sat, 22 Nov 2025 17:17:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582255</guid>
    </item>
    <item>
      <title>A Guidebook for Effective Use of Incident Data at Texas Transportation Management Centers</title>
      <link>https://trid.trb.org/View/2582234</link>
      <description><![CDATA[This guidebook provides methodologies and procedures for using incident data collected at Texas transportation management centers (TMCs) to perform two types of analysis – evaluation/planning analysis and predictive analysis. For the evaluation/planning analysis, this guidebook provides (1) guidelines for reporting incident characteristics, (2) methods for analyzing hot spots, (3) methodologies for estimating incident impacts, and (4) guidelines and procedures for calculating performance measures. For predictive analysis, this guidebook describes (1) methodologies for predicting incident duration using incident characteristics and (2) methodologies for predicting incident-induced congestion clearance time using combined historical and real-time traffic data. Examples of applications and results from the methodologies and procedures described are provided throughout this guidebook.]]></description>
      <pubDate>Mon, 17 Nov 2025 10:06:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582234</guid>
    </item>
    <item>
      <title>Traffic Management and Planning for Freeway Emergencies and Special Events</title>
      <link>https://trid.trb.org/View/2608452</link>
      <description><![CDATA[A 1-day conference was held on January 13, 1985, at the Omni-Shoreham Hotel in Washington, D.C. with the objective of providing an understanding of the actions and planning that can be undertaken in advance to reduce the impact of congestion due to freeway incidents and special events. Some of the major points under discussion concerned freeway incidents. For example, a one-lane blockage on a three-lane section of freeway reduces capacity by 50 percent, but physical blockage of two of three lanes reduces capacity by about 80 percent. Both special events and incidents on freeways can be more easily and quickly removed and traffic flow restored if incident management or corridor management teams are used. Conference attendees participated in the examination of three case studies. A similar conference is being planned for January 1986.]]></description>
      <pubDate>Sat, 18 Oct 2025 18:52:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608452</guid>
    </item>
    <item>
      <title>Establishment of Construction Safety Early-Warning System for Mountainous Freeways</title>
      <link>https://trid.trb.org/View/2203751</link>
      <description><![CDATA[In recent years, with the rapid development of the national economy, frequent construction accidents on mountainous freeways result in a great loss of national property and become the focus of the nation's attention. It is urgent that a construction safety early-warning system for freeways in mountainous regions be established in order to radically improve the situation of safety management. Major hazard identification and risk assessment is still lacking in the field. In order to solve this problem, a construction safety early-warning system for mountainous freeways was established. The targets, function, input and output of system were analyzed. The safety early-warning indicator system was made up of people, materials, environment, and management factors. The weights of factors were calculated by an analytic hierarchy process. The safety status grade was determined by a safety early-warning model. Results revealed that the safety early-warning system can reflect the safety status in freeway construction of mountainous regions objectively.]]></description>
      <pubDate>Thu, 16 Oct 2025 12:01:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203751</guid>
    </item>
    <item>
      <title>Safe Spacing Research Based on the Theory of Cellular Automation on Freeways in Foggy Weather</title>
      <link>https://trid.trb.org/View/2203759</link>
      <description><![CDATA[The random disturbance parameter γ is introduced into the cellular automaton (CA) model of traffic flow to simulate the cases of car-stopping, hitting the car ahead, and other vehicular crashes occurring on the freeway. By analyzing the disturbance vehicles in the driving environment of different visibilities of foggy weather, the correlations of limited speed and the safe spacing for optimum visibility are developed. The safe spacing and average safe velocity of the vehicle under different visibilities are simulated and analyzed, thus providing the theoretical gist for freeway administrative departments to establish and implement the management strategies.]]></description>
      <pubDate>Tue, 14 Oct 2025 16:52:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203759</guid>
    </item>
    <item>
      <title>Enhancing the estimation of freeway level-of-service thresholds with travelers’ perceptions</title>
      <link>https://trid.trb.org/View/2570871</link>
      <description><![CDATA[Consistency between travelers’ perception of trip quality and the level of service (LOS) classification, as defined by the Highway Capacity Manual, of traffic operational conditions is crucial for both highway system management and roadway improvement funding decisions. However, research on measuring travelers’ perceptions of traffic operational conditions is a relatively immature field and methods to obtain such data are still evolving. Previous studies have employed various techniques to gather data on travelers’ perceptions, including driver interviews at rest stops, focus groups, and rating pre-recorded driving video clips. The challenge with these methods, however, lies in collecting a large sample of responses and/or providing a comprehensive set of traffic operational combinations in pre-recorded videos.This paper presents a novel approach to measuring travelers’ perceptions of roadways, which addresses the limitations of previous studies. The authors' approach combines realistic 3-dimensional traffic stream visualizations with an online survey to reach a broad audience. The number of service levels and corresponding density threshold values are determined through a combination of 𝑘-means clustering and logistic regression. A case study demonstrates the feasibility and effectiveness of the proposed approach based on the responses of 977 Brazilian participants who watched and rated 10,228 video clips.]]></description>
      <pubDate>Fri, 18 Jul 2025 15:45:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570871</guid>
    </item>
    <item>
      <title>Estimation of Passenger Car Equivalents on Basic Freeway Segments from Field-Observed Traffic Data</title>
      <link>https://trid.trb.org/View/2573092</link>
      <description><![CDATA[Passenger car equivalents (PCEs) represent the effects of heavy vehicles on traffic operations. PCE estimation is based on equating the passenger-car-only flow rate to the mix-fleet flow rate such that it results in the same performance for the selected measure. Most existing methods rely on simulation to generate PCE values. However, this approach generates PCEs using the truck characteristics of the simulation rather than local field conditions and heavy vehicle types. Most existing methods estimate the marginal effect of adding one specific truck in the traffic stream assuming relatively high volumes, which results in higher PCE values. This study proposes a new method for estimating PCEs using field-observed traffic data considering the impact of each truck type for a broad set of flows, not just their marginal effect at high flows. Based on density equivalency, the maximum likelihood estimation is used to estimate the means and the variances of the PCE values from traffic data collected at 10 sites on the national motorways of Thailand. When aggregated across all observed percentages of heavy vehicles and flow rates, the PCE values are 1.46 (trucks with length between 5.2 and 13.0?m) and 2.08 (trucks with length of 13.1?m or longer). For low to medium percentage of trucks, the PCEs become lower after the onset of oversaturation. However, for high percentages of trucks the PCEs tend to be higher after the onset of oversaturation. The proposed methodology can be applied using data from other locations to estimate the corresponding PCEs from field-observed data.]]></description>
      <pubDate>Tue, 15 Jul 2025 09:47:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573092</guid>
    </item>
    <item>
      <title>The Use of Freeway Shoulders to Increase Capacity</title>
      <link>https://trid.trb.org/View/2559731</link>
      <description><![CDATA[Every sector of urban transportation faces the problems of rising costs, limited funds, and depleting resources with which to provide for increasing travel demands. Getting the greatest production out of the existing transportation facilities is the goal of every transportation agency. The Texas State Department of Highways and Public Transportation is testing the concept of increasing roadway capacity on urban freeways by restriping the mainlane pavement with narrower lane widths and encroaching on the shoulder to create one additional lane for travel. Two sections of U. S. 59 Southwest Freeway in Houston were modified for study. Before and after data were collected over a seven-year period to determine the effectiveness of reconfiguring the surface geometries of freeways to provide an additional lane for travel 24 hours every day. A second section was modified to provide the additional lane during peak periods only on weekdays. This "permissive" design was studied over a four-year period.]]></description>
      <pubDate>Mon, 14 Jul 2025 14:20:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559731</guid>
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