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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>Is ride-sharing good for environment? Evidence from combining satellite and survey data on U.S. cities</title>
      <link>https://trid.trb.org/View/2708005</link>
      <description><![CDATA[We estimate the causal effect of ride-hailing entry on transport-related air pollution, disentangling its mediating effect through changes in commuting modes in U.S. cities. To do so, we combine two sets of empirical approaches. First, for our main outcome regression, we leverage granular satellite-based NO₂ concentration data and a newly constructed Google Trends-based measure of ride-hailing presence, with the staggered difference-in-differences design. Second, to explore its mediating mechanism, we use household-level commuting mode data to run two auxiliary regressions: commuting modes on ride-hailing entry and ambient NO₂ concentration on commuting modes. For identification on the latter, we construct our instruments by combining geography-based instruments with leave-one-out regional average exposure to Uber’s official entry. We find robust evidence that (i) ride-hailing improves air quality in highly dense cities, but has no significant impact in cities with low to medium density and (ii) this air quality improvement is indeed mediated by the associated changes in commuting mode choices. Our findings provide strong empirical support for the hypothesis that the environmental impact of ride-hailing depends on its complementarity with public transit.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708005</guid>
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    <item>
      <title>A Literature Review on the Integration of Game Theory and Artificial Intelligence in Traffic Signal Control</title>
      <link>https://trid.trb.org/View/2761049</link>
      <description><![CDATA[In the twenty-first century, artificial intelligence (AI) has been one of the most promising breakthroughs in computer science, revolutionizing intelligent transportation systems (ITS). Among the pressing challenges within ITS is traffic congestion. Congestion undermines urban mobility, economic productivity, and environmental sustainability. Traditional traffic signal control (TSC) systems struggle to adapt to the dynamic and multiagent nature of real-world traffic. To address this, researchers have increasingly turned to AI and game theory (GT). The two fields offer powerful tools for modeling and optimizing strategic decision-making in complex environments. While AI enables systems to learn from data and respond adaptively, GT provides a mathematical framework for reasoning about the interactions and incentives of multiple agents in signal control. Studies have shown how each field can successfully optimize TSC operation. Furthermore, studies have shown that integrating AI and GT has been successful in domains such as adaptive traffic signal control. This integration has been relevant when decentralized agents must coordinate and when conflicting objectives require substantial computational resources to be fully optimized. Moreover, advancements in multiagent learning, mechanism design, and behavioral modeling have further deepened the synergy between these fields. This paper presents a literature review examining the most recent (2018–2025) research progress at the intersection of GT and AI in traffic signal control. We highlight key application areas at both isolated and multi-intersection levels. We also identify open research challenges/limitations and propose potential future research areas for practical use.]]></description>
      <pubDate>Wed, 19 Aug 2026 09:25:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761049</guid>
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    <item>
      <title>Urban network percolation and traffic dynamics: Hysteresis, critical exponents, and resilience</title>
      <link>https://trid.trb.org/View/2728023</link>
      <description><![CDATA[Motivated by recent empirical findings relating percolation and macroscopic fundamental diagram (MFD) phenomena, this paper investigates the percolation and hysteresis mechanisms underlying urban congestion through controlled numerical experiments. We simulate network loading/unloading experiments on microscopic grid networks using the kinematic-wave model and perform percolation analyses on link-level densities using a congestion threshold kthr. Our main findings are: (i) cluster-size distributions near criticality follow power laws with Fisher exponent values concentrated in the range 2 ≤ τ ≤ 2.35, with a mode around τ ≈ 2.1, close to the ordinary two-dimensional percolation value; (ii) a percolation-based mechanism explains clockwise hysteresis in the MFD: during unloading, persistent large clusters lower the percolation threshold Kperc, rendering the network more fragile than during the loading phase for the same average density; (iii) this hysteresis mechanism is observed primarily for long-block networks (λ > 1) and its strength scales inversely with the turning probability; and (iv) the chosen definition of kthr governs the relative timing between the percolation transition and the flow critical point. We discuss practical implications for resilience-aware congestion management.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728023</guid>
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    <item>
      <title>Ramp Metering: Signal for Success [video]</title>
      <link>https://trid.trb.org/View/2727304</link>
      <description><![CDATA[This video explains how ramp metering works as a means of controlling traffic onto freeways. It highlights the experiences of the California Department of Transportation, the Colorado Department of Highways, the Los Angeles County Transportation Commission, and the Minnesota Department of Transportation.]]></description>
      <pubDate>Wed, 29 Jul 2026 13:35:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727304</guid>
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      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion - Phase 2</title>
      <link>https://trid.trb.org/View/2733194</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total.  The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.    

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium, leading to travel times that can be significantly higher than travel times from the System Optimal, particularly in congested urban networks where the effects of individual decisions cascade throughout the system.  With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal.  Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas.

In Phase 1, the research team developed the research foundation for this problem. This work includes the literature review and the development of an online dispatch-and-relocation framework for a centrally controlled autonomous vehicle fleet. The Phase 1 framework matches requests to vehicles while accounting for pickup deadlines, near-term vehicle availability, and proactive repositioning toward forecasted demand. Phase 1 also establishes a comparison structure against a traditional human-driver ride-hailing system and an initial simulation capability that traces routes and estimates vehicle miles traveled, deadhead miles, passenger waiting time, revenue, and related performance measures.

Phase 2 will build directly on this foundation and is the primary focus of the next stage of the project. In Phase 2, the team will scale the optimization and simulation framework so it can solve problems at the size of major metropolitan areas. This includes extending the model to larger networks and richer demand patterns, improving computational tractability for larger instances, and strengthening the simulation module so it can evaluate passenger-vehicle matches and route decisions under more realistic operating conditions. To make the model scalable, the team will aggregate the service region into zones and solve the resulting problems repeatedly over short rolling horizons. The team will also need to calibrate the demand forecasting and routing inputs for large urban networks and test the algorithms on progressively larger instances to ensure that the solution quality and computation time remain practical. The purpose of Phase 2 is to determine how much centralized control of autonomous fleets can reduce system-wide travel, deadhead mileage, waiting times, and congestion when evaluated on realistic metropolitan-scale settings.

]]></description>
      <pubDate>Wed, 22 Jul 2026 17:37:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2733194</guid>
    </item>
    <item>
      <title>Digital twin for managing the curb and reducing congestion (Phase 2)</title>
      <link>https://trid.trb.org/View/2732948</link>
      <description><![CDATA[Curb space in dense urban cores is under intense pressure from freight deliveries, service vehicles, and passenger car parking activities. Without a data-driven view of curb regulations and demand, cities face double-parking, spillback congestion, and safety conflicts. This project addresses the gap by creating an open-data-based digital twin that links curb regulations, observed curb activity proxies, and network performance to support actionable curb management decisions.

The research team is developing a strategic curb digital twin for a portion of downtown Los Angeles (DTLA), built using publicly available data to ensure transparency and replicability. In Phase 1, the team integrated multiple open datasets (GIS networks from the LA GeoHub, land use data from DataLA, OpenStreetMap) and developed heterogeneous freight demand models distinguishing commercial and residential delivery behaviors. Phase 2 adds an analytical curb allocation optimization layer with targeted microsimulation validation. While the broader research agenda includes multimodal curb demand modeling (pursued in parallel work), Phase 2 addresses the policy question: given competing freight delivery and passenger parking demands, how should curb space be optimally allocated across blockfaces and time-of-day periods?]]></description>
      <pubDate>Wed, 22 Jul 2026 17:17:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732948</guid>
    </item>
    <item>
      <title>Optimization of Public Transport Route Accessibility in High Congestion Areas Using Arc GIS: A Case Study of Rawalpindi City</title>
      <link>https://trid.trb.org/View/2686168</link>
      <description><![CDATA[Due to increasing urbanization and environmental concerns, there is a growing need to enhance and optimize public transportation systems to make them more accessible, efficient, and sustainable. The aim of this study is to improve the public transportation network by optimization of the routes in high congested areas of Rawalpindi city. Geographic Information Systems (GIS) have emerged as a powerful tool for achieving such goal. The integration process involves collecting and analyzing geospatial data, including route information, passenger demographics, and real-time traffic conditions, to optimize transit routes. By incorporating real-time traffic data into the GIS model, the analysis provided insights into congestion configurations and prospective solutions. GIS-based maps presented performance metrics such as reduced travel times, enhanced accessibility scores, and better public transport usage. These metrics were critical for validating the proposed solutions and ensuring their feasibility for implementation. GIS-based route optimization study also proposed BRT systems in our study area (Rawalpindi) to create efficient, timesaving, and passenger-centric public transport options. To maximize its impact, cities need to encourage people to switch from cars to BRT. The success of a GIS-based optimization transportation system requires close collaboration between the public and private sectors.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686168</guid>
    </item>
    <item>
      <title>A multiscale decomposition and semantics-guided feature reconstruction LLM framework for traffic flow forecasting</title>
      <link>https://trid.trb.org/View/2684466</link>
      <description><![CDATA[Traffic flow forecasting plays a critical role in intelligent transportation systems (ITS), particularly in urban congestion management and resource allocation. Although large language models (LLMs) have demonstrated powerful capabilities in sequence modeling and reasoning, their application to the traffic domain remains challenging due to inherent modality differences. Specifically, how to effectively transform numerical traffic time series into semantic representations that can be understood by language models. Moreover, traffic flow data often exhibits complex and varying temporal patterns across different sampling scales. The inherent multiscale nature and multidimensional heterogeneity of such data impose high demands on models to achieve structural alignment and semantic fusion, posing significant challenges for conventional LLMs that are primarily designed for text-based tasks. To address these issues, this paper proposes a novel traffic flow forecasting framework, Reconstructed Multiscale Forecasting for Traffic (ReMFT), which integrates multiscale temporal decomposition with semantic integration. The framework first incorporates a multiscale decomposition module to separate and reconstruct trend and seasonal components from raw traffic series, thereby revealing latent temporal structures across different time scales. It then introduces a semantics-guided feature reconstruction module that semantically aligns and embeds the decomposed features in a non-linguistic manner, making them compatible with the LLM’s input representation preferences. This design enables a smooth transition from structural time series modeling to semantic representation learning, enhancing the LLM’s generalization and reasoning capabilities when dealing with the spatiotemporal heterogeneity and dynamic patterns of traffic flow. Experimental results on multiple real-world traffic datasets demonstrate that the proposed method achieves competitive or superior performance compared to several state-of-the-art (SOTA) forecasting models. The code is available at: https://github.com/IansSUn/ReMFT.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684466</guid>
    </item>
    <item>
      <title>The impact of employer-provided car coverage policies on urban spatial structure and commuting behavior</title>
      <link>https://trid.trb.org/View/2618064</link>
      <description><![CDATA[Employer-provided cars have become a standard mode of transportation worldwide, particularly for commuting. In most cases, company cars are considered fringe benefits, with a full package covering all internal travel costs (fuel, tolls, parking, etc.) paid by the employer. However, employers increasingly exclude these nonessential costs and offer only limited coverage packages to employees as a cost-cutting measure. This study analyzes the effects of different coverage policies on urban spatial structure, travel behavior, and residential location preferences under two different congestion management technologies (standard and bottleneck) and labor market structures (locally efficient and inefficient). Limited coverage with standard congestion tolling results in clustered economic activities and populations for both labor market structures. However, limited coverage with bottleneck congestion tolling can cause urban sprawl and more dispersed home locations, mainly due to constant trip costs, given that employers redistribute the surplus in the mobility budget (income effect). This study highlights the policy implications of urban fringe growth and downtown decline.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618064</guid>
    </item>
    <item>
      <title>On the resilience-enhanced control for road traffic systems - An “ecological” approach</title>
      <link>https://trid.trb.org/View/2673505</link>
      <description><![CDATA[The resilience of road systems is one key focus in transportation science literature. In particular, many studies have focused on traffic control development for improving the resilience where the mainstream control methodology is to drive the traffic system to an assumed single optimal equilibrium and a local recovery from failure or disruption. This setting appears to be oversimplified, as road traffic system can feature multiple (locally) stable states like many other complex physical systems, and thus there is lack of understanding on can and how a system transition between stable states. In Holling (1996) and Folke (2006), etc., such ability is defined as “ecological resilience” and it has not been applied within the context of urban road traffic control. In this work, we define urban traffic ecological resilience as the ability of a traffic system to absorb unpredictable congestion disturbances, by shifting to alternative steady states without the need of returning to the original non-congested equilibrium. A traffic flow control framework is developed to enable this ability, and it comprises three aspects: portraying the recoverable scopes via an “attraction region” modeling, designing alternative steady states around the classic equilibrium, and controlling towards local steady states while maintaining stable dynamics. Notably, this paper derives analytically the inner and outer of attraction region with explicit algebraic expressions, innovatively addressing the inherent complexity in attraction region derivation for nonlinear systems. Comparing to the classical resilience-oriented approaches, the proposed ecological resilience approach offers superior adaptability towards hyper-congestion; and enhances the overall system resilience, i.e., the recovery capacity from hyper-congestion. The proposed resilience-targeted control shifts from an engineering-focused paradigm to an “ecological” one, as the objective is not to move towards a rather infeasible optimality, but to facilitate an ecological transition among approachable local optimalities.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673505</guid>
    </item>
    <item>
      <title>Resilient AI adoption strategy for regional port clusters: A deep reinforcement learning approach</title>
      <link>https://trid.trb.org/View/2705563</link>
      <description><![CDATA[The rapid adoption of Artificial Intelligence (AI) drives regional port clusters into hyperconnected ecosystems where deep automation enhances marginal productivity while simultaneously amplifying the risk of cascading failures under systemic shocks. To navigate this complex dynamic, this paper proposes a resilient AI adoption strategy for heterogeneous port clusters using a Deep Reinforcement Learning (DRL) framework. The authors establish a dynamic network model that employs a logistic diffusion model to simulate the deployment of AI technologies and captures the non-linear propagation of congestion across the network. A Constrained Proximal Policy Optimization (PPO) algorithm is then employed to learn an optimal investment policy that balances operational speed with long-term stability under strict budget constraints. Empirical validation on the Yangtze River Port Cluster (YRPC), calibrated with high-resolution AIS data, reveals that a “one-size-fits-all” automation strategy is suboptimal for bulk-dominated networks. Instead, the proposed DRL agent identifies a “Resilience Dividend” by prioritizing foundational technologies—such as predictive maintenance and computer vision for safety—and reinforcing critical bridge nodes. Simulation results demonstrate that this adaptive strategy achieves a superior Pareto balance, maintaining high service levels while reducing the median contagion scale of disruptions by over 60% compared to myopic benchmarks.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705563</guid>
    </item>
    <item>
      <title>Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices</title>
      <link>https://trid.trb.org/View/2701206</link>
      <description><![CDATA[The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701206</guid>
    </item>
    <item>
      <title>Understanding congestion in airport surface operations using 3D fundamental diagrams</title>
      <link>https://trid.trb.org/View/2672075</link>
      <description><![CDATA[Operational delays that arise when demand on the airport surface approaches or exceeds its capacity adversely impact passengers, airports, and the environment. To design effective interventions to manage airport surface congestion, airport operators require a robust understanding of the technology that drives congestion on the airport surface, that is, how delays on the airport surface vary over capacity utilization of its bottlenecks. Theoretical models of congestion technology (CT) exist, however, they are defined for ideal conditions, for instance, by assuming demand being independent of airport surface congestion, thus failing to characterize the realized airport surface operations. The availability of highly granular data on day-to-day surface operations facilitates the development of practically relevant models of congestion that are reproducible under wide-ranging operational scenarios. Nevertheless, obtaining empirical estimates of the CT from observed data on airport operations is challenging due to statistical biases that emerge via the complex interactions between air traffic operations and control at airports and in the wider airspace. In this study, we propose a novel causal statistical approach to model airport surface congestion, represented via delay versus runway and ground capacity utilization relationships, henceforth Three-Dimensional Fundamental Diagrams (3D-FDs). The proposed approach allows us to capture inherent non-linearities in the relationship while addressing the aforementioned confounding biases. Accordingly, we model the 3D-FDs of five major global airports and deliver key new insights into their surface-use efficiency, for instance, by locating their optimum operating point, that is, the point beyond which delays increase at an increasing rate with the intensity of use.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672075</guid>
    </item>
    <item>
      <title>Simulating protective cordon pricing to balance congestion management and affordability in San Francisco</title>
      <link>https://trid.trb.org/View/2693693</link>
      <description><![CDATA[Congestion pricing effectively manages road travel demand but raises affordability concerns for lower-income groups. This study evaluates potential implementations of cordon pricing in downtown San Francisco and their Bay Area-wide impacts. Using the BEAM CORE agent-based workflow, we compare flat-rate and income-based schemes, analyzing changes in accessibility, vehicle miles traveled, mode shift, energy use, and air quality. Both strategies reduce cordon-zone congestion and emissions while triggering external spillover effects, including increased vehicle miles traveled from rerouting, but also improved air quality. The income-based scheme protects lower-income households through reduced fees, yet exemptions alone cannot fully shield travelers: part of their accessibility loss stems from network reorganization rather than direct toll costs, a finding with implications for any income-tiered pricing design. Critically, congestion reduction and affordability need not be a zero-sum trade-off: the income-aware design achieves meaningful traffic reduction while cutting lower-income accessibility losses by 57% compared to flat-rate pricing.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693693</guid>
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      <title>Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing</title>
      <link>https://trid.trb.org/View/2659418</link>
      <description><![CDATA[This paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659418</guid>
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