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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>An inertia-infused ADMM-based splitting algorithm with parallel computing for traffic assignment</title>
      <link>https://trid.trb.org/View/2688668</link>
      <description><![CDATA[In this paper, we propose an inertia-infused alternating direction method of multipliers (ADMM)-based splitting algorithm for the origin-based traffic assignment problem. The method is framed as a sequential Gauss–Seidel update with Jacobi-type parallelization in each subproblem. A Nesterov-accelerated inertial strategy, using information from previous iterations, is applied before updating link flows. Within each decomposed block, link-flow subproblems are solved in parallel via the gradient projection method with inertia. In updating Lagrange multipliers, a nonnegative relaxation factor is incorporated to improve flexibility. Numerical experiments show that with properly chosen inertial and relaxation parameters, the proposed algorithm achieves superior performance compared with the original ADMM.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688668</guid>
    </item>
    <item>
      <title>Vulnerability assessment of urban road networks under extreme weather events</title>
      <link>https://trid.trb.org/View/2687037</link>
      <description><![CDATA[The increasing occurrence of extreme weather events poses significant challenges to urban road transport systems, as spatially diffused disruptions (e.g., flooding) can trigger congestion spillovers and network-wide performance losses. This study proposes a multi-scenario, demand-segmented framework to assess flood-induced transport vulnerability and applies it to the private road network of the Milan metropolitan area. Flood hazard maps are used in combination with a static traffic assignment model to simulate performance degradation under multiple flood scenarios. A composite link-level vulnerability indicator is introduced, combining local congestion effects with systemic relevance measures derived from network-wide travel time changes, and is applied across distinct demand segments. Results show that vulnerabilities concentrate along ring-road systems and their access corridors, with inter-ring and external trips experiencing the greatest impacts, while internal trips are mainly affected by localized congestion. The findings demonstrate that flood-induced transport vulnerability is strongly shaped by demand patterns, network structure and exposure to risk. The proposed framework supports policy-oriented identification of critical road links and targeted preparedness strategies, and it is transferable to other metropolitan areas with comparable urban forms and flood exposure.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687037</guid>
    </item>
    <item>
      <title>Parking in Macroscopic Transport Models: Modelling Parking Capacities in Traffic Assignment</title>
      <link>https://trid.trb.org/View/2580134</link>
      <description><![CDATA[Parking measures are typical for cities that aim to improve the liveability in terms of air quality, noise, congestion and space. The strategic transport models, used to determine the effects of (policy) measures, do not incorporate the effect of parking behaviour during the trip. The behaviour of parking the car near the destination, and walking the last bit, is not yet modelled in these models. In order to incorporate this behaviour in the static traffic assignment, a methodology is developed. The methodology is twofold. First, the parking capacities, i.e. the number of available parking spaces per destination zone are determined using spatial data. Second, these parking capacities are used in the traffic assignment, which uses parking and walking links. In this way, the extra search time for a parking space is modelled, as well as the diverting behaviour when (almost) all parking spaces are occupied. Simulations for a use case in Amsterdam show that the diverting behaviour is modelled and show possible effects of parking policies, for instance on the amount of car traffic in the city.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580134</guid>
    </item>
    <item>
      <title>Graph-Theory-Based Traffic Flow Assignment Model and Path Optimization for Urban Interchanges</title>
      <link>https://trid.trb.org/View/2710893</link>
      <description><![CDATA[To address the problem that traditional traffic assignment models struggle to accurately depict the operational state of urban interchanges due to their complex topological characteristics of layered structures and interwoven paths, this study constructs a graph-theory-based traffic flow assignment and path optimization model. First, the interchange is abstracted as a multi-layered dynamic weighted graph incorporating real-time traffic parameters. Next, a flow equalization algorithm coupling inter-layer flow and weaving conflict constraints is designed. Then, an adaptive K-shortest path generation strategy incorporating a structural penalty mechanism is proposed. Finally, a game theory-based distributed path collaborative assignment framework is established. Experimental results show that the model improves interchange traffic efficiency by more than 30.0% during peak hours compared to traditional methods, and maintains a 36.4% speed advantage even in accident scenarios. Parameter optimization further improves performance by 6.0%. Extending to regional road networks, the collaborative strategy reduces total travel time by 15.2%. These findings validate the effectiveness and advancement of the proposed method in accurately describing the dynamic traffic behavior of interchanges and achieving intelligent collaborative management.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2710893</guid>
    </item>
    <item>
      <title>A general algorithm for traffic assignment problems with continuously distributed user attributes</title>
      <link>https://trid.trb.org/View/2692408</link>
      <description><![CDATA[This paper presents a general-purpose algorithm for solving traffic assignment problems with continuously distributed user attributes. Traditional methods often rely on discretization, which introduces behavioral distortions and limits scalability. The proposed algorithm simulates a cumulative logit (CumLog) day-to-day adjustment process, in which travelers iteratively revise their route choices based on accumulated travel experience. Aggregate route choice probabilities are computed via numerical integration, eliminating the need for arbitrary user grouping, closed-form objective functions, or restrictive problem structures. Using the continuous bi-criteria traffic assignment problem as a test case, we establish convergence and detail implementation strategies. The algorithm is further extended to accommodate multiple continuous attributes, non-separable travel times, and non-additive cost functions. Unlike classical zero-order methods, which struggle with infinite user heterogeneity, the CumLog algorithm obviates intractable class-specific operations. Numerical experiments validate its convergence, efficiency, and generality across a range of problem settings.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692408</guid>
    </item>
    <item>
      <title>Optional no-driving day policy considering travelers’ driving-date preferences</title>
      <link>https://trid.trb.org/View/2682113</link>
      <description><![CDATA[This paper investigates an Optional No-Driving Day (ONDD) policy as a flexible, rebate-incentivized approach to enhancing mobility sustainability while preserving travel freedom. A general mathematical framework is proposed for travelers’ driving-day choice problem which allows non-identical daily traffic flow and is computationally tractable for large-scale applications. By developing the concepts of driving-day patterns and the driving-day incidence matrix, the complex interactions between travelers’ driving-day choices and the fluctuating daily flows are modeled. An equivalent maximization problem is formulated to address the no-driving day choice problem, which provides a mathematical formulation for extensive driving-day choice problems. Moreover, as travelers’ heterogeneous preferences lead to non-identical daily flows, causing excessive congestion on some days while leaving others underutilized, we propose a multi-period, multi-phase ONDD policy. This approach staggers travelers’ ONDD periods to distribute more evenly across days. It is proven that the ONDD policy can achieve Pareto improvements over traditional rigid driving restrictions, even without a rebate. However, the implementation of ONDD without financial incentives leads to increased overall driving costs, highlighting the price of granting flexibility. It is demonstrated that introducing a permit-selling rebate, supported financially by the government or through strategic traffic flow management, can mitigate these costs, thereby enhancing the policy’s effectiveness and acceptability.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:12:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682113</guid>
    </item>
    <item>
      <title>Integrating non-motorized transportation with light rail transit: travel demand models for Rayong, Thailand</title>
      <link>https://trid.trb.org/View/2676284</link>
      <description><![CDATA[Rayong Municipality, a major industrial and economic center in eastern Thailand, faces persistent traffic congestion driven by rapid in-migration, strong car dependency, and limited public transport provision. This study develops an integrated low-carbon mobility framework that links non-motorized transport (NMT)—walking and cycling—with a planned Light Rail Transit (LRT) corridor and evaluates its impacts using a demand-driven economic assessment. The framework is operationalized through a calibrated four-step travel demand model supported by revealed and stated preference surveys, with a context-sensitive bikeway network explicitly designed and incorporated as a feeder to public transport. Traffic assignment outputs, expressed in vehicle-kilometers (PCU-km) and vehicle-hours (PCU-hr), are translated into environmental and economic impacts, including travel time savings and monetized emission reductions. The results indicate that NMT–public transport integration can induce meaningful modal shifts away from private vehicles, leading to an approximate 3% reduction in total traffic volume, a 4% decrease in aggregate travel time, and a 1.5% improvement in average network speed. However, the economic evaluation demonstrates that LRT investment is not cost-effective under current demand conditions, yielding a benefit–cost ratio below unity. Sensitivity analysis highlights the critical role of capital cost realism and supports a phased implementation strategy that prioritizes lower-cost interventions—such as protected bikeways and surface public transport—prior to rail-based investment. Overall, the proposed framework provides transferable, policy-relevant insights for medium-sized, car-oriented cities seeking economically feasible pathways toward sustainable and low-carbon urban mobility.]]></description>
      <pubDate>Thu, 18 Jun 2026 09:05:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676284</guid>
    </item>
    <item>
      <title>Flexible lane allocation for dedicated automated vehicle zones</title>
      <link>https://trid.trb.org/View/2601757</link>
      <description><![CDATA[As urban traffic demands generally fluctuate a lot during different periods of a day, current fixed lane allocation may not be able to provide satisfactory service for the varying and imbalanced traffic flows. With the emerging connected and automated (CAV) technologies, this study proposes a joint flexible lane allocation model to improve the network service capability. Different from previous studies, the model optimizes not only the lane reversals among adjacent intersections but also the lane groups in front of intersections. Moreover, the bottleneck effect of intersections has been thoroughly considered in the modelling process, preventing the system from falling into local oversaturation status. To achieve solutions in reasonable time, a bi-level framework enhanced by linearization techniques is proposed to decompose the coupling relationship between lane allocation and traffic assignment and resolve the curve of dimensionality for large scale networks. Under the framework, solutions can be obtained by iteratively solving two mixed-integer linear programming (MILP) models, determining the lane allocation results and traffic flow assignment respectively. Numerical analysis on an artificial network is provided to illustrate the effectiveness of our proposed method; furthermore, numerical experiments are conducted on a real-world network and validate that our proposed method can outperform state-of-practice lane allocation methods.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601757</guid>
    </item>
    <item>
      <title>An enhanced parallel block coordinate descent algorithm with shared memory for solving large-scale user equilibrium problems</title>
      <link>https://trid.trb.org/View/2597147</link>
      <description><![CDATA[Traffic assignment plays a critical role in urban planning by optimizing transportation efficiency and enhancing overall urban mobility. In large-scale transportation networks, traditional sequential algorithms often exhibit limited computational performance when solving traffic assignment problems (TAPs), which has led to growing interest in parallel algorithms. However, most existing studies primarily focus on the theoretical performance of parallel algorithms, without sufficient attention to their practical applicability. This paper restructures the parallel block coordinate descent (PBCD) algorithm using OpenMP in a shared-memory environment to assess its real-world performance. To address data race issues inherent in parallel computing, a thread-based private data structure is custom-designed. Furthermore, numerical experiments reveal that the convergence behavior of the algorithm becomes unstable as it approaches high-precision solutions, exhibiting significant oscillations. To alleviate this issue, we propose a dynamic block reduction (DBR) strategy that adaptively adjusts the number of OD pairs within each block throughout the iterative process. Two parameter selection methods for DBR are investigated: a fixed-parameter scheme and a self-adaptive scheme based on the Armijo rule. Numerical experiments demonstrate that the OpenMP-based DBR-PBCD algorithm significantly enhances convergence efficiency compared to existing algorithms, particularly for large-scale TAP instances in urban transportation networks.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597147</guid>
    </item>
    <item>
      <title>Evaluating passenger flow variations across an urban rail network induced by new lines using spatio-temporal transfer learning method</title>
      <link>https://trid.trb.org/View/2670174</link>
      <description><![CDATA[In many major cities worldwide, the expansion and construction of new rail transit lines are actively pursued to alleviate operational pressures on existing networks. Evaluating the impacts of new lines on existing ones, particularly through network-wide Origin–Destination (OD) passenger flow forecasting that accounts for newly constructed lines, is crucial for efficient line planning and network operations. However, OD flow prediction faces significant challenges due to the absence of historical passenger flow data for new lines and the changes they introduce to overall passenger volumes and distribution. This study presents a transfer learning-based hypergraph approach to represent OD flow data, tackling computational challenges in megacities with hundreds of urban rail stations. In this model, OD pairs serve as vertices, while their spatiotemporal similarities are captured by hyperedges. Spatial features are extracted from geographical data, while temporal features of existing OD pairs are learned from historical passenger flows. For new OD pairs lacking historical data, transfer learning infers temporal features from spatially similar pairs. These spatiotemporal similarities are then used to construct the hypergraph. Then, the hypergraph convolution is applied to extract high-order spatiotemporal features from the proposed hypergraph model, enabling the prediction of OD flow changes in the expanded urban rail transit network. A logit-based passenger assignment model is adopted to estimate how passengers redistribute across the network in response to the introduction of new lines. The effectiveness and accuracy of the proposed method are validated using real-world data from the Shanghai urban rail network. Results demonstrate that the modeling framework enables detailed analysis of station- and section-level passenger flow changes across the entire network. The integrated prediction–assignment framework presented in this study offers a novel and practical tool to support data-driven planning and operational decision-making in large urban rail systems.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670174</guid>
    </item>
    <item>
      <title>Optimization Framework of Dynamic Train Formation Planning in Combination With a Railcar-to-Track Assignment</title>
      <link>https://trid.trb.org/View/2617767</link>
      <description><![CDATA[To support intelligent transportation systems and autonomous freight rail development, in this study, we have developed a novel optimization framework for dynamic train formation planning. It optimizes network-dimension decisions including railcar routing, blocking, train makeup, and train routing, while incorporating railcar-to-track assignments in technical yards. This integrates decisions across previously isolated network and yard dimensions, addressing imprecision and infeasibility caused by conceptual reduction in existing frameworks. Furthermore, the framework subdivides schedule length to incorporate railcar and train scheduling, responding to demand fluctuations. A state-space-time network captures spatiotemporal characteristics of railcars and trains. A practical method that partitions classification tracks into two states is implemented to maintain a compact network scale. An integer-linear programming model is developed to minimize overall transportation, operation, and delay costs. Practical factors and operational methods are considered to ensure feasibility and effectiveness of dynamic train formation planning. A multi-variable-exploration branch-and-price algorithm is designed to solve the model, which estimates and branches on multiple variables in each iteration for promising convergence progress. A general constraint-driven coactive branching technique is embedded to impose multiple branches simultaneously to strengthen lower bounds. Numerical experiments based on real-world railroad networks with up to 21 yards and 964 shipments (28 473 railcars) assess the framework’s performance and practicality. The improved branch-and-price algorithm demonstrates significant improvements over conventional approaches, reducing total cost and computational time by up to 4.13% and 98.40%, respectively.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617767</guid>
    </item>
    <item>
      <title>The Combined Modal Split and Traffic Assignment Problem With Captive Demand in Mixed Transportation Networks</title>
      <link>https://trid.trb.org/View/2658768</link>
      <description><![CDATA[In past studies on the combined modal split and traffic assignment (CMSTA) problem, there are problems of not considering the three critical factors simultaneously: the user equilibrium (UE) on the transit network, the interaction of modes, and the captive demand. In this paper, a CMSTA model that considers the three critical factors simultaneously is presented. In the model, both the equilibrium on the auto network and the equilibrium on the transit network are considered, the interaction of the auto flow and the transit flow on link travel time is considered, and the captive demand of each mode is considered. An algorithm incorporating the double-stage algorithm, the diagonalization algorithm, and the gradient projection (GP) algorithm is presented for solving the model. The model and algorithm were applied to two networks and their validity is shown. The resultant OD demand for both modes is different with different captivity parameters. When the captivity parameter for auto is fixed, the resultant OD demand for the buses increases with the increase of the captivity parameter for the buses whereas the resultant OD demand for the autos decreases with the increase of the captivity parameter for the buses. The model applies to a city with two transportation modes: auto and bus. They are useful in the travel demand analysis and transportation planning and management.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658768</guid>
    </item>
    <item>
      <title>Optimizing Right-of-Way Allocation in Urban Expressway Ramp Influence Areas: Placement of Autonomous Vehicle-Dedicated Lanes and Buffer Zones</title>
      <link>https://trid.trb.org/View/2658967</link>
      <description><![CDATA[In mixed traffic scenarios involving both human-driven vehicles (HVs) and autonomous vehicles (AVs), the implementation of autonomous vehicle-dedicated lanes (AVDLs) on urban expressway segments can mitigate traffic flow heterogeneity and enhance road capacity. However, in ramp influence areas (RIAs) with AVDLs, existing studies have not sufficiently explored methods for evaluating and optimizing right-of-way allocation. This study addresses this gap by proposing a simulation-based optimization framework that includes simulating complex driving behavior of HVs and AVs in RIAs with AVDLs, and optimizing right-of-way allocation for AVDL entry/exit positions and buffer zone lengths, aiming to improve the capacity and safety of the RIA. We introduce a surrogate-based algorithm combining Gaussian process modeling with simulated annealing to efficiently solve the optimization model. MATLAB-based simulation and optimization software was developed to implement this framework and validated through a case study of Shanghai’s North–South Elevated Expressway, demonstrating up to a 25% improvement in traffic efficiency and safety. This study provides valuable insights and practical tools for urban traffic managers to design and implement effective right-of-way allocation strategies for urban expressways in mixed traffic scenarios.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658967</guid>
    </item>
    <item>
      <title>A simulation heuristic for traveler- and vehicle-discrete dynamic traffic assignment</title>
      <link>https://trid.trb.org/View/2667239</link>
      <description><![CDATA[A dynamic traffic assignment problem is considered where travelers are modeled as integral decision makers and network flow is composed of integral vehicles. As travel behavior affects network conditions and network conditions affect travel behavior, a complex model system results. The versatility of the considered model class has led to increasing practical interest (“agent-based simulation”) but also complicates the development of solvers for mutually consistent travel behavior and network conditions that represent possible long-term states of a transport system. Continuum flow assignment techniques are not applicable to this model class. This work starts out from a Nikaido-Isoda gap function for the traveler- and vehicle-discrete dynamic traffic assignment problem. A tractable but rather uninformative upper bound on this gap function is derived. A reformulation is presented that violates this bound as little as possible while ensuring that the reformulated bound carries relevant information for the subsequently developed new assignment heuristic. The proposed approach is formally related to and experimentally compared with relevant methods from the literature. It is found to exhibit superior performance in nontrivial case studies for Stockholm (Sweden), Oslo (Norway), and Berlin (Germany).]]></description>
      <pubDate>Tue, 26 May 2026 09:40:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667239</guid>
    </item>
    <item>
      <title>Multi-agent reinforcement learning with a hybrid sequential reward feedback strategy for dynamic multi-modal traffic assignment</title>
      <link>https://trid.trb.org/View/2667136</link>
      <description><![CDATA[Urbanization and the expansion of transportation modes have exacerbated the challenges of understanding travelers’ decision-making processes regarding route choice across various transportation modes. This paper proposes a novel macroscopic hybrid sequential game method using multi-agent reinforcement learning (MARL) to address issues of computational efficiency and behavioral complexity in multi-modal transportation network simulations. Specifically, agents’ perception behaviors are modeled as a sequential decision-making process considering road capacity constraints, which helps estimate travel time under congestion effects in the multi-modal traffic assignment. In addition, a hybrid reward framework is proposed, providing system-level reward to guide the multi-agent system towards different Nash equilibria, thereby reducing policy fluctuations. To simulate interactions between agents of different transportation modes, a multi-edge representation and reward structures designed for car, bus, priority bus, and metro modes are adopted to handle the mixed traffic flow through the same road. Furthermore, our approach uses a mean-field multi-agent deep Q-learning method to consider both mode and route choice, simplifying agent interactions through mean-field theory and clustering agents with the same origin–destination (OD) demands. Experimental results demonstrate that the hybrid sequential feedback strategy outperforms the simultaneous feedback strategy regarding convergence speed, agent reward distribution, and network flow distribution. Furthermore, the proposed method is tested on the Sioux-Falls network to verify its computational efficiency in three network change scenarios (disruption, road reconstruction, and new road construction). These findings highlight the potential of the proposed MARL method for large-scale multi-modal transportation network analysis, particularly under various incident scenarios, providing an effective tool for urban transportation planning and project evaluation.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667136</guid>
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