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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>Compact City Dilemma: Overcoming Traffic Gridlock for Sustainable Progress</title>
      <link>https://trid.trb.org/View/2686274</link>
      <description><![CDATA[While the compact urban model promises long-term sustainability, it often presents the immediate challenge of severe traffic congestion resulting from the inherent lag in travel behavior adaptation behind rapid land development. This study introduces a dynamic simulation framework, integrating grid-based modeling with macroscopic fundamental diagram analysis to resolve this conflict. We identify an optimal urban form that minimizes average travel time, a critical form that triggers gridlock, and a feasible development intensity range between the two forms. Validation against empirical data confirms the general V-shaped travel time pattern, yet reveals a critical divergence: our simulation captures the immediate “shock state” of rapid densification with a significantly steeper speed decline, whereas empirical observations reflect long-term “equilibrium states” after behavioral adaptation. This gap quantifies the adaptation lag and underscores the need for gradual implementation. . Extending the analysis to environmental effects using a hybrid life-cycle assessment framework, we find that, in 2050 under deep decarbonization scenarios, the well-to-tank (WTT) emissions curve adopts a V-shaped pattern similar to that of the travel time curve, achieving approximately 22% lower WTT emissions at its minimum relative to the high-density extreme, with only a marginal 1.9% increase in total well-to-wheel emissions. The closeness of the emissions and efficiency inflection points signals substantial potential for mobility–environment synergies. In plain English, success in compact cities requires localized calibration, gradual phased densification, and policies to accelerate travel behavior adaptation and sustainable mode shift—turning potential gridlock into a driver of resilient urban growth.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686274</guid>
    </item>
    <item>
      <title>Hybrid action space approach to traffic signal optimization using deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2652353</link>
      <description><![CDATA[Traffic intersections with time-varying demand encounter persistent challenges in achieving adaptive signal timing optimization and dynamic traffic equilibrium. Existing deep reinforcement learning methodologies predominantly adopt discrete action spaces, which constrain the capacity of such approaches to address complex spatiotemporal traffic dynamics. This research proposes a hybrid action space-based Deep Q-Network (H-DQN) framework for traffic signal control, evaluating its optimization effects through multi-dimensional performance metrics under dynamic traffic conditions. Firstly, this study treats phase policy selection and duration setting as two-level synergistic optimization, thereby proposing a refined hybrid action decision-making mechanism that integrates a dual-layer architecture for joint decision-making on signal phases and green durations. Secondly, the state space is defined by integrating lane saturation levels, queuing lengths, and current green phases across all intersection lanes. At the same time, the reward function is constructed based on the temporal variation in vehicle counts between consecutive sampling intervals, enabling dynamic adaptation to real-time traffic changes while ensuring computational efficiency through optimized design. Finally, this study validates the model in the Simulation of Urban MObility (SUMO) simulation environment across three traffic scenarios, which are uniform traffic with stable speed, non-uniform traffic with stable speed, and complex traffic with varying arrival rates. The results show that the proposed H-DQN model significantly improved intersection throughput efficiency across all three traffic scenarios compared to the DQN method, reducing average queue lengths by 21.58%, 49.43%, and 44.92%, respectively. Compared to Q-learning, the reductions in average queue lengths improved by 86.32%, 87.61%, and 84.51%. Meanwhile, the H-DQN model strengthens average vehicle speeds while minimizing delays, fuel consumption, and lane occupancy, thereby achieving substantial improvements in intersection operational efficiency and traffic throughput.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652353</guid>
    </item>
    <item>
      <title>Dynamic traffic assignment in a bi-dimensional model</title>
      <link>https://trid.trb.org/View/2643233</link>
      <description><![CDATA[Bidimensional models represent the transportation system as a bi-dimensional fluid flowing on a bi-dimensional medium. This approximation allows modelling of very large networks with modest requirements in terms of input data and computational power. The objective of the paper is to develop a methodology for the calculation of dynamic traffic assignment equilibria within the framework of bidimensional models. The approach of the paper assumes a route choice based on instantaneous travel times and departure time based on experienced travel costs. The main issues addressed in the paper are the evaluation of travel times and the calculation of equilibria. Examples of applications on large networks illustrate the chosen approach.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643233</guid>
    </item>
    <item>
      <title>The bi-mode problem with modular buses and private vehicles in the autonomous driving environment</title>
      <link>https://trid.trb.org/View/2643225</link>
      <description><![CDATA[In the process of continuous development of autonomous driving technology, there has been an evolution towards the intensification of transportation through autonomous modular bus (AMB). This paper proposes an innovative bi-mode bottleneck model that analyzes the equilibrium problem of the system consisting of AMBs and autonomous private vehicles (APVs) during the morning commuting process. We categorize all potential commuting modes and provides analytical solutions. Additionally, the impact of fixed passenger load, linear passenger load, and penetration rate under equilibrium condition is explored. Influenced by the coupling efficiency of AMB, there exist six possible equilibrium departure patterns in the mixed system with the fixed load. Throughout the entire stage of introducing AMBs, it has positive effects on reducing societal costs and alleviating road congestion. As the technology matures, a transition towards AMBs becoming the predominant mode of transportation. This study provides valuable insights for managers in implementing strategic decisions.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643225</guid>
    </item>
    <item>
      <title>Day-to-day dynamics in two-sided ridesourcing markets</title>
      <link>https://trid.trb.org/View/2643218</link>
      <description><![CDATA[To understand why ridesourcing markets may be prone to evolve towards potentially socially undesirable equilibrium states, we conceptualize the network effects present in ridesourcing provision. In addition, we propose an agent-based model that allows simulating the effect of market conditions and platform strategies on system performance, accounting for such network effects. This day-to-day model captures sequential decentralized processes characterizing both sides of the two-sided ridesourcing market, i.e. information diffusion, platform registration, platform participation, and learning based on experience. We apply the model on a case representing Amsterdam, the Netherlands. Our simulation results suggest that a profit-maximizing ridesourcing platform may trade-off market transaction volume for higher earnings on successful transactions, a strategy that is harmful to the interests of travellers and drivers, and possibly of (very) limited benefit to the platform. Moreover, we find that ridesourcing operations may be viable even when potential supply and demand in an area are limited.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643218</guid>
    </item>
    <item>
      <title>Coupled dynamics of electric vehicle with a novel solution of equilibrium under regenerative braking</title>
      <link>https://trid.trb.org/View/2666891</link>
      <description><![CDATA[In this paper, to focus on the vehicle handling stability under regenerative braking, a nonlinear high-dimensional vehicle dynamics model considering longitudinal and lateral coupling and load transfer was established. By conducting the experiments to obtain the regenerative braking torque characteristics, fitting expressions are introduced into the dynamics model of the vehicle to analyze the coupled dynamic behavior and stability variations under different maneuvers during regenerative braking. A genetic algorithm-based method is proposed to solve stable stem of the high-dimensional vehicle systems. The results indicate that the vehicle loses stability under both conditions, once unstable the vehicle has difficulty restoring stability under braking force. For stable region of initial states, the phase trajectory rapidly converges to a stable stem. The length of stable stems could serve as a reference indicator for single-pedal regenerative braking controller design. Meanwhile, the initial conditions should close to the stable stems, ensuring that the vehicle can quickly restore stability under perturbation, to improving the robustness of vehicle handling under braking conditions.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666891</guid>
    </item>
    <item>
      <title>Effective travel time surplus maximization stochastic user equilibrium model considering travel time reliability</title>
      <link>https://trid.trb.org/View/2655498</link>
      <description><![CDATA[In this paper, a method for modeling user behavior in a tolled road network is proposed. The effective travel time is defined as the mean travel time plus the travel time variance multiplied by a risk aversion parameter. The sources of the stochasticity of travel time are O-D demand and link capacity which follow a lognormal distribution. Each user has a maximum effective travel time they can spend traveling between origin and destination nodes, which is determined by an indifference function between toll and the effective travel time. The effective travel time surplus is defined as the maximum effective travel time minus the perceived effective travel time, i.e., each user has a perception error. Each user chooses a path so that the surplus is maximized. This study proposes an effective travel time surplus maximization problem, which is formulated as a stochastic user equilibrium model under stochastic demand and link capacity.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655498</guid>
    </item>
    <item>
      <title>A Literature Review and Methodological Recommendations to Measure the Economic Impact of Air Transport</title>
      <link>https://trid.trb.org/View/2666100</link>
      <description><![CDATA[This paper reviews 96 studies on the economic impact of air transport, focusing on four key methodologies: input-output analysis, cost-benefit analysis (CBA), computable general equilibrium (CGE) models, and econometric techniques. Our findings show that input-output analysis and CGE models are best suited for studying the broad economic effects of air transport on the entire economy. Input-output analysis reveals that air transport creates jobs and economic value, with a stronger backward linkage—its effect on upstream industries—than a forward linkage. It also plays a key role in tourism, logistics, and freight. CBA and econometric methods are more effective for analyzing localized effects. The CBA literature indicates that airport expansion projects significantly increase social welfare. CGE models show that subsidies and taxes on air transport can benefit the economy by boosting government revenues, and that capacity expansions help both core and peripheral regions. Econometric studies confirm a positive link between air transport and various economic indicators, including GDP, employment, trade, and foreign direct investment. The methodologies themselves have advanced to overcome certain limitations. Input-output analysis has been extended with multi-period and multi-region models. CBA now uses alternative discounting techniques. CGE models have become more sophisticated with spatial and dynamic applications, and some even integrate air transport networks and game theory. Econometric techniques have adopted more robust models like heterogeneous time series cross section Granger causality, difference-in-difference, propensity score matching, seemingly unrelated regression and spatial econometrics to better address endogeneity. These advanced methods can provide a more robust approach for future research.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666100</guid>
    </item>
    <item>
      <title>A multiplicative regret-based stochastic user equilibrium model</title>
      <link>https://trid.trb.org/View/2630589</link>
      <description><![CDATA[Random regret minimization is an alternative decision rule to the overwhelmingly used random utility maximization in travel choice and network equilibrium models. Existing random regret models (RRMs) mainly adopt an additive error structure, which is inadequate to capture travelers’ magnitude-dependent perceptions of travel alternatives and is often difficult to reflect the impact of transportation network scales. This study proposes a novel multiplicative random regret model (MRRM) to address these issues by taking advantage of the multiplicative error structure. Compared with the traditional additive RRMs, the MRRM addresses the scale-invariance issue and enables alternative-specific travel perceptions while retaining the essential properties of RRMs. Specific distributional assumptions are made for the smooth approximation of the regret function and random perception of alternative-level regret, which guarantees the analytical expression of choice probability that facilitates the application in traffic assignment problems. The MRRM is further integrated into the stochastic user equilibrium (SUE) assignment to endogenously model the congestion effect on regret-based route choice behaviors. The MRRM-SUE model is formulated as a variational inequality problem and solved via a path-based algorithm. Numerical experiments are conducted on different networks to illustrate the features of the MRRM-SUE model and verify its applicability in real-world cases.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630589</guid>
    </item>
    <item>
      <title>A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models</title>
      <link>https://trid.trb.org/View/2642460</link>
      <description><![CDATA[Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific datasets limits generalization across diverse scenarios and reduces reliability in real-world deployment. In addition to behavioral fidelity, ensuring traffic stability is increasingly critical for the safe and efficient operation of autonomous vehicles (AVs), requiring CFMs that jointly address both objectives. However, existing models generally do not support a systematic integration of these goals. To bridge this gap, we propose a knowledge-informed deep learning (KIDL) paradigm that distills the generalization capabilities of pre-trained large language models (LLMs) into a lightweight and stability-aware neural architecture. LLMs are used to extract fundamental car-following knowledge beyond dataset-specific patterns, and this knowledge is transferred to a reliable, tractable, and computationally efficient model through knowledge distillation. KIDL also incorporates stability constraints directly into its training objective, ensuring that the resulting model not only emulates human-like behavior but also satisfies the local and string stability requirements essential for real-world AV deployment. We evaluate KIDL on the real-world NGSIM and HighD datasets, comparing its performance with representative physics-based, data-driven, and hybrid CFMs. Both empirical and theoretical results consistently demonstrate KIDL’s superior behavioral generalization and traffic flow stability, offering a robust and scalable solution for next-generation traffic systems.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642460</guid>
    </item>
    <item>
      <title>On the Role of Non-Localities in Fundamental Diagram Estimation</title>
      <link>https://trid.trb.org/View/2591273</link>
      <description><![CDATA[We consider the role of non-localities in speed-density data used to fit fundamental diagrams from vehicle trajectories. We demonstrate that the use of anticipated densities results in a clear classification of speed-density data into stationary and non-stationary points, namely, acceleration and deceleration regimes and their separating boundary. The separating boundary represents a locus of stationary traffic states, i.e., the fundamental diagram. To fit fundamental diagrams, we develop an enhanced cross entropy minimization method that honors equilibrium traffic physics. We illustrate the effectiveness of our proposed approach by comparing it with the traditional approach that uses local speed-density states and least squares estimation. Our experiments show that the separating boundary in our approach is invariant to varying trajectory samples within the same spatio-temporal region, providing further evidence that the separating boundary is indeed a locus of stationary traffic states.]]></description>
      <pubDate>Mon, 16 Mar 2026 13:50:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591273</guid>
    </item>
    <item>
      <title>Which type of backpressure is more stable? – Comparative analysis based on two-movement intersections</title>
      <link>https://trid.trb.org/View/2636251</link>
      <description><![CDATA[Stability, which indicates that queues do not grow infinitely over time, is a key concept in control policies such as BackPressure (BP). However, its abstract nature and diverse definitions make its comparative analysis difficult both theoretically and experimentally. As a result, simulations in existing studies often use alternative metrics, such as average delay, to evaluate the performance of different control policies. Little research directly compares different stabilities through theory or experiments. In this paper, we compare seven common stability definitions and theoretically demonstrates that they are equivalent in simulations and applications. Furthermore, we propose a 𝘵-test method for identifying whether a queue is stable based on the sequence of queueing differences. This method allows us to classify any sampled demand as stable or unstable based on simulated queues for a given control policy. Therefore, if the network’s dimension, i.e., the number of movements, does not exceed three, we can directly draw the stability region (SR) for all policies and compare their sizes. To accurately reproduce various BP theories, ensure fair comparisons, and facilitate the visualization of SRs, we use simulation codes to simulate a two-movement intersection scenario and discuss its extension to networks. Six distinct types of BP policies are compared, along with analysis for fixed-time and actuated controls. We obtain many insights that are difficult to achieve through purely theoretical analysis and delay-based simulations, including: 1) variability in BP’s SR: the SR typically varies when the BP changes its queue status weight or efficiency weight; 2) size hierarchy of SR: BPs generally outperform actuated controls in terms of SR, and actuated controls tend to outperform fixed-time controls; 3) non-cyclic vs. cyclic BP: non-cyclic BP usually has a larger SR than cyclic BP; 4) effect of real-time supply information: using real-time supply increases the SR of BP, even under the assumption of fixed saturation headway; and 5) SR degradation phenomenon: longer cycle lengths in cyclic BP may cause its SR to degenerate into a rectangular shape typical of fixed-time control.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636251</guid>
    </item>
    <item>
      <title>Assessing the benefits of collaborative ridesharing across transportation network companies</title>
      <link>https://trid.trb.org/View/2633658</link>
      <description><![CDATA[By allowing two or more passengers to dynamically share parts of their trips in a single vehicle, ridesharing can lead to significant vehicle mileage traveled (VMT) savings, but its potential is often limited by market fragmentation, due to the co-existence of multiple Transportation Network Companies (TNCs). Although a collaborative ridesharing market that allows sharing across TNCs can produce additional VMT savings, to what extent such benefits vary based on market characteristics and behaviors of individual TNCs remains insufficiently understood. This study presents a framework to assess the maximum potential benefits of collaborative ridesharing and the contrasting equilibrium benefits resulting from varying strategic behaviors of TNCs. Specifically, we adopt a multi-TNC shareability network approach to estimate the maximum benefits under various market conditions, assuming that all TNCs are fully collaborative. In reality, TNCs’ willingness to collaborate is primarily motivated by their own profit gains, and thus we further propose a game-theoretic model to capture the collaboration dynamics among TNCs as a Nash game, allowing us to estimate the equilibrium benefits of collaborative ridesharing and evaluate the effectiveness of various profit-sharing schemes. Using the real-world TNC market in Manhattan, New York City as a case study, we find that a fully collaborative ridesharing market can generate additional VMT savings of up to 10.3 % over the existing fragmented market, and this upper bound is jointly determined by demand density, market division, competition intensity, and trip length. The Nash game results also reveal that the TNCs’ willingness to collaborate largely depends on the profit-sharing scheme; the Shapley value scheme tends to favor smaller, higher-priced TNCs, while the Equal Profit Method benefits dominant TNCs and more effectively facilitates collaboration among TNCs with greater pricing disparities. The proposed framework provides valuable insights for market regulators and business alliances, enabling them to evaluate collaboration outcomes and design appropriate profit-sharing schemes to promote and sustain collaborative ridesharing.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633658</guid>
    </item>
    <item>
      <title>Why the Kelvin–Michell linearization may be best for practical applications</title>
      <link>https://trid.trb.org/View/2634012</link>
      <description><![CDATA[This study considers three alternative assumptions that justify linearizingthe free-surface boundary condition for a ship that steadily advances through regular waves or in calm water. Flow-velocity computations for a typical slender ship and a typical blunt ship show that linearization about a ‘base-flow’ chosen as the flow in the infinite-gravity limit g=∞, also known as the ‘double-body flow’ around the ship-hull surface and its free-surface mirror image, or as the flow in the zero-gravity limit g=0 offers questionable benefits over the classical Kelvin–Michell linearization, which assumes that the velocity of the flow created by the ship is much smaller than the ship speed. Specifically, the computations of flow velocities at the free-surface plane reported in the study show that the horizontal or vertical flow velocities computed in the limits g=∞ or g=0 are much smaller than the ship speed except in very small regions around the ship waterline, and that reliable computations of these flow velocities involve considerable difficulties. Moreover, linearization about the ‘infinite-gravity’ or ‘zero-gravity’ flows is not better theoretically justified than the Kelvin–Michell linearization, which has the huge merit of being incomparably simpler and unaffected by numerical errors.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2634012</guid>
    </item>
    <item>
      <title>Scalable and reliable multi-agent reinforcement learning for traffic assignment</title>
      <link>https://trid.trb.org/View/2632162</link>
      <description><![CDATA[The evolution of metropolitan cities and increasing travel demand impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) approaches outperform traditional methods in modeling adaptive routing behavior without requiring explicit system dynamics, making them attractive for real-world deployment. However, existing MARL frameworks face scalability and reliability challenges when managing large-scale networks with substantial and variable demand. This study proposes MARL-OD-DA, a novel framework that redefines agents as origin–destination (OD) pair routers and employs a continuous simplex-constrained action space. This reformulation reduces the agent population from 𝘖(𝘕) (number of travelers) to 𝘖(|𝘋|) (number of OD pairs), achieving at least two orders of magnitude fewer agents in practice while preserving convexity and enabling efficient adaptation to demand variation, thus significantly improving scalability. In contrast to prior MARL studies constrained to small-sized networks (up to 70 nodes, 2100 travelers) and fixed demand, MARL-OD-DA is validated on medium-sized networks (up to 416 nodes, 1406 OD pairs, and 360,600 travelers) under varying demand scenarios, demonstrating substantial improvements in scalability and applicability. To further enhance reliability, the framework integrates a Dirichlet-based policy, action pruning, and a relative gap-based reward. Theoretical analysis demonstrates that the Dirichlet-based policy reduces gradient bias, stabilizes variance, and enables sparse routing decisions, in contrast to the commonly used softmax-based policy. Experiments on three benchmark networks show that MARL-OD-DA significantly improves assignment quality and convergence speed. On the SiouxFalls network, the trained agents converge within 10 iterations during deployment, reducing the relative gap by 94.99% compared to conventional baselines.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632162</guid>
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