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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>Spatial Markov equilibrium models for taxi services: driver decision, search friction, and locational pricing</title>
      <link>https://trid.trb.org/View/2704077</link>
      <description><![CDATA[This paper develops a modeling framework for stochastic multi-agent systems and applies it to equilibrium and pricing analysis in urban taxi markets. Travel demand is represented as a trip network and embedded in a Markov chain that captures both locational and in-transit taxi states, with transition dynamics reflecting trip durations, search frictions, spatial competition, and drivers’ perceptions of long-term value. The framework features a parametric Markov chain with endogenous transition probabilities and a behavioral model in which agents’ decisions depend on anticipated long-term rewards. We establish equilibrium existence and examine two locational pricing schemes that align individual incentives with system-level service objectives. The equilibrium conditions are formulated as a nonlinear program and solved using successive approximation algorithms. A case study based on Chicago taxi data illustrates the model’s use for regulatory and fleet analysis.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704077</guid>
    </item>
    <item>
      <title>Modeling household-level party composition behavior for multiparty activities: a random parameter nested logit modeling approach</title>
      <link>https://trid.trb.org/View/2698416</link>
      <description><![CDATA[This study presents findings of a household-level party composition model for multiparty activities. It exploits data from a comprehensive Household Travel Survey conducted by Chicago Metropolitan Agency of Planning. The study estimates a random parameter nested logit model to capture households’ unobserved preference heterogeneity and non-proportional substitution patterns in terms of activity party composition for multiparty activities. A wide variety of household demographics, activity attributes and residential neighborhood characteristics are examined in this paper. The magnitude of the impacts of the determinants are tested in this study by analyzing the elasticity of the variables, which suggests that household demographics and attributes of the multiparty activities have significant effects on the household-level activity party composition. Residential neighborhood characteristics, although somewhat less impactful, still play a meaningful role. This model will be implemented within the POLARIS transportation systems simulator to improve the activity generation modeling workflow, and the prediction accuracy of various activity-travel components.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698416</guid>
    </item>
    <item>
      <title>Traffic Safety Impacts of Reduced Police-motorist Interactions</title>
      <link>https://trid.trb.org/View/2730689</link>
      <description><![CDATA[This study seeks to add to the current body of knowledge by exploring the relationship between reduced police–motorist interactions and safety outcomes using a case-study approach. Six jurisdictions were selected for study: three with clear decreases in traffic stops, and three with more stable stop numbers over time. Data on crashes of various severity were obtained for each site. The impact of different trends in stops on safety outcomes are compared using time series analyses. Six case-study locations were selected to examine the impact of reduced traffic stops on traffic safety outcomes. Nashville, Tennessee; Philadelphia, Pennsylvania; and Minneapolis, Minnesota, were selected because of a measurable decline in police interactions with motorists. In contrast, Charlotte, North Carolina; Chicago, Illinois; and Saint Paul, Minnesota, were selected because they showed less distinct declines in police–motorist interactions, temporary declines, or increases in stops.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:06:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730689</guid>
    </item>
    <item>
      <title>Toward an integrated cross-urban accident prevention system: A multi-task spatial-temporal learning framework for urban safety management</title>
      <link>https://trid.trb.org/View/2692840</link>
      <description><![CDATA[The development of a cross-city accident prevention system is particularly challenging due to the heterogeneity, inconsistent reporting, and inherently clustered, sparse, cyclical, and noisy nature of urban accident data. These intrinsic data properties, combined with fragmented governance and incompatible reporting standards, have long hindered the creation of an integrated, cross-city accident prevention framework. To address this gap, we propose the Mamba Local-Attention Spatial-Temporal Network (MLA-STNet), a unified system that formulates accident risk prediction as a multi-task learning problem across multiple cities. MLA-STNet integrates two complementary modules: (i) the Spatio-Temporal Geographical Mamba-Attention (STG-MA), which suppresses unstable spatio-temporal fluctuations and strengthens long-range temporal dependencies; and (ii) the Spatio-Temporal Semantic Mamba-Attention (STS-MA), which mitigates cross-city heterogeneity through a shared-parameter design that jointly trains all cities while preserving individual semantic representation spaces. We validate the proposed framework through 75 experiments under two forecasting scenarios, full-day and high-frequency accident periods, using real-world datasets from New York City and Chicago. Compared with the state-of-the-art baselines, MLA-STNet achieves up to 6% lower RMSE, 8% higher Recall, and 5% higher MAP, while maintaining less than 1% performance variation under 50% input noise. These results demonstrate that MLA-STNet effectively unifies heterogeneous urban datasets within a scalable, robust, and interpretable Cross-City Accident Prevention System, paving the way for coordinated and data-driven urban safety management. Our code is available at https://github.com/fangjiayu98/MLASTNet.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692840</guid>
    </item>
    <item>
      <title>Addressing the alignment problem in transportation policy making: an LLM approach</title>
      <link>https://trid.trb.org/View/2692838</link>
      <description><![CDATA[A key challenge in transportation planning is that the collective preferences of the traveling public often diverge from the policies produced by model-driven decision tools. This misalignment frequently results in implementation delays or failures. Here, we investigate whether large language models (LLMs)–noted for their capabilities in reasoning and simulating human decision-making–can help inform and address this alignment problem. We develop a multi-agent simulation in which LLMs, acting as agents representing residents from different communities in a city, participate in a referendum on a set of transit policy proposals. Using chain-of-thought reasoning, LLM agents provide Ranked-Choice or approval-based preferences, which are aggregated using instant-runoff voting (IRV) to model democratic consensus. We implement this simulation framework with both GPT-4o and Claude-3.5-Sonnet, and apply it for Chicago and Houston. Our findings suggest that LLM agents can approximate plausible collective preferences and exhibit sensitivity to local context. At the same time, they display notable deviations from optimization-based benchmarks and behavioral biases that appear specific to the underlying language model. The results underscore both promise and limitations of LLMs as tools for solving the alignment problem in transportation decision-making.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692838</guid>
    </item>
    <item>
      <title>Unequal streets, unequal journeys: The hidden mechanisms of shared mobility</title>
      <link>https://trid.trb.org/View/2683285</link>
      <description><![CDATA[Emerging shared mobility services, such as ride-hailing and bike-sharing, promise to enhance urban access but often reproduce deep-seated spatial inequalities. Traditional transport equity studies, which rely heavily on census-based structural variables, fail to capture the unmeasurable street-level visual qualities that shape travel behaviors. This study bridges this gap by proposing an artificial intelligence (AI)-driven framework that integrates deep learning and explainable machine learning to decode the perceptual mechanisms underlying multimodal transport inequality. Using Chicago as a case study, the study employed a VGG16 convolutional neural network trained on the Place Pulse 2.0 dataset to extract high-dimensional perceptual indicators such as safety, beauty, and liveliness from over 730,000 Google Street View images. These perceptual metrics were integrated with multimodal travel data including ride-hailing, Divvy bike-sharing, and subway, and then analyzed using Light Gradient Boosting Machine (LightGBM) models with Bayesian optimization. To overcome the “black box” limitation of AI in policy analysis, Shapley Additive Explanations (SHAP) were applied to quantify the marginal contribution of perception to inequality by measuring population-weighted and lower-quintile Gini indices. The results reveal that emerging mobility modes exhibit significantly higher inequality than traditional transit, driven largely by perceptual interactions. Crucially, we identify a “perceptual conversion” mechanism. Positive perceptions, such as safety and liveliness, amplify the conversion of infrastructure into realized accessibility, whereas negative perceptions, including boredom, suppress usage even in structurally well-served areas. This study demonstrates the transformative potential of computer vision and XAI in revealing hidden dimensions of transport equity, offering a data-driven pathway for precise urban interventions in the AI era.]]></description>
      <pubDate>Mon, 13 Jul 2026 13:58:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683285</guid>
    </item>
    <item>
      <title>Transit design, transportation policy and location-mode choice in the era of telework</title>
      <link>https://trid.trb.org/View/2691084</link>
      <description><![CDATA[This study develops an analytical framework to examine how telework affects commuting mode choice, residential location patterns, and urban transit policy. We incorporate user heterogeneity and multi-modality—transit, driving, and telework—into a monocentric city model, upon which a bi-level transit design problem featuring both utilitarian and egalitarian objectives is formulated. Using the model, we characterize the location-mode joint equilibrium and analyze how transit design and financial levers shape outcomes. Analytical results reveal a distinct sorting pattern: lower-income residents locate closer to the central business district and choose public transit, middle-income residents drive from mid-range suburbs, and higher-income residents opt for telework from the periphery, where spatial mixing occurs due to location indifference among teleworkers. A case study of a Chicago commuter corridor confirms these patterns and shows that the post-pandemic uptick in telework results in uneven welfare gains across income levels. We also find that redesigning transit service—particularly increasing stop spacing to improve operational efficiency—and adopting fare-free transit significantly improve social welfare, especially for low-income commuters. By contrast, taxation or congestion tolls yields limited additional benefits. Taken together, these findings highlight how changes in work behavior interact with residential and travel choices, and underscore the need to rethink related public policies in the era of telework.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691084</guid>
    </item>
    <item>
      <title>Large Language Model-Enhanced General Transportation Agent Framework for Human Mobility Forecasting and Synthetic Travel Survey Data Generation</title>
      <link>https://trid.trb.org/View/2721769</link>
      <description><![CDATA[This work presents the LLM-enhanced general transportation agent, a novel framework that leverages large language models (LLMs) to simulate individual-level human mobility behavior. Unlike prior approaches that treat LLMs as generic predictors or planners, this framework employs role-play prompting and synthetic sociodemographic profiles to position LLMs as simulated individuals responding to household travel surveys. The system integrates population synthesis, persona-rich prompting, structured response tools, a dynamic survey engine, and an LLM-as-a-judge evaluation method to generate and vet context-aware, realistic behavioral data. Case studies in Chicago, USA and Lyon, France demonstrate cross-linguistic adaptability and behavioral fidelity, while highlighting challenges in non-English contexts. Quantitative evaluation shows that midsized and smaller models most accurately reproduce empirical travel survey distributions, whereas larger instruction-tuned models generate more coherent and naturalistic responses. Smaller models exhibit greater susceptibility to logical inconsistencies and stereotyped language, emphasizing potential bias propagation in synthetic datasets. The framework enables scalable, model-agnostic synthetic mobility data generation and supports applications such as scenario prototyping, discretionary activity simulation, travel diary construction, and integration into agent-based and microsimulation transportation models.]]></description>
      <pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721769</guid>
    </item>
    <item>
      <title>Agent-Based Modeling of Transportation Energy Shifts and Trends: A Case Study of the Chicago Region</title>
      <link>https://trid.trb.org/View/2711013</link>
      <description><![CDATA[This study uses the Planning and Operations Language for Agent-based Regional Integrated Simulation (POLARIS) simulation platform to evaluate the future of travel demand, system performance, and financial evolution in the Chicago metropolitan region under a range of 2050 scenarios. We begin with a calibrated 2019 baseline reflecting pre-pandemic travel behavior and align with Chicago Metropolitan Agency for Planning (CMAP) travel survey data. A fused transportation network incorporating both POLARIS and Emme infrastructure datasets allows for detailed representation of the region. Scenarios tested include transit expansion, various electrification rates, carbon and Vehicle Miles Travel-based (VMT) charging strategies, freight depot charging, and changes in telecommuting prevalence. Results indicate a shift away from Single Occupancy Vehicle (SOV) travel, with VMT and Vehicle Hours Traveled (VHT) reductions across all 2050 scenarios amplified under distance-based pricing. Transit expansion leads to significant gains in ridership, with elasticity values between 0.4 and 0.6. Despite revenue losses from electrification, VMT charging mechanisms not only recover but exceed fuel tax losses, especially when implemented at medium or high levels. Electrification scenarios show a substantial increase in charging demand, particularly for freight, emphasizing the need for targeted infrastructure planning. The analysis provides insights into how certain pricing strategies, service investments, and some tax or incentive mechanisms could reduce congestion, improve network efficiency, and maintain fiscal viability.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711013</guid>
    </item>
    <item>
      <title>Graph neural networks for residential location choice: Connection to classical logit models</title>
      <link>https://trid.trb.org/View/2696170</link>
      <description><![CDATA[Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, the existing deep learning approaches cannot explicitly capture the relationship among choice alternatives, which has been a long-lasting focus in classical discrete choice models. To address the gap, this paper introduces Graph Neural Network (GNN) as a novel framework to analyze residential location choice. The GNN-based discrete choice models (GNN-DCMs) offer a structured approach for neural networks to capture dependence among spatial alternatives, while maintaining clear connections to classical random utility theory. Theoretically, we demonstrate that the GNN-DCMs incorporate the nested logit (NL) model and the spatially correlated logit (SCL) model as two specific cases, yielding a novel algorithmic interpretation through message passing among alternatives’ utilities. Empirically, the GNN-DCMs outperform benchmark MNL, SCL, and feedforward neural networks in predicting residential location choices among Chicago’s 77 community areas. Regarding model interpretation, the GNN-DCMs can capture individual heterogeneity and exhibit spatially-aware substitution patterns. Overall, these results highlight the potential of GNN-DCMs as a unified and expressive framework for synergizing discrete choice modeling and deep learning in the complex spatial choice contexts.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696170</guid>
    </item>
    <item>
      <title>Semi-on-demand hybrid transit route design with shared autonomous mobility services</title>
      <link>https://trid.trb.org/View/2673503</link>
      <description><![CDATA[Shared Autonomous Vehicles (SAVs) enable transit agencies to design more agile and responsive services at lower operating costs. This study designs and evaluates a semi-on-demand hybrid route directional service in the public transit network, offering on-demand flexible route service in low-density areas and fixed route service in higher-density areas. We develop analytically tractable cost expressions that capture access, waiting, and riding costs for users, and distance-based operating and time-based vehicle costs for operators. Two formulations are presented for strategic and tactical decisions in flexible route portion, fleet size, headway, and vehicle size optimization, enabling the determination of route types between fixed, hybrid, and flexible routes based on demand, cost, and operational parameters. Analytical results demonstrate that the lower operating costs of SAVs favor more flexible route services. The practical applications and benefits of semi-on-demand feeders are presented with numerical examples and a large-scale case study in the Chicago metropolitan area, USA. Findings reveal scenarios in which flexible route portions serving passengers located further away reduce total costs, particularly user costs, whereas higher demand densities favor more traditional line-based operations. Current cost forecasts suggest smaller vehicles with fully flexible routes are optimal, but operating constraints or higher operating costs would favor larger vehicles with hybrid routes. The study provides an analytical tool to design SAVs as directional services and transit feeders, and tractable continuous approximation formulations for planning and research in transit network design.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673503</guid>
    </item>
    <item>
      <title>Leveraging Spatial–Temporal Heterogeneity and Cross-Mode Interactions: A Meta-Learning Approach for Multimodal Transportation Demand Prediction</title>
      <link>https://trid.trb.org/View/2659014</link>
      <description><![CDATA[Accurately and jointly predicting multimodal transportation demand is crucial for pre-allocating transport resources, enhancing the resilience of traffic systems. However, current approaches insufficiently explore inter- and intra-mode heterogeneity, resulting in undifferentiated dependency extraction. Moreover, existing research struggles to model cross-mode interactions among three or more transportation modes and adapt to dynamic relations in multimodal demand. To address these limitations, we propose a novel multimodal demand prediction model based on a meta-parameter learning network (MMDNet), centered on characterizing multimodal traffic spatial-temporal heterogeneity and unifying the modeling of cross-mode interactions. Our model features: 1) a spatial-temporal heterogeneity meta-parameter learning method, capturing both inter- and intra-mode heterogeneity to steer more targeted dependency extraction than previous studies; 2) a spatial-temporal evolving unified graph generator, transcending prior studies’ limitations in unifying dynamic interactions across three or more modes by creating dynamic unified graphs. Extensive experiments on three real-world datasets (New York, Beijing and Chicago) covering four different traffic modes are carried out to evaluate the MMDNet. The model achieves a 6.65% performance gain over advanced baselines and demonstrates strong cross-city adaptability. Abundant interpretability analyses show our model can semantically encode explainable cross-mode interactions and differences between modes. Source codes are available at https://github.com/zhjiang1/MMDNet]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659014</guid>
    </item>
    <item>
      <title>Low-Cost Pedestrian Safety Zone Case Study</title>
      <link>https://trid.trb.org/View/2706014</link>
      <description><![CDATA[A previous study by Blomberg and Cleven (1998) developed a pedestrian safety zone approach in Phoenix, AZ and Chicago, IL, focusing on pedestrian safety countermeasures in subsets of the cities that had experienced a high number of crashes involving people 65 and older. It proved efficient deploying countermeasures, significantly reducing the targeted pedestrian crashes in Phoenix. This original zone process, however, still needed significant resources to address the entire city, and required a relatively long time to plan and implement. The primary objectives of the present study included adapting the previous zones approach to be implementable quickly by cities using their resources and demonstrating the resulting low-cost pedestrian safety zones approach in several cities. Three cities—Gainesville, Florida; Kalamazoo, Michigan; and Saint Paul, Minnesota—agreed to demonstrate the low-cost zones approach. Each site prepared a case study report documenting the activities reported here. The approach showed to be flexible and effective in all three cities. The discussion section of this report includes ideas for supporting more widespread use of the technique.]]></description>
      <pubDate>Wed, 27 May 2026 14:32:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706014</guid>
    </item>
    <item>
      <title>Joint optimization of multimodal transit frequency and shared autonomous vehicle fleet size with hybrid metaheuristic and nonlinear programming</title>
      <link>https://trid.trb.org/View/2670004</link>
      <description><![CDATA[Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670004</guid>
    </item>
    <item>
      <title>Mining Hidden Ridesharing Patterns: A Data-Driven Gap Analysis of Chicago TNC Trips</title>
      <link>https://trid.trb.org/View/2673005</link>
      <description><![CDATA[This study paved the way for developing digital twins of smart and emerging urban mobility systems, using shared mobility services such as ridesharing as a key case study. As cities contend with challenges, such as traffic congestion, environmental sustainability, and transportation equity, shared mobility platforms (e.g., UberPOOL and Lyft Shared) have emerged as promising solutions. Leveraging Chicago’s Transportation Network Companies (TNCs) shared mobility data set, this research uncovers latent patterns in user behavior and trip-sharing dynamics through data mining and exploratory analysis. It distinguishes between trips, where users authorized ride-sharing and those that were actually pooled, revealing key spatial, temporal and behavioral difference. Economic factors also played an important role. For instance, the hourly gap between authorized and successfully pooled trips was narrower on weekends, suggesting more stable matching opportunities, while users who authorized but were not pooled tended to pay less per mile than the general trip population. Building on these insights, this study integrates both supervised and unsupervised machine learning methods to enhance the understanding of ridesharing dynamics. Density-based spatial clustering of applications with noise (DBSCAN) was employed to uncover latent trip groupings, which served as the foundation for developing predictive models that estimate the likelihood of successful ride matches. Multiple classifiers, including Logistic Regression, Random Forest, and XGBoost, were implemented and rigorously evaluated to identify the most effective predictive model. This integrated approach not only provides a comprehensive perspective on ridesharing behavior and trip shareability within current mobility platform, but also builds the foundation for early-stage digital twins that can simulate, optimize, and inform decision-making in future smart mobility systems, including autonomous vehicle fleet operations.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673005</guid>
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