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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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    <item>
      <title>Assessing the Reliability and Usability of Mobile Ticketing App Data for Transit Analytics: A Case Study of Unitrans in Davis, California</title>
      <link>https://trid.trb.org/View/2690984</link>
      <description><![CDATA[Mobile ticketing apps have become increasingly popular among transit agencies due to their cost efficiency and ability to streamline payments. Beyond operational efficiencies, these apps also generate vast travel data with the potential to support transit agencies in decision-making. However, this data contains incomplete trip information and suffers from representation bias. Several questions remain unanswered: Is this data a statistically representative sample of all transit riders? What are its potential applications? This research will address this gap by evaluating the potential applications and representativeness of app data. The project focuses on ZipPass, a mobile ticketing app used by Unitrans in Davis, California. Within six months of launch, ZipPass has already generated over 350,000 spatial activation records. Researchers devised a strategy to integrate ZipPass data with the onboard transit survey and the UC Davis campus travel survey. They also plan to conduct a targeted survey of active ZipPass users to supplement rider-specific and trip-level information. The team will explore how ZipPass data, along with support from supplementary data sources, can be used for two potential applications to support the agency: (1) estimating transit ridership and (2) understanding riders' origin-destinations. This research will study the reliability and usability of mobile ticketing app data for transit analytics by assessing its quality after augmenting the data with other existing resources to increase contextual information. The research will provide valuable insights to transit agencies looking to harness mobile ticketing data for operational purposes. Since periodic onboard transit surveys are required for federal funding, both mobile ticketing data and transit survey data are available to agencies at no extra expense. Small agencies can leverage our findings to integrate at least these two datasets and effectively utilize them for operational improvement. The project will create a framework for them to integrate mobile ticketing data with periodic transit surveys to support their transit planning and decision-making. While Unitrans serves as our primary case study, the research is designed to be applicable and scalable to transit agencies nationwide.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:29:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690984</guid>
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    <item>
      <title>Estimating origin-destination matrices with sparse seed matrices</title>
      <link>https://trid.trb.org/View/2679122</link>
      <description><![CDATA[Origin-destination matrices of traveller flows are a key ingredient to transport planning. In public transport planning, most agencies conduct origin-destination surveys to extract line-level origin-destination matrices. These matrices, however, only partially represent ridership, as only a fraction of travellers are surveyed. Hence, they need to be scaled to real ridership, typically by using automatic passenger counts (APC) and algorithms such as iterative proportional fitting (IPF). This procedure works well for busy lines, where seed matrices present few or no zeros (i.e., absence of observations for a given origin-destination pair), however it becomes less reliable on sparsely used lines, where seed matrices present a high percentage of structural and sampling zeros. It is currently unknown, up to which percentage of zeroes IPF can be reliably used, and how to handle zeroes more generally. In this paper, we apply IPF to simulated (ground truth) and real origin-destination seed matrices to quantify the reliability of IPF and to test different replacement values for zeroes. We work with matching data from automatic passenger counters and a large origin-destination survey with 26,000 + participants on 70 + public transport lines that was conducted in 2022 in Geneva, Switzerland. We find that the reliability of IPF measured by the estimation error exponentially correlates with the percentage of zeros in the seed matrix. We test replacement values of zeroes between 0 and 10 and find that 1 is the best replacement for sampling zeros in the seed matrix to minimize the estimation error and simultaneously improve convergence. Practitioners and academics can use these results to maintain the advantages of IPF in practice (computational lightweight, simplicity, implementation in most common software) yet improve its reliability on sparsely used lines.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679122</guid>
    </item>
    <item>
      <title>Multi-View Hypergraph-Based Ride-Sourcing Origin-Destination Demand Prediction</title>
      <link>https://trid.trb.org/View/2610648</link>
      <description><![CDATA[Ride-sourcing services have become a vital urban mobility mode, offering flexibility and convenience. Accurate origin-destination (OD) demand prediction is crucial for ride-sourcing platforms to deliver efficient on-demand mobility services. Existing studies have tried to extract complex spatial-temporal dependencies of OD pairs from different aspects. Differently, we propose a novel multi-view hypergraph OD demand prediction framework, which utilizes hypergraphs to associate multiple OD pairs from static and dynamic views simultaneously. In the static hypergraph view, we explore spatial-temporal correlations from three perspectives: points of interest, time series, and motifs. Among them, motifs can capture local higher-order network structures in the OD network. In the dynamic view, we utilize real-time OD demand as prior information to learn representations of multiple OD pairs directly and capture evolving spatial-temporal dependencies. Moreover, a gate mechanism and probability constraints are designed to enhance accuracy and reliability. Finally, experimental results on two large-scale ride-sourcing datasets from Hangzhou and Ningbo verify the superiority of our proposed model over seven baseline models. Accurate OD demand prediction aids in guiding vehicle dispatching and dynamic pricing for ride-sourcing platforms.]]></description>
      <pubDate>Thu, 26 Mar 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610648</guid>
    </item>
    <item>
      <title>An XGBoost-Based Three-Stage Prediction Approach for True User Demand of Bike-Sharing Systems Based on Spatio-Temporal Analysis</title>
      <link>https://trid.trb.org/View/2610632</link>
      <description><![CDATA[A bike-sharing system (BSS) is easily unbalanced due to the uncertainty of user demand at each bike station during the day, which appeals for an effective bike reposition solution based on the accurate prediction of user demand. However, there is a discrepancy between the bike pickup/drop-off record (satisfied demand) and the user’s first choice of origin/destination stations (i.e., true user demand) since the BSS cannot capture the unsatisfied user demand (i.e., abandoned rentals and transferred rentals/returns) that occurs at either empty stations (failed rentals) or full stations (failed returns). To efficiently rebalance the BSS, this paper focuses on accurately forecasting the true user demand of the BSS. First, we extract the spatial-temporal features of bike usage and establish a spatio-temporal model for true user demand prediction. Then, an XGBoost-based three-stage prediction approach is proposed to accurately predict the true user demand including the station clustering, the system record rectification, and the true user demand prediction. The real data from the Citi Bike in New York is applied to verify the proposed method and the experimental results demonstrate that the proposed approach outperforms the existing methods.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610632</guid>
    </item>
    <item>
      <title>Physics-Informed Mobility Perception Networks for Origin-Destination Flow Prediction</title>
      <link>https://trid.trb.org/View/2617707</link>
      <description><![CDATA[Origin-destination flow prediction traditionally depends on intricate numerical simulations of human mobility. Data-driven approaches, including transformers, have disrupted this paradigm with sophisticated predictive models. However, purely data-driven approaches often act as closed box models, failing to reveal the underlying mechanisms of human mobility. We address these limitations with physics-informed mobility perception networks (PI-MPN), a continuous-time process that implements a key principle of diffusion from human mobility, and introduces the principle into data-driven deep learning networks. PI-MPN consists of data-driven mobility perception networks and physics-informed neural diffusion networks. PI-MPN models precise spatio-temporal movement by data-driven networks, learning urban human mobility as a neural flow by physics-informed networks. We conduct comprehensive evaluations on two real-world traffic datasets, revealing the superiority of PI-MPN over existing data-driven benchmarks. Moreover, case analyses demonstrate that the diffusion process can describe the underlying urban dynamics, thus enhancing the model interpretability of human mobility. Model implementation is available at https://github.com/xuesong-wu/PI-MPN]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617707</guid>
    </item>
    <item>
      <title>A spectral clustering enabled SPSA algorithm for dynamic origin-destination demand matrix estimation</title>
      <link>https://trid.trb.org/View/2643234</link>
      <description><![CDATA[The simultaneous perturbation stochastic approximation (SPSA) algorithm has been widely employed in the dynamic origin-destination demand estimation (DODE) problem. However, SPSA results in the inaccurate estimation of the dynamic OD matrices when the magnitudes of OD flows in the OD matrix are uneven. This paper proposes an enhanced SPSA algorithm, named SNMF-SPSA, to improve the estimation accuracy. The SNMF-SPSA algorithm innovatively leverages the potential spatial–temporal information in the dynamic OD matrices to guide the gradient approximation, without introducing substantial computational burden. Furthermore, the macroscopic fundamental diagram (MFD) is also considered in the objective function to capture the non-linear network traffic dynamics, thereby enhancing the estimation consistency. Two real urban networks with different scales were used to validate the performance of SNMF-SPSA. The results demonstrate that SNMF-SPSA can achieve the optimal result in fewer iterations, and the results of OD flows estimation are more accurate.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643234</guid>
    </item>
    <item>
      <title>Privacy-aware pedestrian trajectory prediction with dual-channel destination guidance and multi-factor aggregation federated learning</title>
      <link>https://trid.trb.org/View/2643230</link>
      <description><![CDATA[Accurate pedestrian trajectory prediction plays a critical role in enhancing the safety and reliability of autonomous driving. Traditional methods often overlook the subjective intentions of pedestrians, limiting their ability to address the inherent uncertainty in trajectory prediction. Moreover, they typically use centralized data aggregation from heterogeneous environments, raising concerns over privacy breaches. To tackle these challenges, we propose a context-guided trajectory prediction framework tailored for autonomous driving. It leverages scene semantics to generate a dual-channel destination representation, comprehensively describing pedestrians’ potential destinations. A conditional diffusion model is introduced to generate diverse yet accurate future trajectories by conditioning on both pedestrian destinations and social interactions. To protect data privacy, we leverage federated learning with a novel multi-factor aggregation scheme that dynamically adjusts each client’s contribution based on data quality, quantity, and training dynamics. Experiments on benchmarking datasets validate that our method achieves superior and privacy-aware trajectory prediction performance.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643230</guid>
    </item>
    <item>
      <title>Heterogeneous multi-view graph gated neural networks for real-time origin-destination matrix prediction in metro systems</title>
      <link>https://trid.trb.org/View/2643229</link>
      <description><![CDATA[Short-term origin-destination (OD) matrix prediction in metro systems faces challenges of high dimensionality, data sparsity, incomplete information, and semantic complexity. This paper proposes an effective framework called Multi-Graph Gated Neural Networks with Linear Modulation (MGGNLM) to address these challenges. We introduce distillation units to mitigate matrix dimensionality and sparsity issues, while incorporating real-time passenger flow data to handle incomplete information. The metro network is transformed into a heterogeneous graph comprising three components: a connectivity graph based on geometric location, a function-aware graph derived from GPT-2, and a mobility-pattern-aware graph constructed using Jensen-Shannon divergence. Through numerical experiments on Hangzhou and Nanjing datasets, our model demonstrates superior performance in multi-step OD demand prediction, improving WMAPE by 3.06% and 3.31% respectively compared to state-of-the-art methods. Additionally, MGGNLM exhibits exceptional performance in few-shot learning scenarios, making it particularly valuable for practical applications in metro systems.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643229</guid>
    </item>
    <item>
      <title>Seat allocation optimization for railway systems with equity considerations</title>
      <link>https://trid.trb.org/View/2643228</link>
      <description><![CDATA[This paper investigates a railway seat allocation problem with a focus on equity. We aim to distribute the railway capacity more fairly among passengers from different Origin-Destination (OD) pairs while enhancing profitability. We first develop a Mixed Integer Linear Programming (MILP) model for scenarios with deterministic demand. We then further extend our study by formulating Stochastic Programming (SP) and Distributionally Robust Optimization (DRO) models for scenarios with demand uncertainty. Additionally, we derive the deterministic equivalent of the DRO model using a box ambiguity set. Furthermore, we explore the relationships between the proposed DRO and SP models, both of which can be efficiently solved by common MILP solvers like GUROBI. To validate our approach, we perform numerical studies on a small-scale example and the Zhengzhou-Xi’an high-speed railway corridor. The results demonstrate that the proposed optimization methods improve equity across OD pairs, where the DRO model can yield high-quality solutions.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643228</guid>
    </item>
    <item>
      <title>From trip purpose to space-time flexibility: a study using floating car data and google popular times</title>
      <link>https://trid.trb.org/View/2643222</link>
      <description><![CDATA[Understanding the relationship between space-time flexibility and trip purpose is essential for efficiently planning transportation systems and to better understand travel behaviour, as it affects not only the demand for different modes of transport, but also the travellers route/service and departure time choice. The study aims to rigorously explore the temporal and spatial flexibilities inherent to various trip purposes – work, shopping, sustenance, and others – by harnessing the capabilities of Floating Car Data (FCD) and Google Popular Times (GPT). FCD provides high-resolution data on vehicular movements, offering insights into spatio-temporal characteristics such as routes, speeds, and origin-destination points. Conversely, GPT furnishes a nuanced perspective on the temporal aspects of activities by revealing visitation patterns at different venues. Through a probabilistic approach, the proposed methodology innovatively infers users' flexibility through the analysis of spatio-temporal features from both FCD and GPT. This data is subsequently employed to assemble sample Origin-Destination (OD) matrices, where each matrix represents trips from a specific origin (O) to a designated destination (D) within a defined time frame, all sharing comparable levels of flexibility. The findings offer valuable insights into the interconnection between trip purpose and flexibility, thereby paving the way for the development of an OD demand estimation model that incorporates spatio-temporal flexibility as a parameter, enhancing the precision and adaptability of transportation planning endeavours.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643222</guid>
    </item>
    <item>
      <title>Competitive or complementary role of bike-sharing to public transport: Budapest case study</title>
      <link>https://trid.trb.org/View/2644242</link>
      <description><![CDATA[Bike-sharing offers a suitable option for short rides in major cities worldwide, yet its role in urban transport remains unclear. This study examines how bike-sharing (BS) interacts with public transport (PT), either as a complementary feeder or a competing substitute service. Using a unique origin–destination dataset of over 2.2 million rentals in Budapest, Hungary (September 2023–August 2024), descriptive statistics and a zero-inflated negative binomial model were applied to explain both zero and positive rental counts based on PT availability and network and cycling infrastructure characteristics. Results show that bicycle distance is the strongest predictor of both zero rentals and ride intensity, while bicycle-friendly infrastructure and relative PT travel time significantly increase BS demand. Findings indicate a dual role of BS, though competition with PT dominates. BS often substitutes for short PT trips (1–2 stops), especially for the metro, or is used to avoid transfers. Competition is stronger when BS is faster than PT, notably in the morning peak, or when bicycle-friendly roads run parallel to PT lines. Complementarity emerges mainly in winter, when cycling conditions are worse, and in areas where PT availability is weak, but the metro is available nearby. Tram and especially metro connectivity positively affect BS use, while links with bus service are weak. These results can guide policymakers in expanding BS infrastructure to reinforce its complementary role.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644242</guid>
    </item>
    <item>
      <title>Multimodal traffic assignment from privacy-protected OD data</title>
      <link>https://trid.trb.org/View/2639383</link>
      <description><![CDATA[The (static) traffic assignment (TA) problem, which computes network equilibrium flows from origin–destination (OD) demand under flow conservation, is central to transportation modeling. As multimodal transportation systems (MTSs) grow, sharing detailed OD data – such as trip counts, timestamps, and routes – raises serious privacy concerns. Differential privacy (DP) has emerged as the leading standard for releasing such data, offering adjustable protection beyond traditional anonymization. However, current methods mostly apply extrinsic DP by adding noise to aggregate OD matrices before release, without fully addressing its effects on traffic modeling. This reveals TA’s unpreparedness for privacy-protected data and calls for redesigned methods that operate reliably under such constraints. To fill this gap, we propose the privacy-preserving traffic assignment (PPTA) framework, which embeds DP intrinsically within the TA process. Instead of externally perturbing aggregate demand, PPTA injects structured noise at the individual trip level. This preserves privacy while ensuring equilibrium feasibility through chance-constrained optimization, unifying privacy protectweion and traffic assignment. The framework supports various discrete choice models and noise types, using a moment-based approximation to boost computational efficiency. Our results show PPTA attains a privacy-utility balance beyond extrinsic methods, enabling robust, privacy-aware multimodal routing, network design, and pricing.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:49:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639383</guid>
    </item>
    <item>
      <title>ASB-TDM: Towards flexible and user-friendly transportation demand management in polycentric metropolitan areas</title>
      <link>https://trid.trb.org/View/2659493</link>
      <description><![CDATA[This paper proposes a novel, flexible and user-friendly approach, namely, attraction site-based transportation demand management (ASB-TDM) to alleviate road congestion and improve social welfare in trip-making, where both pull and push strategies are imposed on attraction sites in polycentric metropolitan areas. Under the ASB-TDM, attraction sites - as the objects under government regulation - are charged for causing excessive congestion at traffic hotspots or subsidized to attract consumers and balance network traffic. The impacts of ASB-TDM can be partially transferred to the travelers. There is a game between the government, attraction sites and travelers. Government, as the leader, aims to maximize social welfare, while the attraction sites pursue maximizing their own profits, and travelers choose the destination and path accordingly so as to maximize individual trip-making utilities. A tri-level programming model is established to study the game, which is then solved using a meta-heuristic solution approach integrating method of successive algorithm, diagonalization algorithm, and elitism-based genetic algorithm. Experiments were conducted based on a four-node network, the Nguyen-Dupuis network, and the Sioux Falls Network. Numerical results indicate that the oligopolistic competition among attraction sites can lead to improved social welfare and alleviated traffic congestion due to the dispersion effects of attraction sites in polycentric metropolitan areas, as the induced travel demand is diverted to underutilized areas (e.g., subcenters). The ASB-TDM scheme with government participation further enhances these benefits by optimizing the competitive dynamics through strategic subsidies and charges, leading to superior outcomes compared to the pure market-driven approach.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659493</guid>
    </item>
    <item>
      <title>Self-supervised graph learning for OD flow semantic awareness: harnessing high-order relationships via trajectory chains and POI contexts</title>
      <link>https://trid.trb.org/View/2633667</link>
      <description><![CDATA[Origin-Destination (OD) flow semantic awareness enables the identification of trip patterns across diverse groups and spatiotemporal contexts, revealing spatial relationships and interactions. However, the lack of annotated semantic information in OD data poses a significant challenge, as it hinders the analysis of travel purposes. To address this challenge, we propose a novel self-supervised graph learning framework that leverages bicycle trajectory chains and Points of Interest (POIs) within a 15-minute walking radius. By integrating Graph Attention Networks (GAT) and Hypergraph Convolutional Networks (HGCN), our framework extracts spatial and high-order semantic features from mobility data without requiring labeled training data. A Transformer encoder further enriches node contextual features, enabling the inference of trip purposes and the identification of diverse spatiotemporal travel patterns. Empirical validation in Xiamen, China, demonstrates the framework’s effectiveness in uncovering meaningful OD flow semantics, providing new insights into urban mobility dynamics. The identified flow semantics successfully reveal the underlying mobility patterns and emphasize the synergistic potential of shared bikes within the public transportation network.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633667</guid>
    </item>
    <item>
      <title>Integrated optimization of ride-pooling and shared micro-mobility services with meeting points</title>
      <link>https://trid.trb.org/View/2633663</link>
      <description><![CDATA[This paper studies the integrated optimization of ride-pooling and micro-mobility services within a multimodal transportation network to enhance the likelihood of ride-pooling, reduce the waiting time for passengers, and improve the platform’s profit. We consider an integrated platform that simultaneously provides ride-pooling and micro-mobility services. Instead of door-to-door ride-pooling services, we consider inter-modal transfer at meeting points, where riders can access or egress nearby pickup or dropoff (PUDO) points using micro-mobility vehicles and then get picked up and dropped off at these meeting points by ride-pooling vehicles. We investigate the real-time operational strategies of such a multimodal system taking into account the availability of micro-mobility vehicles at meeting points. In view that riders’ PUDO choices are interdependent in the integrated services, we devise a new dual-graph-based method that enables the decomposition of ride-pooling vehicle routing decisions and assignment decisions. We develop several interconnected subproblems that consider the mutual impacts between ride-pooling and micro-mobility services while we jointly determine rider-vehicles assignment, PUDO selection, ride-pooling vehicle routing decisions, micro-mobility vehicles repositioning decisions, and the transportation of micro-mobility vehicles on ride-pooling vehicles along with riders. We devise efficient algorithms for constructing graphs, identifying feasible trips, and making routing decisions for ride-pooling vehicles based on dynamic programming. The proposed models and algorithms are validated using real-world ride-hailing and bike-sharing data from Manhattan, New York City. The tests demonstrate that our algorithms can efficiently compute optimal matching. The results suggest that jointly operating ride-pooling and micro-mobility services can enhance rider shareability and reduce the repositioning costs of micro-mobility vehicles, thus benefiting both systems. Compared to door-to-door services, the integrated services can increase the number of served riders by more than 10 % while reducing repositioning costs by more than 50 % in the morning peak hour.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633663</guid>
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