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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <language>en-us</language>
    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>BTS Transportation Probe Data Guide: Location Based-Services Data</title>
      <link>https://trid.trb.org/View/2685587</link>
      <description><![CDATA[Location based–services (LBS) data comprise location sightings that are derived from the usage of location-enabled mobile applications (apps) on smartphones and/or cellular-enabled tablets. In other words, LBS data encompass the geographic information generated when mobile apps request position updates. These data are collected through software development kits (SDKs) embedded in mobile apps, creating an array of location pings that can be triggered by user interactions, background processes, or automated system functions. Unlike data sources that are purpose-built for transportation, LBS data are a byproduct of broader commercial mobile app functionality. A fundamental limitation of LBS data is their opportunistic nature—location information is captured whenever apps require positioning for their core functionality, whether for navigation, advertising, social media check-ins, or weather services. This behavior creates highly irregular spatiotemporal patterns that vary dramatically based on individual user behavior, device settings, and app-usage patterns.  This document includes the following sections: Capabilities; Limitations; Vendors and Aggregators; Markets; Scale of LBS Data for Transportation Uses; General Details; Temporal and Spatial Scales of the Data; and Use Cases of the Data.]]></description>
      <pubDate>Thu, 09 Apr 2026 13:41:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685587</guid>
    </item>
    <item>
      <title>Offshore wind farm siting method based on multi-source data fusion</title>
      <link>https://trid.trb.org/View/2687491</link>
      <description><![CDATA[A site selection method integrating natural resource conditions and vessel traffic characteristics derived from the Automatic Identification System (AIS) is proposed to enhance the safety and rationality of offshore wind farm layout. A specific sea area in the East China Sea is selected as the case study. Environmental suitability is evaluated using a grid-based scoring framework considering wind speed, water depth, wave height, distance from shore, and distance to shipping lanes. The weights and comprehensive suitability scores are determined using the Criteria Importance Through Intercriteria Correlation (CRITIC) and Weighted Linear Combination (WLC) method.Based on this framework, spatial clustering is performed on AIS trajectory data to characterize vessel navigation patterns and identify high-frequency shipping corridors with different levels of navigational interference. The natural factor suitability map is then overlaid with the AIS-derived traffic density map to identify areas exhibiting high suitability and low navigational interference. Several optimal candidate areas are accordingly identified.The results indicate that the proposed method effectively addresses both environmental constraints and maritime safety requirements, demonstrating good spatial adaptability and engineering applicability. In addition, the selection process allows flexible adjustment of factor configuration and screening thresholds according to practical development needs, indicating strong potential for practical deployment.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:07:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687491</guid>
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    <item>
      <title>Northeast Freight Corridor Charging Plan: Roadmap Report</title>
      <link>https://trid.trb.org/View/2685485</link>
      <description><![CDATA[This study examines the implementation of charging infrastructure for electric medium- and heavy-duty freight vehicles along key highways in the Northeastern United States (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont). Based on the study, 39 priority sites are identified based on criteria such as power demand forecasting, economic and environmental site impacts, existing plans for utility upgrades, proximity to highways, and available truck parking. This report includes: the importance of corridor charging, the scope of the Northeast Freight Corridor Charging Plan (NFCCP), an overview of the methodology for site selection, site power demand projections, implementation barriers, and recommendations.]]></description>
      <pubDate>Tue, 07 Apr 2026 17:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685485</guid>
    </item>
    <item>
      <title>Passive data for active policy: Evidence-based insights from GNSS and CDR applications in transport planning</title>
      <link>https://trid.trb.org/View/2681351</link>
      <description><![CDATA[This paper presents inherent characteristics of data collection methods and provides a systematic literature review, guided by the PRISMA methodology, to evaluate the potential of Global Navigation Satellite System (GNSS) and Call Detail Record (CDR) data as tools for supporting transportation policy, with a focus on their capacity to complement or replace traditional travel surveys. The topic is of growing importance due to the increasing availability of passively collected mobility data and the limitations of conventional survey methods, such as high costs, infrequent updates, and respondent fatigue. The review highlights the added value of GNSS and CDR data in providing real-time, large-scale, and high-resolution insights into travel behaviour, capabilities not attainable through traditional means. While these data sources currently fall short in capturing subjective and socio-demographic information intrinsic to travel diaries, the paper underscores their utility in dynamic travel analysis, demand modelling, and policy evaluation when integrated into hybrid data collection frameworks. The findings emphasise that GNSS and CDR data are not outright replacements, but valuable complements to traditional surveys, with significant potential to enhance evidence-based transport planning and decision-making.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:36:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681351</guid>
    </item>
    <item>
      <title>City Mobility and Night Life Monitor</title>
      <link>https://trid.trb.org/View/2579248</link>
      <description><![CDATA[This paper presents an Internet of Things (IoT) system designed to collect and analyse information regarding the travel patterns and movements of individuals in densely populated locations, in the context of smart cities. People’s movements are retrieved from coarse-grained aggregated cellular network data without collecting sensitive information from mobile devices and users. These data were provided by a Portuguese cellular operator to the Lisbon City Council to characterize people movements in the city. In this sense, the mobile phones act as useful sensor devices for collecting rich spatiotemporal information about human movement patterns. The purpose of this research work is to create a machine learning-based data-driven approach that is able to receive anonymised data from telecommunication operators to provide a big picture about citizen mobility in the city and to identify patterns based on the collected data, in order to provide relevant information for city planning and events coordination. Some of the main applications of the proposed system are the coordination of big events and the management and control of commuting traffic.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579248</guid>
    </item>
    <item>
      <title>Points of Interest in Smart Cities and Visitor Behavior</title>
      <link>https://trid.trb.org/View/2579246</link>
      <description><![CDATA[Smart cities leverage technology and data to enhance the quality of urban life, including the management of points of interest (POIs) and visitor experiences. This paper explores the relationship between POIs and visitor behavior in smart cities, examining the impact of technology-driven solutions on understanding, analyzing, and optimizing visitor experiences. It highlights the importance of data-driven approaches in identifying and managing POIs, enhancing visitor satisfaction, and driving economic growth. The paper reviews existing literature, discusses key concepts, and presents case studies to illustrate the role of POIs in smart cities and their influence on visitor behavior. Our major contribution is a data driven approach to extract useful information from real data to municipality decisions and understand the problem. It concludes with recommendations for future research and practical implications for city planners, policymakers, and tourism authorities.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579246</guid>
    </item>
    <item>
      <title>Dynamic Stiffness Prediction in Cracked Cantilever Beams Using Enhanced ANN Techniques</title>
      <link>https://trid.trb.org/View/2669780</link>
      <description><![CDATA[This research paper provides a comprehensive study on how Artificial Neural Networks (ANNs) can be deployed to predict the stiffness characteristics of a cantilever beam with a crack of various depths and positions. The most destructive source of failure is considered to be vibration, so the major focus of this paper will be on how the cracks affect the modal stiffness. This study has various applications, such as airplane wings, bridges, stadiums, and arenas. A common research gap was noticed amongst the existing studies; the position of the cracks in the cantilever wasn’t considered, but this paper discusses how the location of cracks severely affects the dynamic behaviour of the cantilever. This study was done by carrying out modal analysis on a cantilever of the same dimensions with different crack configurations. Various crack dimensions and orientations were analysed to understand the effects of the crack on the dynamic behaviour of the cantilever. From the modal analysis results, we evaluated the natural frequency of the cantilever beams with various crack depths and locations. A decrease in natural frequency was observed as the crack depth increased, from which we can infer that the cantilever will experience resonance at much lower external vibration, which makes the structure unreliable. Cracks near the support markedly lower natural frequency due to maximum shear force and bending there, whereas free-end cracks have a negligible impact compared to a reference cantilever. Simulation results feed an ANN, enabling it to accurately predict dynamic characteristics for any combination of crack depth and position. The developed ANN model achieved high prediction accuracy with a Mean Squared Error (MSE) of less than 1x10-5 and an R2 value exceeding 0.998 on the test dataset, demonstrating its robustness as a tool for structural health monitoring.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669780</guid>
    </item>
    <item>
      <title>Spatiotemporal Patterns and Influencing Factors in Intercity Travel within Urban Agglomerations</title>
      <link>https://trid.trb.org/View/2664380</link>
      <description><![CDATA[The investigation of spatiotemporal characteristics and patterns in intercity travel within urban agglomerations constitutes a pivotal component of urban transportation planning. However, the vast spatial expanse of urban agglomerations and the high costs associated with traditional surveys pose challenges in gathering comprehensive data that reflect intercity travel dynamics accurately. This study harnesses mobile signaling data and applies an enhanced Nonnegative Matrix Factorization (NMF) technique to uncover spatiotemporal travel patterns within the Yangtze River Delta urban agglomeration. The analysis identifies three distinct spatiotemporal travel patterns: leisure and business dual-driven travel (LBT), outbound tourism and family visits (OTV), and return trips related to tourism and family visits (RTV). In addition, the Quadratic Assignment Procedure (QAP) model traditionally utilized in social network analysis is employed to examine the factors influencing these travel patterns. The findings indicate that macroeconomic factors, spatial proximity and travel convenience uniformly influence all of the identified patterns, whereas factors related to Work-life balance and tourism and leisure exhibit varying degrees of significance across the patterns. The results further demonstrate that the critical influencing factors are aligned closely with the spatiotemporal distribution characteristics of each pattern, corroborating the efficacy of the proposed methodology in mining and analyzing spatiotemporal travel patterns.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664380</guid>
    </item>
    <item>
      <title>The mediating role of agglomeration economies in high-speed rail’s heterogeneous impact on firm location decisions</title>
      <link>https://trid.trb.org/View/2664374</link>
      <description><![CDATA[High-speed rail (HSR) has the potential to generate significant network externalities by improving accessibility and connectivity, which can foster agglomeration economies and influence firm location decisions. However, these effects are not universal and may vary across regions and sectors. This study examines the varied benefits of HSR on accessibility and connectivity improvements across different sectors, focusing on the mediating roles of localization and urbanization economies in firm location decisions. Findings indicate that the enhanced transport accessibility and connectivity induced by HSR attract businesses to cluster in cities along railway corridors, demonstrating significant geographical spillover effects. While HSR dummy and connectivity provides network-wide "borrowed size" profits, while accessibility has increased inter-regional competitiveness. These enhancements predominantly benefit Scientific research, Technological services, Real estate, and the Leasing/Business services. In all sectors, accessibility has a more significant impact on firm entry than connectivity. Agglomeration economies considerably influence the effect of HSR on firm location decisions, exhibiting substantial variation in mediation strength across different sectors. These findings present novel avenues for utilizing HSR infrastructure to guide spatial economic reconfiguration and industrial enhancement towards sustainable regional development.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664374</guid>
    </item>
    <item>
      <title>Integrated Emergency Medical Facility Location and Patient Dispatching Under Uncertainty</title>
      <link>https://trid.trb.org/View/2610644</link>
      <description><![CDATA[In the face of a sudden public health emergency caused by a new infectious disease, it is necessary to establish a multi-level emergency medical facility (including primary and superior facilities) to address the surge in medical needs. In this context, traditional hospitals are responsible for patient screening, primary emergency medical facilities are responsible for treating mild cases, and superior emergency medical facilities are responsible for treating severe cases. Against the backdrop of uncertainties such as patient self-referral and the autonomous progression of the disease, we address an important problem of integrated emergency medical facility location and patient dispatching under uncertainty and propose a multi-stage stochastic programming model to formulate the problem. For a deterministic model under a given set of scenarios, a Decomposition-based Dual-level Heuristic (DDH) algorithm is proposed to efficiently solve the problem, where the upper level employs tabu search to optimize the location scheme, and the lower level utilizes a patient allocation heuristic to provide an optimized patient dispatching solution. Numerical experiments are conducted using Wuhan, China, the epicenter of the COVID-19 outbreak, as an example. The results show that the DDH algorithm achieves high quality solutions close to those obtained by state-of-the-art solver CPLEX but with significantly reduced computational overload. The DDH algorithm is also compared with the progressive hedging algorithm and genetic algorithm, showing its superior performance in terms of solution quality and computational efficiency. Through extensive data analysis, valuable conclusions and managerial insights are obtained, providing useful references for emergency response in similar public health emergencies in the future.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610644</guid>
    </item>
    <item>
      <title>Hierarchical Queries for 3D Lane Detection Based on Multi-Frame Point Clouds</title>
      <link>https://trid.trb.org/View/2610629</link>
      <description><![CDATA[3D lane detection based on multi-frame point clouds is a critical task for autonomous driving. The challenge lies in efficiently performing temporal fusion using multiple data frames with incomplete yet complementary contexts. Existing methods either directly concatenate consecutive frames, avoiding intrinsic limitations of the raw data, or fuse entire feature maps, without distinguishing lane-related features from backgrounds. These solutions exhibit room for improvement in both precision and efficiency. In this paper, we propose an end-to-end lane detection network with hierarchical queries, which decodes lane features at different levels in a top-down manner for high-precision localization. This framework can be deployed on multi-frame inputs, as it efficiently achieves lane-related sequence fusion with reduced computational costs and improved inference speed. Specifically, we design semi-parametric lane geometry representations to model lanes as parametric curves and discrete points. Accordingly, hierarchical queries are proposed to focus on two-level lane geometries, including curve queries and point queries. Curve queries capture global structures of lanes projected onto the bird’s-eye-view (BEV) flat ground, while point queries aggregate multi-frame sequences obtained through curve-guided sampling, acquiring comprehensive and reliable point-level features. In the training stage, our proposed curve matching and point localization loss optimizes the detected lane geometries at both levels. Experiments conducted on the self-collected MultiBEV dataset validate that our method outperforms previously published single-frame and multi-frame methods. Codes are released at https://github.com/lrx02/HQNet]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610629</guid>
    </item>
    <item>
      <title>Hysteresis behind a freeway bottleneck with location-dependent capacity</title>
      <link>https://trid.trb.org/View/2643248</link>
      <description><![CDATA[Macroscopic fundamental diagrams (MFDs) for traffic networks have gained theoretical and empirical support with new data collection technologies. However, well-defined MFD curves only exist for specific network topologies and are subject to disturbances, particularly hysteresis phenomena. This study examines hysteresis in MFDs and Network Exit Functions during rush hour conditions. We apply the LWR theory to a highway corridor with a downstream bottleneck and identify a figure-eight hysteresis pattern. We analyze the impact of road topology and demand patterns on hysteresis formation analytically. Empirical data from two bottlenecks provides statistical evidence that continuous bottlenecks cause less hysteresis than discontinuous ones. Our observations confirm counter-clockwise loops in real conditions, attributed to demand asymmetries through theoretical analysis. Numerical experiments using the Cell Transmission Model demonstrate that even slight capacity reductions in homogeneous sections can significantly decrease MFD hysteresis while maintaining downstream outflow, achievable through standard traffic control measures like ramp metering.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643248</guid>
    </item>
    <item>
      <title>Crowdsourced task dispatching for the shared electric vehicle relocation problem: a hybrid variable neighbourhood search and genetic algorithm</title>
      <link>https://trid.trb.org/View/2643246</link>
      <description><![CDATA[Shared electric vehicle relocation (SEVR) is essential to the shared mobility and cost-benefit of a one-way, floating-station vehicle-sharing system. This study investigates the crowdsourced task dispatching problem for SEVR to rebalance the spatial variation in supply and demand under random demand. The objective is to minimize total costs by dynamic matching among relocation tasks, stations, and crowdsourced dispatchers. Single-task crowdsourcing (STC) and multitask crowdsourcing (MTC) dispatching models are presented. A hybrid algorithm, combining an improved genetic algorithm with variable neighbourhood search, is devised to solve the problem. Scenario analysis, using trajectory data of electric taxis in Changchun City, shows that compared with the benchmark algorithm, the hybrid algorithm improves the solution quality by 5.09% and reduces the running time by 31.20%. STC is preferred over MTC in the low supply-to-demand density (R) scenario, though MTC is not rejected. However, MTC performs more effectively in the medium and high R scenarios.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643246</guid>
    </item>
    <item>
      <title>Hub-and-spoke network design considering multiple delivery requirements under uncertainties</title>
      <link>https://trid.trb.org/View/2643221</link>
      <description><![CDATA[To satisfy customers' personalized delivery demands and expand the application of hub location models, this paper introduces a hub location problem with multiple service levels and mixture uncertainties, in which two or more allowed delivery times are provided for shipment. In an expected cost-minimization context with delivery-time restrictions, the researched problem explicitly considers fixed hub number, single-assignment pattern from hubs to demand nodes, diversified transportation modes, and two types of real-life uncertainties (stochastic customer demand and fuzzy transportation cost). To help eliminate the mixture uncertainties in the proposed mathematical model, the expected value model and fuzzy chance constrained programming are utilized. By employing the existing fuzzy arithmetic and the newly-proposed operational law of 𝘺-pessimistic value for functions of regular fuzzy numbers, two novel two-phase solution approaches are designed, respectively. The effectiveness of solution approaches and the necessity of uncertain parameters setting in the model are comprehensively illustrated by a series of numerical experiments based on Turkish data set.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643221</guid>
    </item>
    <item>
      <title>Exploring efficiency and spatial equity trade-offs in docked bike-sharing</title>
      <link>https://trid.trb.org/View/2643191</link>
      <description><![CDATA[This study addresses limitations in bike-sharing planning by integrating a disaggregated demand model based on individual user behavior, generated through agent-based simulations, with a supply model derived from spatial analysis. These inputs are applied to the maximal covering location problem to create a comprehensive framework that evaluates interactions between spatial configurations, station size, scale, and design variables while examining trade-offs between efficiency and equity objectives. Applied in Vienna, Austria, the methodology simulates 1,492 individual demand points, optimizes 1,172 candidate locations, and tests 17 scenarios. By enabling precise distance measurements between users and stations, this approach improves station density predictions and resource allocation. Metrics such as daily trips per station, average bike usage, and population coverage highlight performance at the most granular level. Special attention is given to vulnerable groups, such as low-income populations and immigrants, offering insights into the inherent conflict between efficiency and equity to inform urban mobility planning.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643191</guid>
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