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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>Decision generation for interlining bus scheduling: a large language model-empowered approach</title>
      <link>https://trid.trb.org/View/2692668</link>
      <description><![CDATA[The expansion of bus networks amid fleet constraints poses challenges for traditional single-line scheduling. While interlining scheduling offers potential efficiency gains without significant fleet growth, the computational complexity has hindered widespread adoption. To fill this gap, this study explores a novel paradigm that leverages the sequence reasoning and generative capabilities of Large Language Models (LLMs) for complex bus scheduling. Utilizing multi-source data, a multi-task fine-tuning framework is designed for LLMs to jointly master core scheduling tasks such as vehicle assignment, en-route travel time prediction, and demand forecasting. The results reveal that the fine-tuned Qwen2.5-14B-1 M−Instruct model can balance solution executability and computing time. The multi-task fine-tuning jointly optimizes prediction and scheduling, achieving an MSE below 30 for both demand and travel time prediction, decreasing the fleet size and vehicle trip variation by 3.6% and 12.5%, respectively. The complete prompt achieves optimal performance across all evaluation metrics, underscoring the necessity of multi-perspective prompt engineering in constrained generative scheduling. Moreover, the fine-tuned model exhibits robust zero-shot generalization to new routes, reducing fleet size by 5% and lowering trip distribution variance by 4%. This work demonstrates the viability of LLMs for data-driven bus scheduling.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692668</guid>
    </item>
    <item>
      <title>Estimating an e-scooter origin-destination model leveraging Yelp POI data for enhanced urban mobility insights</title>
      <link>https://trid.trb.org/View/2592070</link>
      <description><![CDATA[E-scooter sharing programs have transformed urban mobility, providing an environmentally friendly solution for short-distance travel. Analyzing Origin-Destination (OD) travel behavior associated with e-scooter trip patterns is essential for effective urban planning, infrastructure enhancement, and policymaking. However, while numerous studies exist on developing OD travel behavior for passenger vehicles, there is a significant gap in developing such models for shared transportation modes like e-scooters. This study addresses this gap by estimating an OD model for e-scooter trips, integrating Points of Interest data from Yelp. E-scooter mobility data, traffic analysis zone (TAZ) shapefiles, and crowdsourced data from Louisville, KY, were collected to develop the OD model. Using a gravity-inspired random forest (RF) model, the e-scooter OD trip model was estimated. Findings revealed that total attraction and production have a high positive influence on trip distribution between TAZ, while the distance between TAZ has a significant negative impact. Additionally, the presence of bars, restaurants, shopping malls, and coffee shops strongly influences trip distribution between TAZ, whereas museums and parks have less influence. These results offer valuable insights for planning organizations, informing decisions on the relocation and optimization of e-scooter services to better meet the needs of urban commuters.]]></description>
      <pubDate>Thu, 13 Nov 2025 16:59:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592070</guid>
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    <item>
      <title>Optimizing multi-modal ride-matching with transfers</title>
      <link>https://trid.trb.org/View/2587450</link>
      <description><![CDATA[One of the limitations of ride-sharing is that matched drivers and riders need to have similar itineraries and desired arrival times for ride-sharing to be competitive against other transport modes. By allowing a single transfer at a designated transfer hub, their itineraries need to be only partially similar, and therefore more matching options are created. In this paper, the authors develop an optimal matching approach that matches riders to drivers, taking into account multi-modal routing options to model competition and collaboration between multiple modes of transport. The authors allow for transfers between modes and between multiple drivers. The authors model this as a path-based integer programming problem and the authors develop a simulated annealing algorithm to efficiently solve realistic large-scale instances of the problem. The authors' analysis indicates that a single transfer hub can reduce significantly the average generalized cost of riders and the total vehicle hours traveled by creating efficient matches. As opposed to previous studies, the authors' work shows that ride-sharing not only attracts former public transport users but also former private car users. By allowing for intermodal transfers and by choosing the cost parameters such that transfers are favorable, itineraries where commuters use their car first, before sharing a ride on the second part of their journey, becomes an appealing alternative. Multi-modal ride-matching with transfers has the potential to increase ride-sharing, reduce the number of vehicle hours traveled in private cars, and reduce the number of cars that are present in urban areas during peak hours of congestion.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587450</guid>
    </item>
    <item>
      <title>Temporal transferability of a microsimulation activity-based model: An application in Chile</title>
      <link>https://trid.trb.org/View/2590608</link>
      <description><![CDATA[Urban transport policies in the Global South mostly use traditional trip-based models to replicate and predict individuals' travel in different urban contexts. Given their aggregate nature, they are limited when applied at a more disaggregated scale, contrasting with activity-based models, especially microsimulation-based models, which have been used mainly in the Global North, with few applications in Latin America. In addition, there is a critical need to add empirical evidence to understand the models' temporal transferability, which involves their capability to predict future travel behavior based on estimations with data collected in the present. This paper reports on the replicability and temporal transferability of an activity-based model in Chile. The objective is to understand the challenges of applying these models in these contexts for policy usefulness. The applicability of the model TASHA (Travel Activity Scheduler Household Agent) is studied in two stages: replication and prediction of activities and trips for a base year and temporal transferability of previously estimated parameters to a future year. The model uses trip-based information from conventional travel surveys in a mid-size Chilean city. The exercise provides valuable proof of the principle of several arguments about the advantages of activity-based models such as TASHA. First, the study shows the model's ability to capture current and future behaviour despite data and context limitations. Second, the model supports the analysis of activities in addition to trips, providing a more in-depth assessment of travel behaviour. Finally, and more importantly, the focus on activities, such as end times, brings the opportunity to incorporate a broader range of policies than those traditionally studied in transport.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590608</guid>
    </item>
    <item>
      <title>Paving the way for rural revitalization: Empirical analysis of the 'Sihao Rural Road Policy' and transport mode choice in Inner Mongolia, China</title>
      <link>https://trid.trb.org/View/2563848</link>
      <description><![CDATA[This study investigates the impact of the Sihao Rural Road (SHRR) Program on travel behavior in Qingshuihe County, Inner Mongolia, China focusing on travel mode choice, travel frequency, and travel distance. Using an integrated Structural Equation Model (SEM) and Discrete Choice Model (DCM) framework, data were collected from 127 households between August 2023 and March 2024. The analysis reveals that the SHRR program significantly reduces travel frequency, likely due to improved local accessibility that decreases the need for frequent trips. Simultaneously, SHRR facilitates longer travel distances and promotes greater reliance on motorized modes. This suggests that enhanced infrastructure enables residents to travel farther and more efficiently using private vehicles, motorcycles, and electric bicycles. Car ownership plays a critical role, significantly influencing both travel distance and the adoption of motorized modes. However, its relationship with the use of electric bicycles is more complex, with effects mediated by other factors such as travel distance and frequency. These findings underscore the importance of considering both direct and indirect effects of rural infrastructure policies when evaluating their impact on mobility patterns and transport mode choices.]]></description>
      <pubDate>Fri, 12 Sep 2025 13:38:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563848</guid>
    </item>
    <item>
      <title>Quantifying informal public transport using GPS data</title>
      <link>https://trid.trb.org/View/2573251</link>
      <description><![CDATA[Informal public transport modes transport the largest number of passengers in most developing countries. Despite its significance, limited information is available on the extent of its operations, and passenger counts alone do not provide sufficient insight into network coverage or passenger turnover. GPS tracking has emerged as a valuable tool, yet its potential for understanding minibus taxi operations at the road segment level remains underexplored. GPS studies of informal operators have rarely been extrapolated to volume counts per time period, due to statistical problems (non-representative sampling) and small sample sizes. This paper addresses this gap by developing a methodology to determine the minibus taxi vehicle trip count per street segment from GPS data, to map routes, and identify high-traffic corridors, with an illustrative application in the City of Tshwane, South Africa.The methodology includes data inspection, addressing limitations, and counting trips per street segment using a database and QGIS visualisation. Additionally, the paper outlines detailed steps in QGIS for processing GPS data. The authors show that the method delivers plausible results at the segment level. The methodology can help to address the global South's need for data-driven interventions in its predominant public transport mode.]]></description>
      <pubDate>Wed, 23 Jul 2025 09:15:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573251</guid>
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    <item>
      <title>Operating Manual for the Texas Trip Distribution Package</title>
      <link>https://trid.trb.org/View/2556684</link>
      <description><![CDATA[The Texas Trip Distribution Package is a collection of computer programs designed to perform trip distributions featuring the application of a constrained interactance model. Other programs, available in the package, provide full support. This manual describes the performance capabilities, execution procedures, data specifications, and computational requirements which are related to the usage of the programs.]]></description>
      <pubDate>Mon, 07 Jul 2025 21:59:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556684</guid>
    </item>
    <item>
      <title>Model Construction and Calibration Technical Documentation Draft</title>
      <link>https://trid.trb.org/View/2563988</link>
      <description><![CDATA[This paper describes in detail the process and methodology used within Bismarck/Mandan Metropolitan Planning Organization's (Bismarck/Mandan’s) TP+ transportation planning model. This is a technical reference that documents the methodology and assumptions underling each major step within the model. All of the data provided as input to the model has been either provided by Bismarck/Mandan city planners or produced by the Advanced Traffic Analysis Center (ATAC). This data is compatible with the existing geographic information system (GIS) data system used by both cities. The model has been developed to run in the TP+ modeling system produced by Citilabs and has been completely developed within Citilabs’ CUBE software product. This software provides a method for organizing the script and is used to view and edit the input and output files. The modeling is performed in the following six steps: data preparation, trip generation, trip distribution, mode split, assignment, and calibration.]]></description>
      <pubDate>Tue, 24 Jun 2025 17:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563988</guid>
    </item>
    <item>
      <title>Program Documentation Manual for the Texas Trip Distribution Package</title>
      <link>https://trid.trb.org/View/2549152</link>
      <description><![CDATA[The Texas Trip Distribution Package is a collection of computer programs designed to perform trip distributions featuring the application of a constrained interactance model. Other programs, available in the package, provide full support. The purpose of this manual is to provide data processing personnel with a link between the Operating Manual for the Texas Trip Distribution Package (Research Report 167-1) and the programs contained in the package. The manual describes the operation of the package and provides flowcharts of the programs in the package. Cross references for significant variables and arrays used in the package and formats for all data sets and data cards associated with the package are provided.]]></description>
      <pubDate>Mon, 16 Jun 2025 17:15:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549152</guid>
    </item>
    <item>
      <title>Grand Forks East Grand Forks 2010 Travel Demand Model Update</title>
      <link>https://trid.trb.org/View/2559566</link>
      <description><![CDATA[The Advanced Traffic Analysis Center (ATAC) at North Dakota State University is responsible for updating and maintaining the Grand Forks / East Grand Forks Metropolitan Travel Demand Model (TDM). The model is updated every five years. The model is developed in Citilabs Cube Voyager software and comprises four steps including trip generation, trip distribution, modal split and trip assignment. The model was calibrated to Base 2010 Conditions with trip rates from the Fargo / Moorhead area used for trip generations. The model results were validated against 2010 base year data including trip length frequency distributions, vehicle miles traveled, screenline counts, trip generation totals, and a modeled versus counted traffic volumes. The model was able to replicate the base year 2010 data within acceptable industrial limits.]]></description>
      <pubDate>Mon, 16 Jun 2025 09:17:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559566</guid>
    </item>
    <item>
      <title>How do directed and undirected travel differ? Evidence from year-long bikeshare trips</title>
      <link>https://trid.trb.org/View/2522435</link>
      <description><![CDATA[Among various spatial patterns of bikeshare trips, an interesting pattern is that some bikers come back to the origin in a single trip. This study analyzed two types of travel: directed travel (returning their bikes away from the origin) and undirected travel (returning their bikes at the same or a nearby station). The study examined the characteristics of those bikeshare trips and connected findings with discussions of “traveling for its own sake” in the travel behavior research. Using year-long (2023) trip data in Seoul, the authors modeled the proportion of undirected travel at each station (i.e. where people often take undirected trips) and travel times at the individual-trip level. The authors found that 11% of the bikeshare trips were identified as undirected travel. The results show that weather, temporal factors, land use, and trip characteristics have meaningful effects on bikeshare usage and such effects vary by the travel type. The proportion of undirected travel tends to increase during lunchtime or on weekends and when the bike station is located by the river. In terms of individual travel times, the time of day and the type of bike lanes had heterogeneous effects. People tend to take bikes for longer where bike lanes are protected against other traffic. Additionally, the presence of companions tends to increase travel times. Undirected travel is harder to be predicted than directed travel due to its inherent characteristics. In summary, the bikeshare system is used not only for moving to another place, but also for traveling for its own sake.]]></description>
      <pubDate>Thu, 03 Apr 2025 16:42:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522435</guid>
    </item>
    <item>
      <title>Aggregate crash prediction model based on gravity model: Introducing crash risk distribution concept</title>
      <link>https://trid.trb.org/View/2481718</link>
      <description><![CDATA[Crash prediction models (CPMs) can be valuable for future transportation planning decisions. This study aims to develop CPMs based on the trip distribution step of the common four-step demand models. For this purpose, the Gravity Model is used. For model calibration, the frequency of severe crashes (including the total of fatal and injury crashes) between each origin-destination (OD) pair of traffic analysis zones (TAZs) in the city of Qom in Iran has been used as the dependent variable. The number of trip distributions by purpose, traffic characteristics on the links, and road network characteristics has been used as the explanatory variables. The model validation results show a significant relationship between the mentioned variables. Therefore, in addition to predicting the crash frequency according to trip number changes in the future, the developed model in this study determines the relationship between the crash frequency with the OD characteristics of the trips that lead to crashes. This makes it possible to evaluate the impact of different travel demand management scenarios on safety so that the crash risk (i.e., crash occurrence probability) of trips distributed between TAZs is identified and prioritized and can be planned to improve or reduced them.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:56:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2481718</guid>
    </item>
    <item>
      <title>Estimating MFD model parameters from sparse license plate recognition data: The role of path reconstruction and regionalization</title>
      <link>https://trid.trb.org/View/2488470</link>
      <description><![CDATA[The Macroscopic Fundamental Diagram (MFD) provides a convenient and computationally efficient tool for urban traffic monitoring and control. However, the accurate estimation of the model parameters is a prerequisite for the MFD models to be practically applicable. The significant deficiencies in commonly used data sources underscore the importance of utilizing new data sources to estimate the MFD model parameters. This study proposes a comprehensive framework to estimate critical parameters for multi-region MFD models based solely on License Plate Recognition (LPR) data. An efficient link-based path reconstruction algorithm is introduced to transform observed vehicle trajectories into continuous paths. Considering the inherent incompleteness of observed trajectories, this study presents a novel regionalization framework to match initial or final observation points to specific subregional networks. An adaptive large neighborhood search (ALNS) algorithm is proposed to improve the flow correlation within these subregions. Subsequently, an OD inference model using importance sampling is introduced to estimate the true OD of trips within the identified subregions. The resulting complete vehicle trips can be used to estimate trip length distributions and path flow coefficients. In addition, a method for estimating the MFD shape, tailored for application to sparse LPR data and based on reconstructed vehicle trajectories, is proposed. The effectiveness of these methods is validated through both simulation experiments and empirical studies. This framework is expected to facilitate the practical application of MFD models to realistic road networks.]]></description>
      <pubDate>Thu, 30 Jan 2025 17:09:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2488470</guid>
    </item>
    <item>
      <title>Grand Forks East Grand Forks 2015 Travel Demand Model Update</title>
      <link>https://trid.trb.org/View/2472707</link>
      <description><![CDATA[The Grand Forks East Grand Forks MPO Travel Demand Model (TDM) is updated every five years to reflect new ground truths/data and the advancements in the state-of-the-art in transportation modeling techniques and methods. The current update reflects base year 2015 data. The model is a four-step TDM including trip generations, trip distributions, modal split and trip assignment. The update process involves calibrating the model input parameters and validating the model output with ground truths. For the 2015 base year model, several updates were made to the model to reflect the availability of new and improved data, new and advanced methods in modeling software and the inclusion of long-haul freight movements as part of the model. New data that was used for 2015 model update included: Origin Destination Data (Obtained from Airsage), the traffic analysis tool data, incorporation of truck counts and Freight Analysis Framework (FAF) data to model freights.]]></description>
      <pubDate>Tue, 24 Dec 2024 16:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2472707</guid>
    </item>
    <item>
      <title>Bismarck Mandan 2015 Travel Demand Model Update</title>
      <link>https://trid.trb.org/View/2472706</link>
      <description><![CDATA[The Bismarck Mandan Travel Demand Model (TDM) is updated every five years to reflect new ground truths/data and the advancements in the state-of-the-art in transportation modeling techniques and methods. The current update reflects base year 2015 data. The model is a four-step TDM including trip generations, trip distributions, modal split and trip assignment. The update process involves calibrating the model input parameters and validating the model output with ground truths. For the 2015 base year model, several updates were made to the model to reflect the availability of new and improved data, new and advanced methods in modeling software and the inclusion of long-haul freight movements as part of the model. New data that was used for 2015 model update included: Origin Destination Data (Obtained from Airsage), the traffic analysis tool data, incorporation of truck counts and Freight Analysis Framework (FAF) data to model freights.]]></description>
      <pubDate>Tue, 24 Dec 2024 16:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2472706</guid>
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