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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>Children’s organised out-of-school activities as a source of travel demand: a critical scoping review</title>
      <link>https://trid.trb.org/View/2719381</link>
      <description><![CDATA[Recent decades have seen a shift from unstructured play towards organised out-of-school activities (OOSA) for children, now often considered a mandatory part of children's upbringing. While travel for OOSA likely has a significant contribution to the notorious car-dependence of families, it remains under-researched. This literature review aims to frame and appraise this complex source of travel demand, to provide an empirical and conceptual starting point for further research. We conducted a structured search across 17 leading transport and mobility journals, followed by forward and backward snowballing. Despite a rigorous search strategy, we found only 21 empirical studies on the mode-share and determinants of travel to children's OOSA. Drawing on social practice theory, the review evaluates not only material and infrastructural conditions for travel, but also the cultural scripts and emotional dimensions underpinning participation in OOSA. Following the literature review, we specifically consider the role of parenting as a bundle of practices, including ideals and practices of care and cultivation, all expressed through children's participation in, and travel for, OOSA. The findings reveal a consistent reliance on private cars across diverse geographic contexts, often exceeding car use for the school journey. While structural factors - such as the spatial distribution of activities and poor alignment with public transport - help explain this tendency, they are closely intertwined with cultural scripts of parenting and the emotional dimensions of chauffeuring. In conclusion, the review identifies four possible avenues for further research, including more basic data and nuanced understandings of destinations for OOSA travel, cross-cultural comparisons of access and the cultural shapers of participation, a better understanding of how dimensions of intensive parenting influences travel demand and further engagement with adjacent academic fields and non-English sources to better understand this important aspect of familial travel.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:46:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719381</guid>
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    <item>
      <title>Assessing the impact of the Paris 2024 Olympic Games on bicycle lane utilization with electric bike-sharing data</title>
      <link>https://trid.trb.org/View/2691089</link>
      <description><![CDATA[Major sporting events like the Olympics often catalyze urban mobility investments, yet their lasting impacts on transportation infrastructure remain understudied. This study assesses the effect of the Paris 2024 Olympic Games on bicycle lane utilization, leveraging electric bike-sharing data from Lime and spatio-temporal econometric models. Using a composite cycling network integrating the Réseau Cyclable Olympique (RCO) and other bike lanes from OpenStreetMap, we analyze 6.8 million trips from June to September 2024. A Difference-in-differences framework distinguishes treatment effects between the Inter-competition site links and “Cycling Games” Network, while Spatial Difference-in-differences quantifies spillover effects across the RCO buffer zones. Results reveal a peak surge in bicycle kilometers traveled (BKT) on inter-competition links during peak Olympic days, driven by spectator mobility, though for post-Olympics the utilization exhibited no significant difference compared to “Cycling Games” Network. All post-Olympic buffers experienced demand collapse due to conditional reliance on event-driven traffic in contrast to the Olympic phase. Compared to pre-Olympic, spatial analysis highlights post-Paralympic hierarchical spillovers with buffer zones retaining 26%, 29.6% and 58.7% of Olympic usage respectively. The RCO accommodated substantial bicycle travel demand during the event, thus effectively absorbing the impact of Olympic-induced traffic surges. The RCO’s legacy demonstrates transient demand capture and spatial dependency, emphasizing the need to embed Olympic bike lane infrastructure within permanent cycling networks. Policy implications stress integrating event corridors with long-term urban mobility plans to sustain usage and mitigate post-event fragility. This study advances evidence-based strategies for converting mega-event investments into resilient, low-carbon transportation systems.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691089</guid>
    </item>
    <item>
      <title>Unveiling the key drivers of travel demand via hotspot analysis: a new approach to mitigate the modifiable areal unit problem</title>
      <link>https://trid.trb.org/View/2675519</link>
      <description><![CDATA[With the accelerated urbanization process, accurately understanding urban travel demand has become increasingly important. This study proposes a Hotspot Detection-based Analysis Method (HDAM) for key drivers of travel demand, which aims to minimize the impact of the Modifiable Areal Unit Problem (MAUP) on travel analysis. Focusing on urban multi-density distributed data, HDAM effectively identifies local travel hotspot areas through an adaptive hotspot detection method and constructs buffer zones around these hotspots as analysis units. On this basis, the study further proposes a hierarchical modeling approach to explore the key driving forces influencing travel demand under different levels, forming a data-driven framework for analyzing influencing factors. The results demonstrate that the HDAM method can effectively capture the spatial distribution characteristics of travel demand, and the hierarchical modeling approach significantly enhances the accuracy and reliability of the analysis. The method proposed in this paper helps to deeply explore the formation mechanism of travel hotspots and provides a scientific basis for urban planning and traffic management.]]></description>
      <pubDate>Fri, 10 Jul 2026 12:35:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675519</guid>
    </item>
    <item>
      <title>Quantifying aggregate-level telework occasions and their impacts on vehicle-miles traveled in the US</title>
      <link>https://trid.trb.org/View/2706570</link>
      <description><![CDATA[The COVID-19 pandemic significantly affected individuals’ daily lives and the economy at large, in particular generating unprecedented plummets of all travel demand indicators. In the US, the annual vehicle-miles traveled (VMT) in 2020 plunged by 11% compared to VMT in 2019, and at the national scale had not fully recovered to pre-pandemic levels by 2023. On the other hand, teleworking was widely adopted during the pandemic, and the share of teleworkers has remained high. This study aims to quantify the aggregate amount of teleworking for three regions (US nationwide, Dallas-Fort Worth-Arlington, and Washington-Arlington-Alexandria), and to forecast VMT while accounting for telework effects. We build national/metropolitan level (pre-pandemic) VMT models, propose a method of accounting for telework engagement and adjust VMT predictions accordingly, and evaluate the accuracy of our telework-adjusted VMT forecasts using data through 2023. We argue that for better VMT forecasting, the number of telework occasions should be accounted for, and thus we discuss how to estimate them. We estimate that, in 2019, there were about 17.8 million teleworkers and about 1.5 billion telework occasions at the national level. The pandemic sharply increased the share of teleworkers, and that share has remained substantially higher than pre-pandemic. This implies that about 1.6% and 4.3–5.3% of US annual VMT were reduced by teleworking in 2019 and 2020–2023 respectively. After accounting for the additional telework effects, our VMT forecasts approach the observed VMT more closely errors of 6.2% before vs. 2.6% after, and 4.3% vs. 1.4%, in 2020 and 2021 respectively.]]></description>
      <pubDate>Fri, 10 Jul 2026 12:35:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706570</guid>
    </item>
    <item>
      <title>Correlation between Public Transport Stop Demand and the Number of People Living in Different Distances in Regions</title>
      <link>https://trid.trb.org/View/2724771</link>
      <description><![CDATA[Properly developed public transport infrastructure, a well-organised public transport route network and mixed territorial development contribute to regional development, strengthen the regional labour market and help reduce social exclusion. Mobility issues in sparsely populated areas receive less attention from policy makers and territorial planners than in cities. The accessibility of public transport in sparsely populated and difficult to reach rural areas is less studied. The need for public transport for residents depends not only on the distribution of places of work, education, and leisure of residents, on their transport mobility, but also on public transport infrastructure and the supply of transport. It is ac-cepted that in European cities the average distances to public transport stops are less than 500 m. In re-mote, sparsely populated areas, the distances are much greater. The analysed foreign examples showed that in rural areas the distance varies from 500 m to 4.5 km. Collectively, these studies demonstrate that no consensus has yet been reached on the optimal walking distance to public transport stops that would ensure adequate service accessibility for residents of sparsely populated areas. The purpose aim of this study, based on data from one Lithuanian region (Klaipeda district municipality), is to identify statistically significant distance thresholds that influence public transport use by integrating Geographic Information System (GIS) based spatial population data with passenger demand analysis, thereby cre-ating a data-driven basis for optimizing public transport stop locations in sparsely populated regions. The study was conducted in 4 steps. In the Step 1, a database of stops was prepared, where groups of stops serving the same population were combined. In the Step 2, data on the need for stops were col-lected and the main service distances of stops that will be studied were determined. In the Step 3, the population of residents living at selected distances from specific stops was calculated, thus forming the main database that will be studied in the Step 4. In the Step 4, the level of dependence between stop demand and population was found at different distances. The study showed that the maximum distance at which the dependence remains strong is 1.5 km from the stops. Longer distances do not seem attrac-tive to residents and they no longer consider public transport as an option for making a trip and private vehicles are most often chosen. It has also been found that with smaller distances between stops, the speed of public transport decreases significantly and thus increases travel time for residents who al-ready travel long distances, thus taking up a significant part of their daily journey, and at the same time the correlation between 500 m and 300 m does not have a significant difference. Based on this, it is not recommended to arrange stops more often than every 500 m in rural regions.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724771</guid>
    </item>
    <item>
      <title>The Decoupling Effect Analysis of Meteorological Comfort on Urban Rail Transit Ridership</title>
      <link>https://trid.trb.org/View/2663058</link>
      <description><![CDATA[This study investigates the decoupling relationship between meteorological comfort and urban rail transit ridership in China. Daily meteorological data and passenger volume data from 28 major cities were processed to construct a meteorological comfort index using the entropy weighting method, in which precipitation levels were converted into continuous values based on national standards. A decoupling model was then applied to examine the dynamic interaction between weather comfort and transit use. The analysis identifies three classes of decoupling states: Class A, where passenger travel remains stable despite unfavorable weather; Class B, where moderate sensitivity to meteorological variation is observed; and Class C, where travel is strongly influenced by weather conditions. Results show that most cities predominantly fall under Class B, but with notable fluctuations across seasons and regions. The findings highlight that meteorological comfort does not uniformly determine ridership, but instead reveals differentiated patterns of resilience and vulnerability across urban rail systems. This contributes to a deeper understanding of how external environmental factors interact with public transit demand and provides methodological guidance for improving the robustness of transport planning under climate variability.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663058</guid>
    </item>
    <item>
      <title>Joint Optimization of Passenger Flow Control and Train Skip-Stopping for Overcrowded Metro Lines: A Multi-Agent Reinforcement Learning Approach</title>
      <link>https://trid.trb.org/View/2663055</link>
      <description><![CDATA[During rush hours, the capacity of metro in megacities is insufficient to meet the travel demand, resulting in oversaturation and high risk on platform in stations, especially transfer stations. This paper addresses this problem through the joint optimization of some operational interventions, aiming to alleviate passenger overloads while maintaining travel efficiency. To make the model more realistic, the stochastic characteristics of passengers are considered, including the probability distribution of passenger arrival time, inbound and transfer walking times. To provide a high-quality solution for the complex constraint model, three cooperative agents—governing passenger inflow, transfer flows, and train skip-stopping mode—are architected within improved Double Deep Q learning Network (IDDQN) to form a multi-agent reinforcement learning solution. Empirical validation on Beijing Metro Line 13 and Changping Line demonstrates that the multi-agent framework proposed in this paper can eliminate 100% of passenger over-limit flow while reducing the average waiting time of passengers. It also has a significant improvement in reducing stochastic characteristic impact and accelerating convergence.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663055</guid>
    </item>
    <item>
      <title>Impacts of mobility and social networks on social activity-travel participation using location-based social network data</title>
      <link>https://trid.trb.org/View/2658022</link>
      <description><![CDATA[This paper investigates the relationship between social networks and the activities they generate, by exploring inter-social-activity durations as a proposed measure of social activity participation frequency. To model the proposed measure, data were collected and processed from a publicly-available dataset sourced from the location-based social networking service Gowalla. The data include information from 3065 Texas Gowalla users, regarding social activity-travel behavior, and performance of modularity- and surprise-based community detection. To account for the longitudinal nature of the data, and for possible spatial instability of the model parameters across two major Texas cities, a grouped-random-parameters hazard-based duration modeling approach with heterogeneity in means is employed, and separate models are estimated for Austin and Dallas users. The results suggest that social activity participation frequency is affected by individual mobility, and by a number of social network effects, such as ego social network size, social group variety, and local closeness centrality. The findings call for a thorough investigation of the transportation system and social network interrelationships.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658022</guid>
    </item>
    <item>
      <title>Factors influencing car ownership in Toronto: Insights from a hurdle-ordered model</title>
      <link>https://trid.trb.org/View/2688660</link>
      <description><![CDATA[This study examines the effects of household socio-demographics, dwelling characteristics and the built environment on household auto ownership using data from a survey of 2007 households in the Greater Toronto Area (GTA). A two-stage hurdle model was constructed, consisting of a binary probit model of vehicle ownership (0 or 1+ cars) and an ordered probit model of fleet size (1, 2, or 3+ cars). Variables representing the five dimensions of density, diversity, destination accessibility, distance to transit, and design were included in the model. It was found that household socio-demographics and dwelling characteristics are the principal drivers of auto ownership, as shown by a greater number of variables significant at the 1% level and larger partial effects for these variables. Among the built environment variables, density, and destination accessibility were the most significant. Overall, the factors influencing ownership and fleet size were found to be the same, though there were some notable exceptions to this. These results show that policymakers seeking to reduce car dependency should focus on policies directly targeting the households, rather than those which target them indirectly through changes to the built environment around their residential location.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688660</guid>
    </item>
    <item>
      <title>Long-run trends in usage, service levels, and governance of public transport in Sweden</title>
      <link>https://trid.trb.org/View/2686792</link>
      <description><![CDATA[An efficient public transport system providing good service to the public is a key element of sustainable development. The aim of this paper is to describe and explain the long-run development of public transport supply variables in Sweden. The analysis uses data on the supply of vehicle kilometres and fare levels, as well as on the development of the cost of providing services. This is seen in light of structural changes, with regression analysis being used to explain the development of the aforementioned variables and important policy changes. The analysis utilises yearly data from Swedish counties from 1986 to 2022, totalling 756 observations. The findings show a cost increase associated with the policy changes made in 1995 (related to Sweden becoming a member of the EU) and a cost decrease associated with changes made in 2012 (related to a new public transport act). The supply of vehicle kilometres has been positively affected by changes introduced in 1995. Fare levels were reduced due to changes introduced in 1989, when public transport authorities were given the opportunity to use competitive tendering. To our knowledge, this is the first longer comprehensive study to have considered more recent reforms and service changes in the Swedish public transport sector.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686792</guid>
    </item>
    <item>
      <title>How teleworking reshapes travel distance and mode use for work and non-work tours: A panel analysis</title>
      <link>https://trid.trb.org/View/2688693</link>
      <description><![CDATA[This paper investigates the effects of teleworking on tour generation and travel distance for home-based work and non-work tours using various modes of transportation. We analyzed three waves of the Netherlands Mobility Panel, collected in 2017, 2018, and 2019. Six zero-inflated gamma mixed effect models of travel distance by car, active modes, and public transportation for work and non-work tours were estimated with teleworking duration as a key explanatory variable. Our models incorporated differences in traveler’s mode use preferences by categorizing individuals into exclusive mode users and multimodal travelers based on their levels of car, active mode, and public transportation use across the three survey waves. The results of our analyses revealed that teleworking led to fewer work tours but also to increased distances of non-work tours using cars and active modes. In addition, while teleworking was found to reduce the distances of work tours, these reductions were greater for travelers who exclusively used the car or active modes compared to multimodal travelers. Our findings suggest that the relationship between teleworking, tour generation, and travel distances varies by tour purpose, type of mode used, and individuals’ long-term mode use preferences. This highlights the importance of considering travel modes and trip purposes in investigating the impact of teleworking.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688693</guid>
    </item>
    <item>
      <title>Electric integrated demand-responsive transport services with capacitated charging stations, multiple depots, and customer rejections</title>
      <link>https://trid.trb.org/View/2686683</link>
      <description><![CDATA[This study addresses the integrated dial-a-ride problem using a fleet of electric vehicles. We propose a mixed-integer-linear programming modelling approach considering multiple depots, customer rejection, partial recharge policy and capacitated charging stations. State-of-the-art mixed-integer-linear programming approaches can solve the problem exactly for only less than 10 requests. This is due to the cumbersome modeling of partial routes in a mass transit network, where the number of arcs expands rapidly with the network size. We developed an efficient departure-expanded transit graph to model the problem efficiently by trimming off unnecessary arcs, and we include a preprocessing step based on time-window tightening on the timetabled transit network. We test the proposed method on a set of test instances with up to 50 requests and different initial battery levels of vehicles within a four-hour computational time limit. The results show that the problem can be solved optimally up to 20 customers and about 95% faster compared to the state-of-the-art. We developed a novel compact arc-based formulation for optimizing electric vehicle routing problems with capacitated charging stations. Our computational results provide a reduction in computational time by up to two digits compared to the state-of-the-art replication-based method.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686683</guid>
    </item>
    <item>
      <title>Trip Generation and Traffic Prediction: Evaluating Alternative Traffic Prediction Methods for Traffic Impact Analysis</title>
      <link>https://trid.trb.org/View/2721744</link>
      <description><![CDATA[ Traffic impact analysis (TIA) forecasts how a proposed development will affect the surrounding transportation system and what improvements, if any, are needed to improve safety and efficiency.  In addition to trips generated by the proposed development (typically based on rates published by the Institute of Transportation Engineers [ITE], a TIA will include “background traffic” which is traffic on the roadway generated from other sources. The assumption is that background traffic growth is not driven by the proposed development, nor by other parcels approved but not yet built within the study area.  The purpose of this research is twofold: (1) to determine the extent to which this assumption is valid and (2) if the assumption is not valid, to identify best practices to account for this.  This research will thus (1) review practices from other states regarding ways to combine background traffic growth and site-specific trip generation in the TIA process; (2) compare forecast and observed trip generation for a selected set of developments; and (3) conduct an additional case study to compare the traditional approach of doing a TIA (based on ITE rates) and an approach based on a travel demand model.  Lessons learned from this effort may inform Virginia Department of Transportation (VDOT)’s Traffic Impact Analysis guidelines.  This research need tied with another for being the top-ranked research need by the Transportation Planning Research Advisory Committee (TPRAC).]]></description>
      <pubDate>Thu, 02 Jul 2026 11:02:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721744</guid>
    </item>
    <item>
      <title>Integrating traffic demand management and spatio-temporal analysis of urban growth in emerging Nigerian cities</title>
      <link>https://trid.trb.org/View/2681480</link>
      <description><![CDATA[Traffic congestion in emerging African cities is profusely influenced by the interaction between rapid urban growth and poor Traffic Demand Management (TDM) systems, especially within the key commercial hubs. This study integrates field-based traffic demand analysis with spatio-temporal assessment of built-up growth to investigate congestion dynamics in three selected commercial hubs (Kuto, Itoku, and Lafenwa) of Abeokuta, Ogun State, Southwestern Nigeria. The results indicated profound spatial intensity within 500 m buffers of the hubs alongside severe congestion driven not only by land-use density but by functional heterogeneity, institutional constraints, and behavioural travel patterns. Conspicuously, hubs with lower built-up density but stronger regional connectivity experienced higher traffic intensity, underscoring the role of accessibility and urban function over physical density alone. The study showed that combining geospatial urban-growth analysis with traffic demand indicators provides a more subtle understanding of congestion in medium-sized cities. Practically, the results revealed the necessity for context-sensitive TDM policies that integrate land-use planning, pedestrianisation, parking regulation, and public transport improvement. Theoretically, the study contributes to urban transport literature by advancing an integrated spatial–behavioural framework for analysing congestion in rapidly urbanising cities of the Global South. Consequently, incorporating spatio-temporal urban-growth analysis into traffic modelling strengthens TDM strategies and supports Sustainable Cities and Communities (SDG 11) through more inclusive, resilient, and efficient urban mobility.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:06:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681480</guid>
    </item>
    <item>
      <title>A two-stage stochastic optimization framework for integrated passenger-freight bus transport under time-varying capacity constraints</title>
      <link>https://trid.trb.org/View/2684465</link>
      <description><![CDATA[This paper develops an optimization framework for integrated passenger-freight bus transport (IPFBT), which leverages the spare capacity of public bus systems to enhance sustainable urban freight delivery. We formulate a two-stage stochastic programming model that jointly optimizes the strategic location of passenger-freight integration stations and operational-level freight routing under time-varying passenger demand. The model innovatively introduces a link availability matrix to characterize dynamic capacity constraints under the passenger-priority principle, and designs temporal shift and spatial shift strategies to address capacity shortage during peak periods. To solve this large-scalef, uncertainty-driven problem, we design a decomposition-based algorithm combining a L-shaped framework with Lagrangian relaxation. The master problem is efficiently solved via a greedy set-cover heuristic, while subproblems are decomposed into parallelizable single-commodity shortest path problems. Adaptive multiplier updates and feasibility restoration mechanisms are incorporated to ensure convergence and solution quality. A real-world case study based on the Shijingshan District bus network in Beijing demonstrates the model’s effectiveness and computational scalability. The study reveals phenomena such as obvious economies of scale and “capacity paradox” in the system, and provides implementation recommendations including phased deployment strategies for urban managers.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684465</guid>
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