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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Exploratory system dynamics modelling of urban travel demand amid electrification, automation, and sharing technologies: insights from Shanghai</title>
      <link>https://trid.trb.org/View/2643285</link>
      <description><![CDATA[Vehicle electrification, automation, and sharing are reshaping urban travel demand. This study utilizes a system dynamics model to forecast travel demand in downtown Shanghai through 2035, incorporating these emerging technologies. 5,000 scenarios are evaluated using Exploratory Modelling and Analysis, and scenario discovery is conducted using the Patient Rule Induction Method. Modelling results reveal two opposite CO₂ emissions trends driven by shifts in travel mode shares. (1) 433 scenarios exhibit a desirable trend, with the largest reduction to 83% of the 2022 emission by 2035; (2) Approximately half of the scenarios show a concerning trend, with the largest increase to 145% of the 2022 level by 2035. These opposing trends are attributed to variations in the pace and acceptance of autonomous vehicle adoption. Our findings underscore the necessity for strategies that enhance non-motorised transport while accelerating the development of emerging mobility technologies and encourage green travel choices among residents.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643285</guid>
    </item>
    <item>
      <title>Analysis of Access Egress Mode Choice for Dhaka’s First Mass Rapid Transit</title>
      <link>https://trid.trb.org/View/2646010</link>
      <description><![CDATA[One of the primary reasons behind the slowest traffic in Dhaka, Bangladesh, is the absence of a structured mass transit system to cater to the travel demand of over 21 million people living in the city. This is the first study to explore the heterogeneity in the access and egress mode choice of the only metro rail transit (MRT) operating in the capital. The data for the study was collected through a questionnaire survey conducted at the 16 MRT stations. Walking, buses, and rickshaws are the most preferred modes for accessing and egressing from the metro stations. The bus's time value was almost 1.6 to 2 times greater while egressing from the stations than accessing. In general, the bus was found to be the mode for the poor and retired individuals, while the job holders and the female travelers preferred rickshaws.]]></description>
      <pubDate>Thu, 12 Mar 2026 16:30:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646010</guid>
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    <item>
      <title>Welfare-optimal public transport fares in Denmark</title>
      <link>https://trid.trb.org/View/2635396</link>
      <description><![CDATA[Public transport fare policies are important in shaping efficient, equitable, and sustainable transportation systems. In this study, we optimise a welfare function across a spectrum of flat and distance-based pricing strategies while also analysing the distributional impacts and effects on equity. The analysis reveals that a Danish distance-based fare system replicating the current prices is sub-optimal from both a welfare economic and equity perspective. Using a comprehensive demand model for weekday trips (under 50km) in Denmark, we demonstrate that a flat fare of 24.2 DKK ( ∼  € 3.24), would improve welfare and equity without causing significant operational changes. Effects are driven by consumer surplus gains and increasing market shares of active modes for shorter distances and public transport for longer distances. These mobility changes yield significant health benefits for the population and reduce environmental costs. Nevertheless, the socio-spatial distribution of welfare effects is uneven, with most gains observed in rural and suburban areas and across younger and lower-income groups.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635396</guid>
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    <item>
      <title>Central Dakota 2025 Base Travel Demand Model Development and Calibration</title>
      <link>https://trid.trb.org/View/2671994</link>
      <description><![CDATA[The Central Dakota Metropolitan Planning Organization (MPO), which includes the cities of Minot, Surrey, and Burlington, has been developing a Travel Demand Model (TDM) to incorporate new data, reflect current travel patterns, and integrate advancements in transportation modeling techniques. The updated model is based on 2024 data, ensuring that it accurately represents the region's transportation system. The model follows the four-step TDM process, which includes trip generation, trip distribution, mode choice (modal split), and trip assignment. Each step is calibrated to reflect real-world travel behavior, allowing for accurate forecasts of traffic demand. The model update process involves adjusting key input parameters and validating the model's outputs against observed traffic data to ensure alignment with actual conditions. Model calibration is an iterative process where parameters are refined in multiple stages until the model meets acceptable accuracy standards. This approach ensures that the model is not only a reliable representation of current conditions but also a powerful tool for forecasting future transportation needs and supporting informed decision-making.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:15:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671994</guid>
    </item>
    <item>
      <title>Advanced Transportation Optimization and Modeling (ATOM)</title>
      <link>https://trid.trb.org/View/2676009</link>
      <description><![CDATA[The U.S. transportation system is experiencing increasing complexity driven by evolving infrastructure, land-use patterns, travel demand, demographic shifts, and rapid advances in vehicle and mobility technologies. Emerging behaviors such as telecommuting, ridesharing, and micromobility, along with changing attitudes toward public transit and vehicle ownership, are reshaping how people and goods move across regions. To ensure that transportation investments remain efficient, resilient, and cost-effective, transportation agencies require advanced, data-driven tools to anticipate and evaluate the system-level impacts of these changes.  

This project develops an advanced transportation modeling and optimization pipeline in Austin, Texas, to evaluate the impacts of alternative strategies and technologies through scenario-based analysis. The system will be built around the Behavior, Energy, Autonomy, and Mobility (BEAM) model. BEAM is an open-source, agent-based regional transportation model that enables realistic simulation of travel behavior, mode choice, fuel consumption, and system performance, and associated community-level impacts under different “what-if” scenarios.  

By leveraging BEAM’s scalable, modular architecture, the project will address key limitations of conventional four-step and activity-based transportation models, providing a robust framework for testing strategies such as emerging technologies, infrastructure enhancements, and new mobility services before deployment. The pipeline will be developed and extended to assess additional impacts (via coupling to additional models) and therefore to serve as a decision-support tool for engineers, planners, and service providers, allowing them to evaluate performance outcomes and trade-offs across multiple metrics relevant to both economic productivity and community outcomes. Model calibration and validation of the Austin BEAM Core pipelines will utilize highly resolved local datasets on traffic flows, speeds, and network performance. These data will enable precise representation of real-world operating conditions in the Austin region and ensure the model’s reliability for planning and investment analysis.  

Scenario development will be coordinated with implementation partners regional stakeholders identified through a stakeholder mapping exercise. These scenarios will reflect practical policy and technology options under active consideration in Texas, ensuring alignment with state and regional priorities. The resulting pipeline will be structured for extensibility, allowing future integration with additional datasets and modeling components for use in other applications. Project outcomes will be shared broadly through technical reports, workshops, and data portals to facilitate adoption by other agencies, research institutions, and industry partners.  

Ultimately, this project supports goals of enhancing efficiency, safety, and reliability, while strengthening economic competitiveness and enabling informed, data-driven investment decisions. By combining open-source modeling innovation with public–private collaboration, the project will provide a replicable framework for modern, performance-based transportation system management.  

Moreover, the pipeline embraces and deploys advanced and transformative research: using an open-source, agent-based framework (BEAM) exceeds conventional planning methods. The stakeholder-co-development model (with public and industry partners) ensures that this research is not only theoretically innovative but also rooted in real-world deployment potential. This initiative empowers decision-makers to implement policies that enhance safety, the economy, and with various co-benefits to communities.  ]]></description>
      <pubDate>Tue, 03 Mar 2026 16:42:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676009</guid>
    </item>
    <item>
      <title>A Fully Neural Network-Based Travel Demand and Scheduling Model, Covering Activities, Destinations, and Modes of Transportation</title>
      <link>https://trid.trb.org/View/2635596</link>
      <description><![CDATA[Recent advancements in machine learning offer promising alternatives to traditional theory-driven approaches in activity-based modeling of travel demand and scheduling. While some scheduling models have integrated combinations of activity, destination, or mode choice, none, to the best of the authors’ knowledge, have integrated all three components within a unified, fully neural network-based framework. To address this gap, this paper presents Skyline-NN, a novel, fully neural network-based model designed to simulate full-day travel and activity schedules, encompassing activities, destinations, and transportation modes. At each time step, the model system operates through three sequential sub-models, Trip Generation, Trip Distribution, and Mode Choice, using a utility-maximizing microsimulation approach. A key component is the scalable Zone Block Module, which enables evaluation of a vast number of destinations through shared parameters. This is demonstrated in the Stockholm case study, where the model handles 1375 available destinations. The model is trained on Stockholm survey data and evaluated through simulated daily schedules against a held-out test set. Its performance is benchmarked against traditional multinomial logit (MNL) baselines, showing clear improvements in predictive accuracy. The analysis further examines sequential error propagation to assess the stability of downstream predictions, includes an ablation study on mandatory destination travel time and cost features, and tests transferability using data from Helsinki. Results demonstrate the model’s effectiveness in simulating travel demand and activity scheduling, laying a foundation for fully neural network-based approaches that integrate timing, activity purposes, destinations, and modes of transportation.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635596</guid>
    </item>
    <item>
      <title>Modelling potential demand for cycling in a small city and surrounding region</title>
      <link>https://trid.trb.org/View/2643963</link>
      <description><![CDATA[Increased cycling for non-leisure purposes is often proposed as a solution to the negative externalities of car-dependent transport systems, offering a cheap, fast, and flexible alternative. Public discourse frequently assumes mass cycling uptake will displace car trips over short distances. However, transport systems are complex, and such direct car-to-bicycle substitution cannot be assumed. This study examines the potential for increased cycling in Cork City and its region using a regional strategic transport model across scenarios incorporating improved infrastructure and more favourable cycling perceptions. Analysis uses the National Transport Authority’s Regional Modelling System for the Southwest (Cork), a four-stage model calibrated with Irish census and survey data and containing the country’s most detailed coded cycling network. Mode choice constants are adjusted and benchmarked to walking, and cycling speeds increased to reflect network upgrades. Results suggest significant potential for cycling uptake, but most trips diverted to cycling come from other sustainable modes (walking and public transport). Even optimistic scenarios yield only modest emissions reductions, highlighting the complex dynamics of modal shift and the need for complementary demand management measures to reduce car use effectively.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:32:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643963</guid>
    </item>
    <item>
      <title>Hierarchical bus transit network design in coordination with an existing metro system</title>
      <link>https://trid.trb.org/View/2592506</link>
      <description><![CDATA[The construction of a well-coordinated metro-bus bimodal system may benefit not only passengers performing bimodal trips but also passengers using each individual transit mode. With this aim, this paper focuses on jointly designing a bus network and determining the frequency of bus lines to ensure coordinated operation with an existing metro system. In particular, rather than implementing a single regular line type, a hierarchical bus network structure consisting of different types of bus lines is proposed with the objective of reaching better intermodal coordination. Each line type is characterized by a specific stopping pattern, commercial speed level, and terminal stations. A novel inter-stop distance-based model is proposed to determine the number and the itinerary of the different types of lines and the appropriate bus frequencies so that the total passenger travel time in the bimodal network is minimized under capacity limitations. Meanwhile, a multinomial logit model is incorporated to explicitly capture the endogenous multimodal passenger assignment regarding service level and ticket price. To efficiently solve the hierarchical network design problem, the concept of an improved hierarchical virtual road network is defined. Based on it, a bi-level heuristic decomposition method that breaks down the integrated problem into two simpler subproblems and then solves them iteratively is presented. In addition, the passenger demand information is used internally by the solving procedure in several specialized operators to accelerate the convergence of the algorithm and improve the quality of the solutions. The results of a small-scale case and a real-world network design instance considering three types of lines show that by making a trade-off between lines with different running speeds resulting from different average inter-stop distances, the hierarchical bus network can incentivize commuters into route choices that improve the overall system performance of the integrated metro-bus bimodal network.]]></description>
      <pubDate>Fri, 14 Nov 2025 14:37:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592506</guid>
    </item>
    <item>
      <title>Planning UAM network under uncertain travelers’ preferences: A sequential two-layer stochastic optimization approach</title>
      <link>https://trid.trb.org/View/2592011</link>
      <description><![CDATA[Urban Air Mobility (UAM) holds significant promise for enhancing travel efficiency and improving regional accessibility. However, policymakers face a fundamental challenge: infrastructure planning decisions must often be made before demand is known. This study develops a single-stage stochastic optimization framework with sequential decision layers that mirrors real-world planning constraints. It allows agencies to determine vertiport locations and trip allocations before individual mode choices are realized, incorporating behavioral uncertainty via discrete choice modeling and Monte Carlo simulation. To ensure computational tractability at realistic scales, an improved greedy algorithm (GRD-U) is introduced and benchmarked against established heuristics. Experiments on synthetic instances show that cost-saving potential is greatest in larger regions with low road connectivity, as well as unicentric or dispersed demand patterns. A real-world case study in the Munich Metropolitan Area confirms the framework’s applicability, demonstrating notable improvements in generalized travel cost savings, demand coverage, and accessibility compared to existing siting strategies. A sensitivity analysis highlights how UAM performance responds to changes in operational parameters, such as cruise speed, pricing strategies, and vertiport quantity. The framework offers a transparent and behaviorally grounded tool for early-stage UAM planning. It enables public agencies to anticipate demand patterns under uncertainty, weigh trade-offs between investment scale and system performance, and align infrastructure planning with equity and efficiency goals. These contributions provide practical decision support for cities navigating the complexities of UAM deployment]]></description>
      <pubDate>Thu, 13 Nov 2025 16:59:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592011</guid>
    </item>
    <item>
      <title>Introduction vs. price change of road toll – a panel data analysis of revealed preferences</title>
      <link>https://trid.trb.org/View/2562414</link>
      <description><![CDATA[Cordon-based congestion charging systems effectively reduce traffic, with initial implementations often achieving 15–20 % reductions in vehicle volumes, as observed in Singapore, London, and Stockholm. However, subsequent toll increases typically produce much smaller elasticities, a phenomenon known as "Large Elasticity at Introduction" (LEI). This suggests that introducing tolls on previously free roads triggers stronger behavioral responses than adjusting toll rates on already tolled roads.This study explores LEI using mode-choice data from a panel of 2814 commuters in the Stavanger urban area, collected before and after substantial changes to the toll-cordon system in October 2018. Employing random utility models, the authors test linear and log-transformed cost specifications to investigate the roles of diminishing sensitivity and the zero-price effect.The authors' findings indicate that diminishing sensitivity to cost, captured by a log-transformed specification, is more critical to explaining LEI than a strict zero-price discontinuity. Real-world data confirm strong responses to new tolls at city-center cordons (16–18 % traffic reductions) but weaker responses at previously tolled municipal borders (4 %). These results emphasize that LEI is largely driven by non-linear cost sensitivity, suggesting that introducing low tolls in areas with robust modal alternatives can achieve substantial congestion reductions without requiring steep price hikes.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562414</guid>
    </item>
    <item>
      <title>Optimal matching for ridesharing systems with endogenous and flexible user participation</title>
      <link>https://trid.trb.org/View/2590621</link>
      <description><![CDATA[The performance of ridesharing systems is intricately entwined with user participation. To characterize such interplay, the authors adopt a repeated multi-player, non-cooperative game approach to model a ridesharing platform and its users’ decision-making. Users reveal to the platform their participation preferences over being only riders, only drivers, flexible users, and opt-out based on the expected utilities of each mode. The platform optimally matches users with different itineraries and participation preferences to maximize social welfare. The authors analytically establish the existence and uniqueness of equilibria and design an iterative algorithm for the solution, for which convergence is guaranteed under mild conditions. A case study is conducted with real travel demand data in Chicago. The results highlight the effect of users’ flexibility regarding mode preferences on system performance (i.e., the average utility of users and the percentage of successful matches). A sensitivity analysis on the level of subsidy and the distribution of utility between matched riders and drivers shows that uneven distributions of utility may lead to a higher percentage of successful matches. Additional insights are provided on the effect of a user’s origin and destination locations on their role choice and likelihood to be matched.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590621</guid>
    </item>
    <item>
      <title>Potential for Commuting Demand Management: Analysis of Day-to-Day Modal Variability Based on a 1-Year GPS-Tracking Dataset</title>
      <link>https://trid.trb.org/View/2603857</link>
      <description><![CDATA[Commuting is a major contributor to CO2 emissions from passenger transportation. This study examines the potential of commuting demand management (CDM) by analyzing within-person variability in commuting mode choices. We utilized GPS tracking data to monitor the commuting trips of 129 participants in Germany from September 2022 to August 2023. On average, each participant recorded 10?months of data with 106 trips between their “main home” and “main work” location. Our findings indicated that participants typically use three different main modes for their commuting trips. The Herfindahl–Hirschmann index and the difference between the primary and alternative modes revealed differences in the modal stability among commuters, with some showing high variability and others low. Factors such as gender, possession of a driver’s license, and economic status appeared to influence this variability. We further categorized individuals into commuter modal types (active, public, private motorized) per month using k-means clustering. After 5?months, 71.1% of commuters remained in their original modal type cluster (95% CI: 63.6% to 79.4%). However, by the 10-month mark, this figure had decreased to 58.9% (95% CI: 48.8% to 71.0%). This observed variability in commuting mode choices suggests opportunities for CDM. By designing policies that promote the use of sustainable modes already adopted by commuters, CDM could effectively increase the modal share of these environmentally friendly options.]]></description>
      <pubDate>Fri, 26 Sep 2025 09:07:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603857</guid>
    </item>
    <item>
      <title>A Cost-Based Assignment of Demand-Responsive Transport: A Comparative Study with Public Transportation Alternatives</title>
      <link>https://trid.trb.org/View/2571288</link>
      <description><![CDATA[Demand-responsive transport (DRT), a flexible and dynamic mode, is becoming popular in urban cities but often competes with traditional public transport (PT). This paper explores the integration of DRT and PT, introducing a decision-making strategy that evaluates DRT and PT options based on travel time and fare. By prioritizing routes where DRT offers the most value and relegating less cost-efficient journeys to PT, this strategy aims to optimize the utility of both modes. Implemented and tested using the MATSim simulator in Inner Melbourne, Australia, the findings indicate a significant improvement in system efficiency: 15-18% of requests were economically rejected, reducing overall travel costs by approximately 9-16%, with total fare savings estimated between 14-31%. This study demonstrates the potential trade-offs between system travel time and fare, substantiating the model’s effectiveness in enhancing urban transport systems.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571288</guid>
    </item>
    <item>
      <title>Importance – performance analysis in assessing LoS (Level of Service): A study on the ferry service of Kolkata-Howrah twin city</title>
      <link>https://trid.trb.org/View/2564423</link>
      <description><![CDATA[The ferry service connecting Kolkata and Howrah, a historically significant mass transit system, possesses considerable potential to alleviate congestion and reduce travel time for passengers. Nevertheless, its reach is still limited when compared to road-based transportation, which underscores a troubling disparity. Ferries are frequently utilized not as a preferred travel option but rather out of necessity due to the presence of river barriers. By employing the Importance-Performance Analysis method, an assessment of passenger demand and service functionality has been conducted. The findings indicate significant shortcomings in both the terminals and the vessels. To revitalize the system, it is crucial to tackle these issues at the grassroots level. Focusing on minor enhancements prior to executing large-scale modifications could prove particularly beneficial in rejuvenating this vital mode of transport.]]></description>
      <pubDate>Tue, 15 Jul 2025 09:47:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564423</guid>
    </item>
    <item>
      <title>Can MaaS encourage travel behavior change? The role of cognitive, motivational factors in sustainable and pro-environmental choices.</title>
      <link>https://trid.trb.org/View/2493092</link>
      <description><![CDATA[Although several pilot projects and trials have been conducted to assess the impacts of implementing Mobility as a Service (MaaS), researchers still have doubts about whether such a system can actually influence the travel preferences of car users. While much emphasis has been placed on exploring the organizational and structural challenges of MaaS, fewer efforts have been made to investigate the “human” factors that could play a role in the cognitive process leading individuals to switch from using cars to adopting MaaS. Therefore, in this paper, the authors aim to understand which features should be included in MaaS to effectively encourage car users to modify their travel behavior. Based on an analysis of past literature, which highlighted the influence of behavioral and cognitive variables (such as habits, attitudes, and pro-environmental factors) on people's decisions to adopt sustainable transportation modes, the authors propose some actions that could add value to MaaS. Specifically, the authors argue that MaaS should not only focus on the digitalization of transportation services but also on the effective integration of public and private, shared, and individual modes of transport, considering factors such as fares and the availability of services. Additionally, due to the significant role played by cognitive factors in individuals’ travel behavior choices, the authors suggest that the implementation of MaaS should be accompanied by Travel Demand Management strategies, such as feedback programs, incentives, rewards, and so on, to encourage car users to intentionally choose sustainable and integrated transportation modes. In conclusion, the article suggests that MaaS has the potential to reduce private car usage, but more effort should be devoted to developing soft measures that help individuals perceive the added value that MaaS can offer to their overall travel experience.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493092</guid>
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