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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Exploring Latent and Manifest Effects on Micromobility Mode Choice</title>
      <link>https://trid.trb.org/View/2772601</link>
      <description><![CDATA[Shared modes of micromobility play an increasingly important role in urban transportation. Although mode choices on bike-sharing systems (BSS), shared electric scooters (ES) and short-distance public transit (PT) have been examined in recent years, there is a lack of joint analyses, especially considering latent choice influences such as environmental consciousness. To address this gap in knowledge, we estimate a hybrid choice model (HCM) with a nested mixed multinomial logit choice component for the mode choice between BSS, ES, and PT that fits the data with an adjusted rho-squared value of 0.45 and results in plausible parameter estimates. As a result of our joint consideration of BSS, ES, and PT, we find that respondents with a higher environmental consciousness are more likely to use BSS and PT than ES, even though all three modes are often considered as being used by travelers with a high environmental awareness. Additionally, we find that mode choice is nested in sheltered and unsheltered modes with PT in the sheltered nest and BSS and ES in the unsheltered nest. Furthermore, we find that smartphone or bag holders may not increase the utility for BSS and ES and that the remaining ES battery capacity does not appear to have a large effect on ES choice.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772601</guid>
    </item>
    <item>
      <title>How the built environment shapes the spatiotemporal imbalances in dockless bike-sharing usage: A network-based analysis of Shenzhen, China</title>
      <link>https://trid.trb.org/View/2712995</link>
      <description><![CDATA[Dockless Bike Sharing (DBS) has become a dominant bike-sharing model in many cities due to its flexibility. However, this also intensifies operational rebalancing challenges. The built environment can influence DBS usage through multiple channels, potentially leading to usage imbalances. These imbalances, defined as the net change in bicycle inventory at specific locations, are critical to effective system management. Using large-scale DBS trip data, we apply a K-means clustering approach to aggregate daily origins and destinations into virtual stations, and construct a DBS complex network with these stations as nodes. We then analyze the spatiotemporal dynamics and community structure of the DBS network, and employ regression models to examine the effects of built-environment factors on DBS usage imbalance. The results reveal pronounced tidal patterns in DBS usage imbalance during morning and evening peak periods, whereas the community structure of the network remains relatively stable. Residential areas primarily function as origins of DBS trips during the morning peak, increasing the risk of bike shortages at nearby stations, while industrial areas function as destinations, leading to bike accumulation; the opposite pattern emerges during the evening peak. Metro stations serve as key intermodal transfer hubs, with morning peak inflows leading to bike accumulation and evening peak outflows increasing shortage risks. Our analyses suggest that transit-oriented development and jobs-housing separation constitute important structural mechanisms underlying DBS imbalance. These findings provide empirical evidence on how the built environment shapes spatial imbalance in DBS use, thereby supporting more effective operation and planning.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:38:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712995</guid>
    </item>
    <item>
      <title>Dynamic Matching for Teleoperated Car-Sharing Services</title>
      <link>https://trid.trb.org/View/2709157</link>
      <description><![CDATA[Teleoperated vehicles are a promising concept for increasing the attractiveness of car-sharing services. Such vehicles can be remotely steered by an operator to the location of a customer requesting a vehicle on demand. It therefore eliminates both the need for customers to walk to a car-sharing vehicle and the need for providers to relocate vehicles with drivers on-site to meet the temporal and spatial vehicle demand. The key to a successful teleoperated car-sharing service is to avoid longer service delays by effectively utilizing the fleet of vehicles and the limited number of available operators. The corresponding sequential decision process therefore involves a matching problem deciding which vehicle should be steered next by an available operator in order to fulfill which customer request. This decision is challenging, as both future demand and future availability of vehicles are uncertain, because the rental duration and return location of vehicles are unknown. We propose an approximate dynamic programming approach that combines predictions of future vehicle returns and customer requests with an approximation of the opportunity cost of matching decisions. We demonstrate the merits of our approach in comparison with benchmark policies in a comprehensive computational study based on New York demand data. We derive several important insights, among others, that vehicle predictions are especially valuable, and that a ratio of about one operator to six vehicles is sufficient in our setup.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709157</guid>
    </item>
    <item>
      <title>Examining the adoption potential of a new travel chain integrating electric vehicle sharing and rail transit</title>
      <link>https://trid.trb.org/View/2708310</link>
      <description><![CDATA[Electric Vehicle Sharing (EVS) can reduce transport-related emissions, yet its scalability faces operational and cost barriers. Integrating EVS with Rail Transit (EVS + RT) offers a sustainable mobility pathway by enhancing first-/last-mile connections. Prior studies often view EVS as a substitute for traditional modes, overlooking multimodal adoption willingness. This study investigates EVS + RT adoption via a web-based commuter survey, analyzing demographics, travel patterns, and latent preferences. A Mixed Logit model quantifies the roles of income, environmental awareness, commute distance, and station proximity. Findings show that access to transit hubs, charging convenience, and unified payment platforms raise adoption, while cost sensitivity and car dependence hinder it. Results highlight the need for user-centered planning, such as optimizing EVS station placement and dynamic pricing. By integrating practical and psychological factors, the study provides policy insights to scale EVS + RT and support low-carbon urban mobility.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708310</guid>
    </item>
    <item>
      <title>Tackling transport poverty with offers for shared mobility – a qualitative analysis on mobility needs and barriers in the context of a new mobility hub in a deprived neighbourhood</title>
      <link>https://trid.trb.org/View/2742314</link>
      <description><![CDATA[Increasing urbanization and climate change pose significant challenges to cities’ environments and quality of living. Limited mobility options lead to transport poverty that reduces social participation. To address these issues, promoting sustainable mobility solutions like shared mobility services is essential. This study examines the impact of a mobility hub opened in November 2022, offering shared electric (cargo) bicycles, and cars, on reducing transport poverty in a deprived neighborhood in Utrecht, the Netherlands. In May 2023, the authors conducted three focus groups with residents to investigate their mobility needs, barriers to mobility and to using shared mobility. Participants used various mobility modes, favoring cycling and walking for short distances and cars or public transport for longer trips. Key barriers included financial constraints and limited vehicle access. Many participants were unaware of the mobility hub and expressed concerns about costs, flexibility, accessibility, and potential liability. However, they recognized the potential of the shared mobility hub to promote transport equity if their concerns are addressed. Targeted promotion campaigns are crucial to increase awareness and address concerns about shared mobility. Insights from focus groups with local residents provide a valuable model for other cities aiming to implement inclusive and sustainable mobility solutions.]]></description>
      <pubDate>Fri, 28 Aug 2026 14:43:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742314</guid>
    </item>
    <item>
      <title>Community-Controlled Transportation: The Western New York E-Bike Library Network</title>
      <link>https://trid.trb.org/View/2761963</link>
      <description><![CDATA[Shared Mobility Inc. (SMI) has used their mission of community-controlled transportation to partner with community based organizations on the launch and operations of E-Bike Libraries (EBLs) in Western New York (WNY) and beyond since 2021. For almost four years, the WNY EBLs have provided e-bikes at no cost to underserved communities in Buffalo and Niagara Falls. Participants used the e-bikes for commuting, recreational rides, community bike rides, running errands, and accessing essential services. This program significantly increased e-bike accessibility, with 71% of participants being first-time riders and 78% identifying as Black/African American. Challenges such as bike maintenance, battery charging logistics, and the need for suitable storage were addressed through partnerships with community-based organizations and adjustments to program logistics. Those lessons have been applied to additional EBLs in Pacoima, California and Carlisle, Pennsylvania, which have also been launched by SMI with a continued emphasis on the importance of community-based approaches to sustainable transportation. All of these programs demonstrate the potential of e-bikes to provide an affordable, efficient, and fun transportation option, particularly for underserved communities. By leveraging community partnerships and focusing on accessibility and inclusivity, E-Bike Libraries can significantly contribute to sustainable transportation solutions and promote broader participation in the transportation electrification revolution.]]></description>
      <pubDate>Fri, 28 Aug 2026 14:41:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761963</guid>
    </item>
    <item>
      <title>Joint Design of Unidirectional Dedicated Lanes and Multi-Depot Shared Autonomous Vehicle Fleets with Flexible Rebalancing</title>
      <link>https://trid.trb.org/View/2696087</link>
      <description><![CDATA[Early-stage deployment of shared autonomous vehicles (SAVs) is likely to be constrained by still-maturing autonomous driving technology and safety regulations, making it realistic to operate SAVs from a limited number of depots and to confine automated driving to a subset of dedicated links embedded in mixed traffic. We formulate a joint design problem for unidirectional SAV-only travel sections and a multi-depot SAV fleet, explicitly accounting for flexible return-to-any-depot operations. A space–time mixed-integer linear programming model is proposed that integrates budget-constrained lane activation, depot-level fleet sizing, and daytime operations of SAVs and human-driven vehicles (HDVs); Depot-specific fleet sizes are determined endogenously, and end-of-horizon depot imbalances are penalised as a rebalancing cost that approximates off-service-hours repositioning. Optional strong–connectivity constraints are introduced to eliminate absorbing SAV-only subnetworks and to test stricter lane-design rules. Numerical experiments on the Sioux Falls network show that, under tight lane budgets, unidirectional dedicated sections combined with a central multi-depot layout substantially reduce empty running and weighted travel cost while achieving high SAV utilisation. The main efficiency patterns are robust across rebalancing weights, connectivity requirements and HDV ownership-cost scenarios: moderate rebalancing penalties already drive depot imbalances close to zero, and higher HDV costs primarily amplify SAV adoption and benefits in demand-dense central areas.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696087</guid>
    </item>
    <item>
      <title>What factors contribute to the helmet-wearing choice behavior of shared e-bike riders in China</title>
      <link>https://trid.trb.org/View/2696991</link>
      <description><![CDATA[Riding a shared electric bike (shared e-bike) without wearing a helmet is one of the main characteristics of negative riding behavior and has a major negative impact on riders' safety. However, shared e-bike riders persistently resist helmet use despite safety risks, with mechanisms behind this choice unclear. This study aimed to identify the factors influencing the wearing of a helmet, such as individual socioeconomic characteristics, trip characteristics and riders' attitudes toward helmet wearing, in different situations. A random parameter logit regression model was estimated to model the factors influencing riders' engagement in negative behaviors. A questionnaire-based study was conducted to investigate the factors influencing the helmet-wearing behavior of shared e-bike riders in China. The results showed that age, average ride distance, social norm, helmet cleanliness, fear of COVID-19 and riders' previous experience with injury while riding without a helmet are variables that contribute to whether one wears a helmet. The present study provides theoretical evidence supporting the use of helmets when riding shared e-bikes.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696991</guid>
    </item>
    <item>
      <title>A two-stage sequential intention model for autonomous vehicle adoption: Formation and updating of purchase and sharing intentions</title>
      <link>https://trid.trb.org/View/2704141</link>
      <description><![CDATA[Understanding consumer intentions is critical for predicting the market adoption of autonomous vehicles (AVs). However, existing models often fail to capture the sequential nature of intention formation and updating. This study aims to address this gap by developing and validating a two-stage sequential intention model, adapted from the extended unified theory of acceptance and use of technology (UTAUT2) model. The first stage models the formation of initial purchase and sharing intentions based on value-centric constructs, and the second stage models the updating of purchase intention by conceptualizing the final decision as a trade-off between the initial willingness, the appeal of sharing, and the perceived sharing risk. The empirical results, based on a survey of 447 Chinese car owners, provide strong support for the proposed two-stage model. We find that the formation of initial intentions is driven by performance expectancy and price sensitivity, whereas the updating phase reveals that perceived sharing risk exerts a significant negative influence on the final purchase intention. By distinguishing between intention formation and updating, the model offers a nuanced and realistic understanding of AV adoption. This study provides a powerful framework for analyzing the complex value proposition process and offering refined insights for policy and industry strategy.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704141</guid>
    </item>
    <item>
      <title>Survival of shared (battery) swapping-mode electric vehicle industry: does manufacturer’s attitude matter?</title>
      <link>https://trid.trb.org/View/2700632</link>
      <description><![CDATA[Shared (battery) swapping-mode electric vehicles (SSEVs) offer a promising alternative to private vehicle ownership, yet the associated complex stakeholder dynamics would influence their adoption. This study analytically examines how heterogeneous electric vehicle (EV) manufacturers’ strategic attitudes (neutral, positive, negative) shape SSEV market development and proposes mechanisms to align incentives across the ecosystem. By building an analytical model, we first analyze how manufacturers’ attitudes impact SSEV operators’ decisions and profitability. We then develop a cooperative contract to achieve a win–win situation for the profits of EV manufacturers and SSEV operators. Finally, to maximize social welfare, we compare the effects of the price subsidy policy and the service cost subsidy policy. Key insights include: (1) SSEV operators should adjust the service level based on the placement rate of SSEVs. (2) Negative attitudes of manufacturers hinder the growth of the SSEV industry; when consumer sensitivity to battery charging time is sufficiently low, both manufacturer and operator profits improve if the attitude of manufacturers is positive. (3) Signing a cooperative contract can avoid the negative impact of manufacturers’ negative attitudes on consumer adoption of SSEVs and increase the profit of both parties. (4) When implementing a government subsidy to lift social welfare, price subsidies are more effective than service cost subsidies.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700632</guid>
    </item>
    <item>
      <title>Designing rating-based mechanisms for dockless bike-sharing: Behavioral incentives and sustainable mobility</title>
      <link>https://trid.trb.org/View/2700615</link>
      <description><![CDATA[As a widely adopted mode of urban mobility, dockless bike-sharing offers flexible and affordable short-distance transport. However, the unregulated nature of dockless systems often leads to operational difficulties, such as improper parking and equipment misuse by users, which create unverifiable damages for bike-sharing firms and additional operational costs. We analyze the optimal design of a user rating-based incentive mechanism to promote user effort and mitigate these damages. The proposed mechanism features a simple, binary rating structure that depends only on the user’s most recent behavior. Users receive favorable access unless the most severe form of damage is observed, in which case penalties are applied. This structure induces high-effort behavior through the threat of future exclusion while avoiding the complexity of tracking full behavioral histories. We characterize the conditions under which the firm benefits from implementing this mechanism, highlighting the trade-off between the cost of providing incentives and the gains from reduced damages and lower rebalancing or maintenance needs. By linking operational improvements to broader goals of sustainable and equitable mobility, our findings provide theoretical support for simplified rating systems observed in practice and offer policy-relevant insights for behavior-based digital governance in the sharing economy.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700615</guid>
    </item>
    <item>
      <title>Staging at the Curb: Evaluating the impacts of shared automated vehicle fleet operations under curb usage restrictions</title>
      <link>https://trid.trb.org/View/2702894</link>
      <description><![CDATA[Shared automated vehicle (SAV) ridehailing services are now operating in several metropolitan regions in the United States. While providing benefits, SAV services may exacerbate issues related to curb usage and vehicle kilometers traveled (VKT) in urban areas. The objective of this study is to provide guidance to cities by evaluating the impacts of SAVs’ short-term curb usage for staging between serving ride requests, under different curb restrictions and SAV operational strategies. We focus on the following performance metrics: VKT, curb productivity, customer wait time, and customer matching rate. To perform the analysis, we use a high-fidelity commercial simulation tool that models the dynamics of SAV fleet operations. We use high-quality, high-resolution data, including synthetic ridehailing trip data and forecasts of curb availability at each curb front in San Francisco, California. Our baseline scenario assumes a hypothetical fleet of 1700 vehicles serving 68,000 daily trips. We construct scenarios that vary in whether, where, and when SAVs can stage at curbs, as well as whether they strategically reposition to high-demand areas. We also vary the day of the week. According to our simulation results, excluding SAVs from staging at the curb would increase daily VKT by more than 200,000 km, a nearly 60 percent increase compared to scenarios in which SAVs can stage at the curb. In a separate analysis, we find that prohibiting SAV curb staging in residential areas and on curbsides with metered parking would increase empty VKT by 5.4%. We also present key performance metrics that are both temporally and spatially resolved, providing additional policy-relevant information. Simulation modeling is supplemented by expert interviews (n = 14) with practitioners, regulators, and policymakers in curbside management and innovative mobility to gain additional insight into policy considerations related to SAV curb access, staging, and parking.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702894</guid>
    </item>
    <item>
      <title>Assessing resilience of urban bike-sharing systems: Weather and land-use influences on demand dynamics</title>
      <link>https://trid.trb.org/View/2706627</link>
      <description><![CDATA[Bike-sharing systems (BSS) play a key role in the development of eco-friendly cities but are sensitive to external factors, particularly weather conditions. Most existing studies have examined demand-related questions in BSS; however, few focus on station-level demand resilience under extreme weather conditions. To fill this gap, this study proposes an integrated machine-learning framework to quantify station-level demand resilience and explain its spatial variation. Station-specific XGBoost (eXtreme Gradient Boosting) models are developed to simulate hourly trip demand in a weather-controlled manner. By comparing simulated demand under predefined normal and extreme weather scenarios, the demand change rate is constructed to quantify station-level resilience. Furthermore, a second-stage XGBoost-SHAP (SHapley Additive exPlanations) framework is employed to analyze this resilience metric and identify land-use drivers of heterogeneous resilience across stations. Using the BIXI system in Montréal, Canada as a case study, our findings reveal that stations with higher street density and a shorter distance to downtown maintain more stable demand throughout the year. The influence of public transit facilities on demand resilience varies by day of the week: the number of nearby bus lines negatively affects demand resilience on weekdays, but positively affects it on weekends. Overall, this research provides a reproducible analytical framework for mapping bike-sharing systems resilience, with direct implications for land-use-informed system design under climate stress.]]></description>
      <pubDate>Mon, 24 Aug 2026 09:03:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706627</guid>
    </item>
    <item>
      <title>Optimization of cross-regional dispatching for shared autonomous electric vehicles considering charging and carpooling constraints</title>
      <link>https://trid.trb.org/View/2707582</link>
      <description><![CDATA[The rapid development of shared autonomous electric vehicle (SAEV) systems offers significant potential for improving urban mobility efficiency and reducing transport-related emissions. However, most existing studies address vehicle assignment, charging management, and carpooling feasibility separately, without explicitly capturing their interactions under cross-regional and energy-constrained operations. This study proposes a unified cross-regional optimization framework that jointly considers passenger matching, fleet rebalancing, charging constraints, and carpooling feasibility within an integrated decision-making structure. The model explicitly incorporates passenger time windows, charging requirements, and inter-regional vehicle transfer rules, enabling coordinated and energy-aware SAEV operations. A particle swarm optimization-based solution approach is developed and compared with greedy and genetic algorithms to evaluate computational performance and solution quality. This framework has achieved continuous improvements in operational efficiency. Compared to the baseline approach, the proposed solution maintains stable service quality across varying demand intensities while reducing overall system costs and energy consumption. Sensitivity analysis further confirms the model's robustness to variations in passenger waiting tolerance and operational parameters. These findings highlight the importance of integrating spatial deployment, energy constraints, and carpooling decisions in SAEV systems. The proposed framework provides actionable insights for designing scalable, low-carbon, and resource-efficient shared autonomous mobility systems in sustainable cities.]]></description>
      <pubDate>Mon, 24 Aug 2026 09:03:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2707582</guid>
    </item>
    <item>
      <title>Transport modal shift: Do riders prefer e-bike sharing over e-scooter sharing in Canada?</title>
      <link>https://trid.trb.org/View/2739339</link>
      <description><![CDATA[The rapid expansion of electric micromobility sharing services, such as e-scooter and e-bike sharing, is transforming urban mobility. However, there is limited understanding of the behavioral mechanisms that drive modal shifts in electric micromobility sharing (EMS). This study investigates the factors influencing the modal transition from e-scooter sharing (ESS) to e-bike sharing (EBS) in cities where both services are available. Utilizing the theory of planned behavior, the authors employed a structural equation modeling approach to analyze survey data from Canadian users. The findings indicate that trip characteristics, particularly purpose and distance, are the most significant predictors of modal shift, with leisure-oriented and long-distance trips increasing the preference for EBS. Social influence also significantly shapes behavioral intentions, whereas environmental and safety concerns exert less influence. These results underscore the predominance of functional and social drivers in the adoption of electric micromobility. Further, policymakers and service providers are advised to explicitly align infrastructure improvements with these distinct spatial and trip characteristics, such as deploying e-bike mounting racks in suburban areas and implementing targeted e-scooter corrals in dense downtowns, and to leverage peer-based social marketing strategies to encourage modal shifts.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2739339</guid>
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