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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>Exploring taste heterogeneity and substitution patterns in dockless bike-sharing parking preferences</title>
      <link>https://trid.trb.org/View/2639393</link>
      <description><![CDATA[Dockless bike-sharing (DBS) is an important sustainable urban transportation mode in many cities but faces challenges with disorderly parking management. This study aims to explore the presence of taste heterogeneity and substitution patterns in DBS users’ parking preferences and to determine how interpersonal variations, alternative-specific attributes, and socio-demographic characteristics affect parking choices. Based on stated-preference data collected in China, a mixed nested logit (Mixed NL) model is employed to account for both inter-alternative correlation and random taste heterogeneity. The results indicate that reducing the distance to parking and increasing monetary fines are more effective in discouraging disorderly parking than offering incentives for orderly parking or adjusting parking fees. Social influence also plays a critical role, as users are more likely to park disorderly when they observe others doing so. Meanwhile, the research also reveals that users are willing to pay an average of 0.8 CNY to reduce the distance to parking by 100 m, and are willing to accept on average an additional 58 m of the distance to parking in exchange for 10 min of free riding time. These insights into DBS users’ parking behaviour enhance the understanding of the effectiveness of possible policy interventions and offer a valuable reference for developing future management strategies.]]></description>
      <pubDate>Thu, 12 Mar 2026 14:02:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639393</guid>
    </item>
    <item>
      <title>Manage the Curb: Optimization of Time-Varying Parking Zones in Micromobility Systems</title>
      <link>https://trid.trb.org/View/2616178</link>
      <description><![CDATA[Station based and free floating are the two established parking regimes of micromobility systems. The former restricts pickup and drop-off to designated stations, which is less convenient for users but reduces the nuisance of sloppily parked rental bikes and scooters. Free-floating micromobility allows users to use any public parking space within the operating area, which increases user flexibility but creates organizational overhead to deal with improperly parked vehicles. Time-varying parking zones, realized via curbside management software (CMS) and geofencing technology, promise a reasonable compromise in the convenience-clutter trade-off. Based on spatiotemporal information, the city administration can either permanently (e.g., pedestrian zones) or temporarily (e.g., weekly farmers’ market) block certain urban areas in their CMS. The micromobility providers, also having access to the CMS, must ensure that vehicles are not returned to undesignated areas with digital fences during the announced times. This paper introduces an optimization approach for micromobility providers to plan time-varying parking zones, given the dynamic municipal parking limitations. Opening and closing urban areas for parking not only requires a digital reaction (i.e., (un)blocking via geofencing) but also produces costs (e.g., removing the remaining scooters of previous periods from the market square). Hence, our optimization task aims to minimize the total costs associated with dynamic parking zones, whereas representative user trips are guaranteed travel within a given time budget. Based on this setting, we show that most urban stakeholders can profit from time-varying parking zones (compared with a static operating area). Our case study based on Berlin-Mitte shows that dynamic parking zones reduce urban space usage at decreasing service costs and only slight user concessions regarding their convenience.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616178</guid>
    </item>
    <item>
      <title>Unlocking Green Mobility: How Government Intervention Sensitivity and Psychology Drive Dockless Bike-Sharing in Da Nang, Vietnam</title>
      <link>https://trid.trb.org/View/2655734</link>
      <description><![CDATA[Sustainable transportation is rapidly gaining attention worldwide, yet research on the psychological and policy-related factors influencing the adoption of dockless bike-sharing (DBS) services, particularly in emerging markets like Vietnam, remains limited. This study addresses this gap by investigating the psychological factors influencing Vietnamese commuters’ intention to use DBS services in Danang City, given of the growing importance of sustainable transportation. By integrating the theory of planned behavior (TPB), the technology acceptance model (TAM), the norm activation model (NAM), and government intervention sensitivity (GIS), the study identifies the key drivers of DBS usage. A sample of 874 respondents was analyzed using partial least squares structural equation modeling (PLS-SEM). The findings reveal that GIS, attitudes, personal norms, and perceived behavioral control significantly affect the intention to use DBS, with GIS playing the most crucial role. The model explains 63.5% of the variance in intention to use DBS. Furthermore, perceived ease of use and perceived usefulness indirectly affect intention through attitude, while environmental awareness and responsibility attribution strengthen personal norms. The research offers practical recommendations for improving DBS infrastructure, optimizing mobile applications, and developing green mobility policies.]]></description>
      <pubDate>Thu, 22 Jan 2026 09:11:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655734</guid>
    </item>
    <item>
      <title>How Parking Regulations Enforced with Virtual Geofences Influence Bike-Sharing Demand: A Case Study in China</title>
      <link>https://trid.trb.org/View/2646171</link>
      <description><![CDATA[Dockless bike-sharing has emerged in recent years as a sustainable mode of transportation, offering convenience and environmental benefits. However, its rapid growth has given rise to challenges such as indiscriminate parking. To tackle this issue, mandatory parking zones enforced through the use of virtual geofences have been widely adopted by governments and bike-sharing companies. Despite their widespread implementation, the impact of such regulations on bike-sharing demand remains largely unexplored. This case study examines whether the introduction of parking regulations influences users' demand for bike-sharing in a medium-sized city in China. Furthermore, the relationships between demand and various potential influencing factors were analyzed using four district-specific negative binomial regression models. To verify the robustness of these findings and assess overall policy effects, a pooled model incorporating all grid cells across the city was also constructed. The results indicate that virtual geofence density is a significant factor in areas where demand changes were observed. Downtown areas are less affected because of their higher virtual geofence density, whereas suburban areas experience a greater negative impact. The findings suggest that increasing the virtual geofence density in suburban areas could mitigate the negative effects of parking regulations. Additionally, implementing a one-size-fits-all approach by transitioning from a dockless mode to a virtual-geofenced mode across all areas may not be optimal. Instead, a hybrid approach, with both parking schemes coexisting in the city, is recommended.]]></description>
      <pubDate>Tue, 30 Dec 2025 08:56:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646171</guid>
    </item>
    <item>
      <title>Real-time reposition management of bike-sharing systems: a synchronous predict-then-optimize approach</title>
      <link>https://trid.trb.org/View/2606316</link>
      <description><![CDATA[As a convenient and low-carbon transport service to address the “last mile” problem, bike-sharing systems (BSSs) have been rapidly developed worldwide. However, the salient spatiotemporal imbalance between demand and supply has led to the bike-sharing repositioning problem (BSRP), aiming to reposition bikes from surplus stations to insufficient stations efficiently in BSS. This paper proposes a synchronous prediction then instantaneous optimization (SPtIO) approach, which consists of a multi-task multi-gate mixture of topology adaptive graph convolutional networks (3M−TAGCN) station relocation demand prediction model and a transformer policy-based reinforcement learning (TPRL) bike-sharing repositioning model. The 3M−TAGCN model makes synchronous and dynamic predictions for the real-time relocation demands of all stations by learning features and relationships from historical inflow and outflow spatiotemporal data. Leveraging the advantage of “offline training + online optimizing”, the TPRL model instantaneously figures out the dynamic BSRP based on predicted relocation demands. The policy of the TPRL model consists of a transformer network and a mask method, and the training process incorporates the policy gradient algorithm. Experiments on the New York Citi Bike dataset demonstrate that the 3M−TAGCN prediction model outperforms other baseline models in various scenarios. The TPRL bike-sharing repositioning model effectively determines near-optimal repositioning schemes. Evident results have shown significant improvements in the proposed SPtIO approach over the service quality and repositioning efficiency of BSSs.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606316</guid>
    </item>
    <item>
      <title>Modelling the influence of urban morphology on bikeshare station use: a clustering approach</title>
      <link>https://trid.trb.org/View/2603778</link>
      <description><![CDATA[Docked bikeshare schemes have proliferated across UK cities since the first scheme was introduced in 2010. These schemes have been widely adopted for their contributions to decarbonising transport, improving health, and enhancing connectivity through first and last-mile trips. As bikeshare expands to new cities, planners and operators increasingly require a localised understanding of the factors influencing bikeshare use. Urban morphology in UK cities varies widely, however, encompassing differences in street layouts, building design, accessibility, and land use. Meanwhile, industry bikeshare planning guidelines are often broad, without distinguishing between city size and character. These variations pose challenges for bikeshare scheme planning in different settings, emphasising the need for robust, data-driven models that are sensitive to urban context. This paper employs cluster analysis to classify urban areas within several UK cities, with the aim to understand the combined contextual urban factors that influence bikeshare use. This approach, rarely applied in micromobility research, offers a nuanced and unique methodological contribution. The cluster analysis distinguishes between types of residential neighbourhoods, which is a component less commonly incorporated within existing studies. With the data obtained, statistical analysis offers granular insights into the relationship between the built environment and docking station use. It is highlighted that denser residential neighbourhoods with favourable accessibility have consistent associations with trip generation, while accessible suburban neighbourhoods are more varied. The findings have implications for both initial planning and scheme expansion, relevant to station location optimisation, forecasting future demand, fleet size adjustment and integration with existing public transport networks.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603778</guid>
    </item>
    <item>
      <title>Bicycle Parking in Residential Developments: A Dialogue Between Planners and Developers</title>
      <link>https://trid.trb.org/View/2616199</link>
      <description><![CDATA[There is limited academic research on the topic of bicycle parking. This paper seeks to understand how cities can increase the supply of bicycle parking in residential developments. The researcher conducted a total of 21 interviews - twelve with developers and nine with planners - for this project. Based upon the findings, the study offers the following policy recommendations for cities to increase the supply of bicycle parking: (1) Bundle incentives such as the reduction in automobile parking for more bicycle parking with big ticket rewards such as density bonuses and reductions in Transportation Impact Fees; (2) Pilot demonstration programs that would allow developers to test and compare a variety of bicycle racks; (3) Create bicycle parking ordinances that allow for flexibility; and (4) Start/continue programs such as Safe Routes to School and open streets events to educate the public about alternative modes of transportation. Cities will need more bicycle parking as they look to expand their on-street bicycle infrastructure. These recommendations can help cities achieve that goal.]]></description>
      <pubDate>Sat, 20 Dec 2025 17:26:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616199</guid>
    </item>
    <item>
      <title>National Transportation Atlas Database (NTAD): Bikeshare 2017-Present [dataset]</title>
      <link>https://trid.trb.org/View/2620565</link>
      <description><![CDATA[The Bikeshare 2017-Present dataset is from the Bureau of Transportation Statistics (BTS), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics’ (BTS's) National Transportation Atlas Database (NTAD). The bikeshare layer shows the location of all bikeshare docking stations, along with their address if known and the city and state it is located in. Prior to April 30, 2025, this bikeshare layer reflected the bikeshare stations available for the latest Intermodal Passenger Connectivity Database (IPCD) data collection along with intermodal passenger connectivity information. To provide this timelier snapshot of bikeshare stations, the Bureau of Transportation Statistics is no longer including connectivity information. To obtain the previously provided IPCD Bikeshare layer on NTAD for the latest only bikeshare year that included connectivity information, query the current IPCD layer on NTAD (https://doi.org/10.21949/1522239) using the query where the “BIKE_SHARE” field is equal to 1, signifying that bikeshare service is provided at that location.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:22:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620565</guid>
    </item>
    <item>
      <title>Federal Highway Administration University Course on Bicycle and Pedestrian Transportation Lesson 17: Bicycle Parking and Storage</title>
      <link>https://trid.trb.org/View/2592839</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) University Course on Bicycle and Pedestrian Transportation contains modular resource material that is intended for use in university courses on bicycle and pedestrian transportation. Bicycle parking is an important supporting element in bicycle programs. Quite simply, bicyclists need a safe and convenient place to park or store their bicycles along or at the end of most trips. This lesson contains the following information on developing an effective bicycle parking program: basic bicycle parking strategies; bicycle rack designs, specifications, and costs; and bicycle parking ordinances.]]></description>
      <pubDate>Sun, 26 Oct 2025 17:29:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592839</guid>
    </item>
    <item>
      <title>A hybrid e-bike sharing system design problem considering multiple types of facilities</title>
      <link>https://trid.trb.org/View/2587260</link>
      <description><![CDATA[Shared electric bikes (e-bikes) have become a rapidly growing mode of transportation worldwide, with electric bike-sharing systems (EBSSs) successfully implemented in numerous cities. The mainstream EBSSs can generally be categorized into two types: station-based and free-floating (or dockless). Each type has its respective advantages and disadvantages. For example, free-floating systems have a lower total construction and maintenance cost, but some users return e-bikes at improper locations without considering social impacts, such as blocking vehicle and pedestrian movements, and the induced safety issues. A hybrid e-bike sharing system (HEBSS) that combines elements from both systems has the potential to exploit the advantages of both and overcome their drawbacks, leading to an improvement in system performance. However, few existing studies have proposed a methodology to design such a system to demonstrate its effectiveness and address the inconsiderate e-bike return behavior.In this paper, the authors formulate the design problem of an HEBSS as a bi-level optimization problem. The upper-level problem is to determine the locations and capacities of various facilities, including charging stations and geofencing areas, aiming to maximize social welfare under a budget constraint. The lower-level problem is an e-bike sharing network equilibrium problem with elastic demand considering the inconsiderate drop-off behavior of users, waiting time costs, roaming behavior during rental and return processes, and parking rewards and fines. The upper-level problem is solved by the authors' proposed hybrid solution method, which is based on genetic algorithm coupled with their proposed capacity-setting heuristic. The lower-level problem is transformed into a fixed demand equivalent problem and solved by the self-regulated averaging method. The authors present numerical results to demonstrate the properties of the problem, identify the key factors that affect the design, illustrate the performance of the proposed hybrid solution algorithm, and provide design insights to the system operator.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587260</guid>
    </item>
    <item>
      <title>Preferred streets: assessing the impact of the street environment on cycling behaviors using the geographically weighted regression</title>
      <link>https://trid.trb.org/View/2561635</link>
      <description><![CDATA[Cycling transport systems are an important way to reduce the city’s carbon emissions. Street renovation and renewal policies aim to encourage cycling transport by improving the cycling environment. However, most existing research studies the relationship between the street environment and cycling behavior from a global perspective, ignoring geospatial heterogeneity. Also, methods evaluating the cycling environment based on the frequency of cycling ignore the difference between spontaneous and necessary trips, hiding the problems that exist in streets with a high frequency of cycling. Therefore, the preferred streets index was proposed to evaluate the street cycling environment based on the difference between the cycling trajectory and the shortest path. Geographically weighted regression was used to explore the local effects of street environments on cycling behavior. The experimental results on Xiamen Island show that the type of street and the density of bicycle parking spots have a positive impact on cycling, while the effect of the availability of streetlights, availability of traffic lights, and POI density on cycling was determined by the geographic context of the street. These results provide concrete guidance for improving the cycling environment and enrich the evaluation methods for the cycling environment.]]></description>
      <pubDate>Wed, 17 Sep 2025 09:01:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561635</guid>
    </item>
    <item>
      <title>Incorporating equity in the vehicle rebalancing operations of dockless micromobility services</title>
      <link>https://trid.trb.org/View/2548070</link>
      <description><![CDATA[Dockless micromobility services, including shared bicycles and scooters, are emerging as sustainable travel alternatives in many cities. The optimal operation of these services, however, often depends on rebalancing operations that redistribute micromobility vehicles to service area locations with less than desired vehicle levels. Existing rebalancing models typically prioritize operational efficiency or business objectives, such as relocating vehicles to maximize served demand or profits. This study contributes a rebalancing model that incorporates the goal of improving equity-in-access to dockless micromobility through rebalancing operations. Specifically, a two-step approach is proposed to optimize the rebalancing operations of dockless micromobility services according to efficiency and equity objectives. In the first step, an optimization model is used to find micromobility vehicle distributions that maximize system-level efficiency and equity performance indicators across a specified time horizon. In the second step, a multi-objective pick-up and delivery problem is used to develop vehicle relocation plans aimed at achieving the optimal distributions determined in the first step. Numerical examples are presented to illustrate the application of the proposed methods. As part of the numerical tests, machine learning-based models trained using real-world data were shown to accurately predict equity-based performance indicators for a dockless e-scooter service in Puerto Rico.]]></description>
      <pubDate>Tue, 08 Jul 2025 09:57:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548070</guid>
    </item>
    <item>
      <title>Bicycle parking allocation and its equity implications: The case of Bogota, Colombia</title>
      <link>https://trid.trb.org/View/2548089</link>
      <description><![CDATA[Bicycles are a sustainable alternative for urban mobility; however, their usage depends mainly on safety, convenience, and infrastructure availability, which have been widely studied. However, most studies have overlooked the importance of bicycle parking facilities in the network-planning process. In this work the authors aim to draw attention to the importance of including bicycle parking (BP) facilities in a well-thought-out bicycle infrastructure planning process highlighting the detrimental effects of not doing so for transportation equity, using Bogotá, Colombia, as a case study, where bicycle trips have multiplied in the last few years, reaching a 6.6 % modal share in 2019. To this end, the authors present a geospatial analysis and machine learning approach to assess the network coverage of bicycle parking spots. Additionally, the authors compared the city's bicycle trip patterns and applied a survey to know the perception of users (n = 397). The results show that the current distribution of bicycle parking in the city does not favour equity, given that it is not in line with the origin and destination of bicycle trips. This could widen socio-territorial inequity by affecting accessibility to bicycle use for daily commutes. To the best of the authors' knowledge, this study presents the first assessment of the impact of parking distribution on the planning of bicycle infrastructure in the Global South.]]></description>
      <pubDate>Tue, 08 Jul 2025 09:57:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548089</guid>
    </item>
    <item>
      <title>Dynamic rebalancing strategies for dockless bike-sharing systems</title>
      <link>https://trid.trb.org/View/2543921</link>
      <description><![CDATA[Bike-sharing systems have developed rapidly with the influence of the sharing economy, and many operational challenges have arisen. The bike rebalancing problem is one of the main challenges in bike-sharing systems. In this paper, the authors propose a framework to address the dynamic bike rebalancing problem in dockless bike-sharing systems by using trucks to relocate bikes to meet the time-varying demand at each location. The authors decompose the problem into two processes: dynamic clustering and bike relocation. For dynamic clustering, the authors propose an optimisation model to select cluster centroids and decide the number and coverage of clusters to maximise operational profit based on trip revenues and expected traversal costs between clusters. An Adaptive Large Neighbourhood Search (ALNS) algorithm is developed to solve this problem. Clusters with too many bikes would lead to bike piles and cause urban blight, while clusters with too few bikes may result in user dissatisfaction. To prevent such issues, in the bike relocation process, the authors construct vehicle routes with pickup and delivery for bike relocation between clusters. The authors test the framework using real data from Louisville, USA. The authors show that the proposed ALNS can efficiently solve large real-life instances and obtain high-quality solutions. Numerical experiments also indicate that the dynamic clustering model significantly increases average daily profit compared to static clustering benchmarks. The authors provide operators with several insights into the impact of clustering and relocation in bike-sharing systems.]]></description>
      <pubDate>Fri, 30 May 2025 15:54:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543921</guid>
    </item>
    <item>
      <title>Effectiveness of traffic signs to prevent fly parking</title>
      <link>https://trid.trb.org/View/2522040</link>
      <description><![CDATA[As bicycle use increases, so does the need for formal parking spaces to safely store them while performing other activities at a destination. In the Netherlands, several municipalities have created indoor and outdoor formal parking spaces, which remain underutilized. Instead, many cyclists choose to ‘fly park’, i.e. informally lock their bicycle to objects on the street. This can cause dangerous situations or inconvenience, for example by blocking sidewalks. The discrepancy between the use of formal and informal parking spaces may be attributed to a lack of information provided to cyclists about the available formal parking options. This study investigated the effectiveness of different traffic sign designs in encouraging the use of formal parking spaces. The designs were developed within this research with the intention of capturing different communication strategies, namely hazardous, neutral educative and negative educative. A stated preference choice experiment was then performed to allow the comparison of the effectiveness of the different designs, and thus communication strategies. The responses were analysed using discrete choice modelling. According to the results, traffic signs alerting users to the fact that controls are performed (hazardous communication) are the most effective in the fly parking prevention, especially for frequent bicycle users.]]></description>
      <pubDate>Wed, 28 May 2025 12:01:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522040</guid>
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