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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Optimization of On-Campus Students’ Travel Utility</title>
      <link>https://trid.trb.org/View/2562245</link>
      <description><![CDATA[Many universities worldwide have experienced high influx of students’ intake in recent times. However, most university authorities fail to plan for the surge by improving the conditions of existing transport infrastructure or the construction of new facilities for effective on-campus mobility. This study optimised students’ trip utility using the Genetic Algorithm (GA) technique. Objectives of the study were to identify the available mode choices on the Joseph Sarwuan Tarka University, Makurdi (JOSTUM), Nigeria campus, examine students’ socio-demographic characteristics vis-à-vis travel behaviour, develop a students’ trip utility function, and optimize the function using GA technique. An online self-structured questionnaire was administered to 364 randomly selected students drawn from three colleges of the University with a total population of 4,045 students. The instrument examined students’ socio-demographic characteristics and travel behaviour that were used to build the trip utility function, then optimised using GA technique. Results of the study indicated that students’ mode choices were influenced by monthly income, car/bike ownership, accessibility of public transport, safety, and comfort. Statistical performance of the optimised model showed 61.10% coefficient of determination with buses as the optimum mode choice. Therefore, the study recommended improved bus service, increased headway, and the construction and rehabilitation of pedestrian infrastructure for sustainable transport development to cater for the preferences and travel demands of university students.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562245</guid>
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    <item>
      <title>Exploring the Generative Impact of Building Layout and Functional Distribution on Campus Mobility Using Diffusion Models</title>
      <link>https://trid.trb.org/View/2613255</link>
      <description><![CDATA[This study proposes a diffusion model-based framework to analyze the influence of building layouts and functional distributions on shared-bike mobility within the Tsinghua University campus. By integrating building layout data, point-of-interest (POI) distributions, and origin-destination (OD) shared-bike traffic flows, the framework predicts and evaluates campus mobility dynamics. The methodology combines static spatial data with dynamic traffic data, employing a contrastive learning-based encoder to extract latent spatial-functional features, which are then used as conditional inputs to a diffusion model. The diffusion model predicts traffic flow matrices by iteratively refining noisy inputs into high-resolution outputs. Results indicate the model effectively captures mobility patterns, especially in high-density traffic areas, achieving low root mean square error (RMSE) and well-converged loss during training. Visualizations demonstrate the model’s capability to replicate observed traffic dynamics, offering insights into the interaction between campus design and mobility behaviors. Findings contribute to improving campus infrastructure planning and sustainable transportation management.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613255</guid>
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    <item>
      <title>Analyzing Usage Trends of Shared Micromobility Among University Students</title>
      <link>https://trid.trb.org/View/2627775</link>
      <description><![CDATA[Effective management and integration of the increasingly prevalent shared micro-mobility services, particularly in university environments, requires a data-driven understanding of their usage patterns. This study descriptively analyzes the operational patterns of such services on a university campus by examining the temporal distribution of rides and the role of key locations as origins and destinations. Trip data were analyzed using frequency distributions for usage patterns and spatial assessments of trip endpoints relative to significant points of interest, and the findings revealed the distinct temporal usage peaks, as well as the periods with the highest overall volume. Specific origins and destinations were identified as significant for a considerable portion of trips, emphasizing the role of student behavior in shaping micro-mobility usage. These results highlight distinct usage patterns and underscore an urgent need for targeted interventions to ensure sustainable and responsible integration of shared micro-mobility services in the university environments.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627775</guid>
    </item>
    <item>
      <title>Predicting Micromobility Demand in University Campus Environments</title>
      <link>https://trid.trb.org/View/2627534</link>
      <description><![CDATA[Shared Micromobility services such as e-scooters and e-bikes are promising transportation alternatives, but their successful implementation is highly dependent on a comprehensive understanding of the demand patterns of each. Thus, this study applied a hybrid conditional modeling approach that combined a classification and regression model to explore the demand for micromobility and predict ride volumes between zones on a university campus. The results showed that the predictive accuracy of the conditional model was superior to that of the traditional direct prediction model, and an analysis of the feature importance of the traditional model provided deeper insights by identifying the most influential factors in determining ride counts as day of the week, pickup zone, and drop-off zone; holidays and exam schedules had a comparatively minor effect. These results underscore the significance of weekly temporal trends and campus-specific locations in forecasting micromobility demand and offer actionable insights for optimizing campus transportation planning and operations.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627534</guid>
    </item>
    <item>
      <title>Mobility patterns and the potential of innovative mobility services for a university community</title>
      <link>https://trid.trb.org/View/2572519</link>
      <description><![CDATA[In recent years the contemporary landscape of mobility is witnessing significant transformations, in terms of advanced technologies and innovative services. In particular, Mobility as a Service emerged as a novel concept, aiming at providing an integrated and personalized mobility experience that seamlessly combines different transport modes into a unique service. This study seeks to investigate the potential of such innovative services, examining mobility patterns and discerning opinions within a university community. The objectives include: analyzing main origin-destination clusters and identifying primary transportation services adopted, through a survey administered to infer Revealed Preferences; assessing the feasibility of deploying a network of sensors based on MaaS-related data through spatial analysis of survey responses. The case study is the Polytechnic University of Bari in Italy, located in a city where several MaaS-related projects are underway. Findings indicate a robust sample, facilitating accurate geolocalization and providing insights into predominant origin-destination pairs and transportation preferences; this paves the way for identifying distinct mobility clusters and developing a customized MaaS framework accordingly. Furthermore, it expects to demonstrate the feasibility of utilizing MaaS-related data for establishing a network of mobile sensors, offering valuable insights for future transportation planning and management initiatives within the territory.]]></description>
      <pubDate>Mon, 15 Dec 2025 10:32:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572519</guid>
    </item>
    <item>
      <title>Shared Space Safety: A Study of Campus Travel and Mixed Mode Interactions</title>
      <link>https://trid.trb.org/View/2625580</link>
      <description><![CDATA[A study of campus travel and mixed-mode interactions will develop a data-driven baseline of safety conditions on Hilltop Way at San Diego State University—a steep roadway where pedestrians, skateboarders, scooter users, cyclists, and vehicles frequently converge, creating conflicts during class transitions. Video data collected from both ground-level cameras and aerial drone footage will capture user behaviors, travel speeds, yielding patterns, and near-miss events. Analytical techniques such as post-encroachment time (PET) and computer-vision–based variable extraction will be applied to quantify the frequency and severity of potential conflicts. The resulting dataset and safety assessment framework will enable rigorous before–after evaluations of future countermeasures introduced by the university, allowing their effectiveness to be measured in terms of changes in near-crash indicators and interaction dynamics. The project’s outputs—including annotated datasets, analysis tools, and methodological guidelines—will provide a transferable model for studying multimodal safety on shared streets, advancing United States Department of Transportation priorities in safety, innovation, and data-driven decision-making.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:50:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625580</guid>
    </item>
    <item>
      <title>The Parma University Campus as major trip attractor. Traffic microsimulation for modelling vehicle access scenarios</title>
      <link>https://trid.trb.org/View/2571382</link>
      <description><![CDATA[University campuses are multi-modal and major trip attractors, facing strong interactions of several transport modes within a unique system. Although campuses may share common features, they have different transport needs. The development of policies that encourage active mobility and transit service and the improvement of road infrastructures are main strategies to accomplish sustainable transportation goals. While many universities devote efforts to reducing drive-alone commute trips, private vehicles often remain the most affordable and convenient choice for many employees and students. In addition to discourage or apply strict restrictions to vehicular traffic, the optimization of the inbound and outbound flows is a necessary approach to reduce congestion and safety issues on and near campus Micro-simulation models are increasingly popular for examining these complex traffic problems at detail level, emulating traffic behavior in a transport network over time and space to predict a system performance. In this perspective, the article describes the case study of the mitigation of motorized traffic problems of the University Campus of Parma (Italy), which is implementing multiple sustainability strategies towards modal shift to non-motorized systems and optimization of public transport. Specifically, a traffic micro-simulation modelling was implemented to study the vehicle access at the main entrance to the Campus delimited area from the public road network via a multi-lane roundabout. Different scenarios, that do not involve any investment in infrastructure but only interventions in the management of the scheduled educational activities and services, were presented for increasing the users’ safety and level of service. Following a description of the software calibration process and its validation to match the locally observed conditions, some operational solutions based on the re-planning of the lesson timetable were presented to reduce the current congestion levels within the campus and in its vicinity during peak hours.]]></description>
      <pubDate>Tue, 02 Sep 2025 08:45:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571382</guid>
    </item>
    <item>
      <title>Gender disparities in bike-sharing usage: Unveiling regularity and influential factors in a large-scale campus in Tianjin, China</title>
      <link>https://trid.trb.org/View/2567389</link>
      <description><![CDATA[Bike-sharing offers convenient and eco-friendly solutions primarily for short-distance trips. However, previous research has primarily focused on urban users, with limited attention to gender-specific bike-sharing usage behavior within campus environments. Compared with urban environments, campus bike-sharing exhibits unique temporal and spatial patterns driven by class schedules, campus layout, and restricted usage zones, leading to more regular and concentrated travel behavior. Using origin-destination travel data from dockless bike-sharing system implemented on a large-scale university campus in Tianjin, China, this research examined the spatiotemporal differences in bike-sharing usage across genders. Then, the entropy rate method and machine learning models were employed to quantify bike-sharing usage regularity and identify influential factors affecting regularity, respectively. Results show that male users are 1.85 times more than females, double the ridership, and demonstrate higher regularity compared to females. Travel distance is similar between males and females, but males have faster riding speeds. Competitive relationship between genders is revealed, particularly during peak hours around student residence and academic building. Finally, policy implications are suggested to enhance the existing campus bike-sharing service and promote the equity between genders.]]></description>
      <pubDate>Wed, 16 Jul 2025 09:50:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567389</guid>
    </item>
    <item>
      <title>Behavior Analysis of Shared Micro-Mobility Service in Mobility as a Service: Empirical Evidence from a Large-Scale Trial in a University Community</title>
      <link>https://trid.trb.org/View/2543494</link>
      <description><![CDATA[To date, there is limited evidence on how shared micro-mobility services are being used in Mobility as a Service (MaaS) trials. To add new evidence, this research investigated the MaaS trial data from the University of Queensland in Brisbane, Australia. Descriptive analysis and statistical models were employed to analyze participants’ usage behavior in shared micro-mobility services with MaaS bundles. From this analysis, several critical and interesting findings emerged. First, when compared with bundles including micro-mobility, basic mobility bundles with unlimited public transport services were more popular in the university community. Second, compared with the pay-as-you-go option, trial participants were more willing to adopt shared micro-mobility services with PASS options with daily travel budgets. Third, there was a substantial underutilization of shared micro-mobility budgets within existing mobility bundles. Fourth, multimodal bundle subscribers used shared micro-mobility services about two to three times longer per day, compared with basic bundle users. Finally, shared micro-mobility service providers seemed to experience positive externalities from other micro-mobility providers in the market. The findings could provide insights to optimize existing MaaS business models with micro-mobility and to help evaluate the impacts of MaaS on the travel behavior of university populations.]]></description>
      <pubDate>Mon, 28 Apr 2025 15:53:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543494</guid>
    </item>
    <item>
      <title>Active travel in the university setting: Assessing the effects of social behavior, socioeconomics, and spatial location</title>
      <link>https://trid.trb.org/View/2506281</link>
      <description><![CDATA[University campuses are pooling efforts to promote active mobility to reduce their negative impacts on the urban environment, which is significantly influenced by the overreliance on motorized modes of transport. Providing sufficient and safe accessibility conditions for active travel has been highlighted as a crucial strategy for transforming campuses into more livable and sustainable areas in cities. To further explore the likelihood of active mobility uptake at university campuses, this study explored university students’ mobility patterns over time, examining the role of social behavior, socioeconomics, and spatial location factors. The Faculty of Engineering at the University of Porto, Greater Oporto, Portugal, provided the empirical focus for this research. The data analyzed were acquired through surveys of representative samples and spatial analysis over the academic years of 2012, 2017, and 2023. The statistical analysis explained the tendencies and multifactorial influences on travel behavior among university students. Results indicated that travel distance is associated with housing options and travel costs, whereas access to a metro station was associated with walking or cycling. Hence, this study contributed to a deeper understanding of active travel behavior. It provided insights to guide planning practitioners and decision makers in creating integrated transport policy packages that address the barriers and needs of the university community and the surrounding neighborhoods.]]></description>
      <pubDate>Thu, 13 Feb 2025 17:25:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2506281</guid>
    </item>
    <item>
      <title>Heuristics and Metaheuristics for Solving Capacitated Vehicle Routing Problem: An Algorithm Comparison</title>
      <link>https://trid.trb.org/View/2380387</link>
      <description><![CDATA[The Capacitated Vehicle Routing Problem (CVRP) is an optimization problem that involves arranging vehicle routes while considering vehicle capacity. This research aims to compare the effectiveness of several heuristic (Path Cheapest Arc, Path Most Constrained Arc, Savings, Christofides) and metaheuristic (Greedy Descent, Guided Local Search, Simulated Annealing, Tabu Search) algorithms for determining the routing scenarios and vehicle types for faculty transportation between the male campus in Ponorogo and the female campus in Mantingan Ngawi at Universitas Darussalam Gontor. The research involves decision variables for vehicle routing determination and the objective of minimizing the distance traveled. The constraint function includes two options: one vehicle with a capacity of 60 passengers and four vehicles. This research utilizes Google OR Tools with the Python programming language using Google Colab to facilitate the calculation process. The research results indicate that metaheuristic algorithms outperform heuristics for complex case studies (four vehicles). This study recommends using metaheuristic methods, specifically Christofides Guided Local Search and Christofides Simulated Annealing, for determining the best routes with the shortest distance and time. Further research was developed using algorithms such as hyperheuristics or matheuristics.]]></description>
      <pubDate>Thu, 25 Jul 2024 17:11:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2380387</guid>
    </item>
    <item>
      <title>Selection of sustainable transportation strategies for campuses using hybrid decision-making approach under picture fuzzy sets</title>
      <link>https://trid.trb.org/View/2400706</link>
      <description><![CDATA[Given the increasing environmental concerns and urban congestion, sustainable transportation strategies have become crucial for campuses, which often reflect the transportation challenges of broader urban environments. Identifying such strategies specifically for campuses is urgently needed to tackle the adverse impacts of unsustainable transportation practices on carbon emissions, local air quality, campus health, and institutional budgets while promoting a culture of sustainability among university communities. To address this, a set of strategies, including 23 sub-strategies and 8 main strategies, along with 6 evaluation criteria, was determined following extensive research. Thus, the present study aims to select and prioritize the strategies of sustainable transportation within the campus environment by proposing a novel hybrid multi-criteria decision-making (MCDM) method, integrating Stepwise Weight Assessment Ratio Analysis (SWARA) and Evaluation Based on Distance from Average Solution (EDAS) under Picture fuzzy sets. This research also carried out sensitivity and comparative analyses to confirm the consistency and reliability of the results produced by the proposed hybrid method. The results indicate that short-term strategies, such as providing facilities for bike riders and reorganizing campus parking, ranked highest due to their immediate feasibility and cost-effectiveness. Conversely, strategies related to remote and flexible work options or affordable housing, which require a longer implementation process, and significant investment, ranked lower in the final rankings. Policy-makers, stakeholders, and campus administrators will gain useful insights from this research, which not only provides a holistic perspective on sustainable transportation planning within educational settings but also proposes a novel methodology, bridging the knowledge gap in the existing literature.]]></description>
      <pubDate>Wed, 17 Jul 2024 10:12:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2400706</guid>
    </item>
    <item>
      <title>Prioritizing bicycle parking improvements: An application of the logsum approach</title>
      <link>https://trid.trb.org/View/2390989</link>
      <description><![CDATA[Increasing the number and quality of bicycle parking facilities is essential to promoting cycling. However, quantifying the benefits of such improvements is difficult and has not yet been the focus of research. Apart from rule-of-half-based approaches, logsum analysis is a method for estimating the consumer surplus of measures, reflecting actual behavior.This paper presents such an analysis of a set of measures on the RWTH Aachen University campus as a case study. Considering only the direct consumer surplus, the economic efficiency of the analyzed measures diverges significantly. The replacement of inadequate front wheel racks has the best consumer surplus-cost ratio, while the construction of bicycle parking stations has the worst. Taking into account modal shift effects and related changes in externalities and reduced demand for car parking, the cost-benefit ratios of the measures would be much higher. Even though the presented logsum approach ignores these second-order effects and focuses exclusively on economic efficiency, it is still useful in practice for prioritizing potential measures. Apart from that, the results show that groups of cyclists benefit differently from certain types of measures depending on their student status, employee group, and the resale value of their bicycle. Additionally, the analysis emphasizes the importance of including informal parking when modeling bicycle parking behavior.]]></description>
      <pubDate>Fri, 28 Jun 2024 14:02:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2390989</guid>
    </item>
    <item>
      <title>Optimization of the subsidy for university faculty relocation in campus suburbanization</title>
      <link>https://trid.trb.org/View/2379780</link>
      <description><![CDATA[This study explores the optimal subsidy policy to maximize the benefits associated with the suburbanization of university campuses. A transport accessibility index is introduced, and a model is developed to analyze faculty housing relocation, incorporating factors such as transport accessibility, housing price, relocation subsidy, and the influence of children. The impact of housing relocation is assessed using a regional output model that considers both production and consumption aspects. Subsequently, a decision-making model is established to determine the optimal subsidy level and the number of faculty to relocate, with the overarching goal of maximizing total regional benefits. The findings reveal that an increase in subsidies correlates with a rise in the willingness of faculty to relocate, leading to heightened benefits for the region. However, the rate of benefit increase shows diminishing returns with each increment change in the subsidy. Notably, the study demonstrates that 70% of the additional benefits to the region emanate from the housing market, accurately reflecting the current financial landscape in China. This insight underscores why city governments frequently leverage land markets to actively promote suburbanization.]]></description>
      <pubDate>Wed, 29 May 2024 09:26:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2379780</guid>
    </item>
    <item>
      <title>Implementation of a spontaneous matching algorithm for on-demand shuttle systems in microsimulation</title>
      <link>https://trid.trb.org/View/2348294</link>
      <description><![CDATA[On-demand shuttle systems have gained popularity in the last years, recently more and more applications have started worldwide. The core of such an on-demand shuttle system is an intelligent matching algorithm, where vehicles and passenger requests are matched to each other in an optimized way. An appropriate platform to model and simulate matching algorithms is indeed a microscopic traffic simulation, as well-known for other modes of transportation. However, on-demand shuttle systems were not studied sufficiently in microsimulation environments up to now, most probably due to special characteristics of dynamic routing. This paper aims to close this gap and model a platform for on-demand shuttle systems in a microscopic traffic simulation, allowing a holistic microsimulation considering all transport modes. A spontaneous matching algorithm is introduced and its implementation in the microsimulation is elaborated. The results of a case study in the University of the Bundeswehr Munich campus are presented, showing the impacts of operation type and fleet size on essential metrics. The potential of such a platform as well as further research insights are discussed at the end of the paper.]]></description>
      <pubDate>Sat, 18 May 2024 17:07:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2348294</guid>
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