Multimodal Transportation最新文献

{"title":"Reinforcement learning in transportation research: Frontiers and future directions","authors":"","doi":"10.1016/j.multra.2024.100164","DOIUrl":"10.1016/j.multra.2024.100164","url":null,"abstract":"<div><p>The development of reinforcement learning (RL) provides innovative solutions for various decision-making problems in transportation, often pertaining to integrating advanced vehicular technologies such as connected and autonomous vehicles and electric vehicles. This paper reviews transportation research with RL-based methods over the recent decades. We start with a bibliometric analysis through extensive literature retrieval of 1030 papers from 1996 to 2023. We identify different research areas of RL in transportation, summarizing the most visited research problems. We find that, at the vehicle level, motion and route planning and energy-efficient driving problems have attracted the most attention. Meanwhile, adaptive traffic signal control and management have been the most visited problems at the network level. We discuss several potential future directions, including the migration of RL models from simulations to real-world cases, designing tailored control architectures for complex transportation systems, exploring explainable RL in transportation research to ensure transparency and accountability in decision-making processes, and integrating people and vehicles into transportation systems in a sustainable and equitable manner.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000455/pdfft?md5=e3dac5e354b11e94ecca9b838c9118dd&pid=1-s2.0-S2772586324000455-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancement of the impacts 2050 model to enable a whole system sustainability assessment of rideshare","authors":"","doi":"10.1016/j.multra.2024.100171","DOIUrl":"10.1016/j.multra.2024.100171","url":null,"abstract":"<div><p>Emerging concepts, such as Mobility as a Service (MaaS), could evolve to provide sustainable mobility, especially in densely populated urban areas. However, recent studies highlight the challenge of evaluating how the complex interactions of user demographics, mode choice, vehicle automation, governance, and efficiency will impact the sustainability of future mobility. Given this challenge, this research identifies a whole system (STEEP - social, technical, economic, environmental, and political) framework as essential to assess the overall sustainability of emergent urban mobility systems such as rideshare. The need is a single tool that can rapidly explore the long-range sustainability impact of such alternative future mobility scenarios for a given city region. This paper documents enhancements made to Impacts 2050, a strategic-level model of urban mobility, to address this need, including updates to the statistical travel behavior model and the addition of rideshare including trip occupancy. Results obtained with the enhanced Impacts 2050 showed that, while rideshare use increased significantly for some scenarios, its overall mode share remained limited. In addition, though rideshare enabled users to shed car ownership, the overall percentage increase of “no car ownership” was low. An urban mobility sustainability scorecard based on STEEP and generated by output from the enhanced Impacts 2050 is presented.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000522/pdfft?md5=6cc981bdab92bb50f51d7ef93c060687&pid=1-s2.0-S2772586324000522-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-creating multimodal transportation hubs in Switzerland: How to close the gap between actors across different scales, levels, and sectors","authors":"","doi":"10.1016/j.multra.2024.100168","DOIUrl":"10.1016/j.multra.2024.100168","url":null,"abstract":"<div><p>Transforming railway stations and their surroundings into multimodal transportation hubs (MMTHs) involves numerous actors at different scales and from different economic sectors and levels of government. Successful transformations offer a wide range of benefits to sustainable development, including increased public transportation use and mixed-use, high-density station districts. Intensive collaboration and, ideally, co-creation are critical to achieving these outcomes through MMTHs; however, orchestrating all involved actors is challenging and requires supporting methods, and the knowledge required to develop and refine methods is scarce and rarely subjected to systematic analysis. Based on 15 semistructured interviews and two design thinking workshops attended by 13 and 20 MMTH experts, our study shows that the challenges of co-creating MMTHs relate not only to professional matters but also to managing collaboration and implementation among a large number of actors with various roles and interests. In this paper, we develop design guidelines for reviewing and evaluating two current methods and a prototypical method (the functional model) with the goal of identifying potential improvements and supporting MMTH co-creation in Switzerland. These guidelines cover the broad spectrum of co-creation activities, from organization and design collaboration with relevant actors to the development of a shared vision to support financing and the planning process. The functional model encompasses many aspects of the design guidelines and closes gaps between actors across different scales, economic sectors, and governmental levels. Due to the relatively low effort involved, the method can be repeated as needed throughout MMTH development, which often takes several years. Our study demonstrates that existing MMTH co-creation methods require improvement, and the design guidelines developed here suit this purpose. Our work thus contributes to the further development of MMTH co-creation methods, ultimately supporting sustainable development such as CO₂ emission reduction and responsible land use.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000492/pdfft?md5=3714111297e6fe50f6aa490dd1aefbca&pid=1-s2.0-S2772586324000492-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring micromobility choice behavior across different mode users using machine learning methods","authors":"","doi":"10.1016/j.multra.2024.100167","DOIUrl":"10.1016/j.multra.2024.100167","url":null,"abstract":"<div><p>In an effort to capture travelers’ propensity towards micro-mobility options, a consumer survey was designed and conducted in the state of Florida in Fall 2021. In addition to collecting socioeconomic, demographic, attitudinal, and trip-related information, stated-preference scenarios were presented to the respondents, in which they were asked to choose between their current mode, and three different micro-mobility alternatives, namely: e-scooter, e-scooter + public transit, and moped. A machine learning classification model, the tree-based Extreme Gradient Boosting algorithm was applied to study users’ mode choice toward micromobility options given its non-parametric nature and high predictive power. SHAP values were then used to analyze the contributing factors for each of the micro-mobility options. In addition, Local Interpretable Model-agnostic Explanations (LIME) was employed to interpret and validate the SHAP findings at the individual prediction level. Model results show that age, car-oriented attitudes, lack of familiarity/previous experience, and lack of appropriate infrastructures were the major barriers to choose micro-mobility services. Such services can be suitable alternatives for young people who come from large families or ride-share users who have prior experience with micromobility services. Among different micro-mobility alternatives, mopeds were favored by males and green travelers. It seems that e-scooter + public transit was considered a safe and comfortable option, especially for students and low-income individuals, but generally not favored by travel time-sensitive or green travelers. Finally, e-scooters seem to be a favorable option for younger individuals with short travel distances. Our findings provide additional insights on policies that may help encourage the use of micromobility devices and promote sustainable, affordable, and equitable mobility services.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000480/pdfft?md5=627854bf007e54aa81f10ca0ead8441d&pid=1-s2.0-S2772586324000480-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of technology belief, perceived risk and initial trust in users’ acceptance of urban air mobility: An empirical case in China","authors":"","doi":"10.1016/j.multra.2024.100169","DOIUrl":"10.1016/j.multra.2024.100169","url":null,"abstract":"<div><p>Urban Air Mobility (UAM) is poised to revolutionize transportation, necessitating an assessment of public acceptance before broad commercial adoption. This study presents the Urban Air Mobility Acceptance Model (UAM-AM), which draws from the Technology Acceptance Model (TAM) and underscores the crucial role of initial trust, technology belief and perceived risk. The UAM-AM is validated using Structural Equation Modeling (SEM) based on 544 questionnaires for the first time in China. The findings highlight the significant impact of perceived ease of use and perceived usefulness on acceptance, uncovering a complex interplay with the intention to utilize UAM services. Notably, initial trust emerges as a foundational factor, influencing attitudes directly or indirectly through perceived ease of use and perceived usefulness. Moreover, the research identifies technology belief and perceived risk as fundamental drivers of initial trust. Examination of demographic segments reveals a heightened technology belief among individuals with backgrounds in science, indicative of a more favorable attitude towards UAM adoption. In closing, the paper presents recommendations for policymakers, service providers, and eVTOL manufacturers to formulate effective strategies that promote public acceptance during the initial phases of UAM deployment.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000509/pdfft?md5=047451640b5c4fbbaac15c4dcf4f8fb5&pid=1-s2.0-S2772586324000509-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for taxi service conditions in estimating route travel time from floating car data using Markov chain model","authors":"","doi":"10.1016/j.multra.2024.100172","DOIUrl":"10.1016/j.multra.2024.100172","url":null,"abstract":"<div><p>Recognising the variations in driving behaviour between taxis in the <em>empty</em> and <em>carry</em> conditions is pivotal for enhancing the accuracy of route travel time estimations using floating car data. However, existing methods largely overlook this distinction. In light of this, this study aims to harness these variations for more precise estimations. Utilising taxi data, we segmented the information by service conditions and executed distinct estimations for each segment. The route travel time was deduced through convolutional operation, complemented by a Markov chain model to discern correlations between travel times across various links. Our innovative approach realised a substantial enhancement in accuracy. Notably, when accounting for distinct service conditions, there was a reduction of 51.44% in mean absolute error and a 46.83% decline in maximum percentage error. By providing more accurate and reliable travel time predictions, our methodology enables better-informed traffic management decisions. Accurate travel time estimations are essential for optimising traffic signal timings, planning efficient routing strategies, and managing road network usage. These improvements in traffic management can lead to smoother traffic flow, reduced travel times, and ultimately, diminished congestion in urban areas.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000534/pdfft?md5=9d880b6e35f44d43c139dc4a2f00a4c4&pid=1-s2.0-S2772586324000534-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation","authors":"","doi":"10.1016/j.multra.2024.100165","DOIUrl":"10.1016/j.multra.2024.100165","url":null,"abstract":"<div><p>This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000467/pdfft?md5=da8c0a7b7f718db6d301e0cca2f5c377&pid=1-s2.0-S2772586324000467-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gated ensemble of spatio-temporal mixture of experts for multi-task learning in ride-hailing system","authors":"","doi":"10.1016/j.multra.2024.100166","DOIUrl":"10.1016/j.multra.2024.100166","url":null,"abstract":"<div><p>Ride-hailing system requires efficient management of dynamic demand and supply to ensure optimal service delivery, pricing strategies, and operational efficiency. Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner to forecast demand and supply-demand gap in a ride-hailing system poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting these spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000479/pdfft?md5=551d06c4b7c13a4f3346a444a828e9f5&pid=1-s2.0-S2772586324000479-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demand study of freight transportation via railway of four commodity groups: A case study on Costa Rica's Pacific Coast Route","authors":"","doi":"10.1016/j.multra.2024.100157","DOIUrl":"10.1016/j.multra.2024.100157","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Having railway mobility as an option among transportation modes is of prime importance for the freight logistics of a country and the overall performance of its commerce sector. This work presents a case study in Costa Rica, where freight transportation via railway was formerly available during the last century but has not been in full service since 1995. Specifically, this paper assesses the link connecting the capital city of San Jose to the port of Caldera on the Pacific Ocean coast, henceforth referred to as the “Pacific Coast Route”. Although government institutions and industries alike have stressed the importance of its reactivation in recent years, few efforts have been made to specifically study this route with a level of detail that enables them to understand the composition of freight markets.&lt;/p&gt;&lt;p&gt;This study presents a novel demand estimation differentiated by four types of commodities developed to calculate the potential amount of goods that could flow through this corridor, measured in monetary terms and as volumes. Trends in the expected share were analyzed to identify variables that may improve the attractiveness of the railway service and the participation of clients along the route from the country's main economic activities which includes the following: agriculture &amp; livestock, manufacturing, minerals, and forestry.&lt;/p&gt;&lt;p&gt;The four-step transportation model was chosen for its suitability towards this case study; where available data sets were limited, the information was aggregated into yearly values, and the network was defined with few transportation zones. Activity-based models were not feasible in this case due to the lack of information on complete routes and volume flows. The model evaluated a first scenario given the present design of the route and a second scenario involving improvements in the alignment and the addition of a new segment.&lt;/p&gt;&lt;p&gt;Regarding the opportunities of moving goods according to their type, in terms of volume, minerals (48 %) and agricultural (31 %) have the largest shares of participation in this region, followed by manufacturing (20 %) and forestry products (1 %). However, the manufacturing sector stands out in financial terms, accounting for 71 % of the total value of goods mobilized in the study area.&lt;/p&gt;&lt;p&gt;It was found that the demand is most sensible for changes in the service fare, while the benefits of shorter travel times and a larger attraction area in scenario 2 were not significant. A service fee of $USD 2.35/ton in average is recommended to reach a 30 % modal shift towards the railway (following the European Commission's goals to reduce CO&lt;sub&gt;2&lt;/sub&gt; emissions from freight transportation in 2030).&lt;/p&gt;&lt;p&gt;Finally, recommendations for public policies are provided to the entities in the country in the sectors of Agriculture, Health, Energy and Environment, Transportation, and Public Services. The suggestions aim to develop the new railway project with a holistic perspective, c","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000388/pdfft?md5=d0f66ae7c52ef3c84926c8c6e0f01600&pid=1-s2.0-S2772586324000388-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative optimization method for multiple feeder buses timetables for bridging rail transit","authors":"","doi":"10.1016/j.multra.2024.100155","DOIUrl":"10.1016/j.multra.2024.100155","url":null,"abstract":"<div><p>Urban multimodal public transport system with rail transit as the backbone and bus as the collaborative operation is an important carrier for the travel of urban residents. For improving efficiency of urban public transport services, it is important to optimize the feeder bus operation plans and promote efficient bridging. A quantitative method based on the data envelope analysis (DEA) algorithm was proposed to effectively evaluate the transfer efficiency of transit hubs and identify service bottlenecks, which used multi-source data including smart card transaction data, bus vehicle operation data and public transport trip chain data based on multimodal individual travel transaction data. Considering the passenger transfer and supply capacity of the feeder bus, and balancing between transfer and regular travel needs, a collaborative optimization model based on the branch and bound (BB) algorithm which can quickly obtain the absolute optimal solution of small-scale optimization problems is established to combinatorial optimize less efficient feeder bus line groups. The Tiantongyuan North transit hub and its feeder bus line in Beijing were selected as the case analysis. The results indicated that the main reasons for the failure of public transport to achieve DEA effectiveness are the departure frequency and the number of operating buses. With the collaborative optimization of the bus routing schedule, the average total transfer time for the transfer passengers is reduced by 19.9%, while the transfer time and waiting time are reduced by 22.8%, 16.1% respectively, which will help improve the efficiency and service level of multimodal public transport collaborative operation.</p></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772586324000364/pdfft?md5=3af96e76730cd581f386ed6460db2927&pid=1-s2.0-S2772586324000364-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}