Transportation Research Part C-Emerging Technologies最新文献

{"title":"Optimizing multi-modal ride-matching with transfers","authors":"Patrick Stokkink ,&nbsp;André de Palma ,&nbsp;Nikolas Geroliminis","doi":"10.1016/j.trc.2025.105274","DOIUrl":"10.1016/j.trc.2025.105274","url":null,"abstract":"<div><div>One of the limitations of ride-sharing is that matched drivers and riders need to have similar itineraries and desired arrival times for ride-sharing to be competitive against other transport modes. By allowing a single transfer at a designated transfer hub, their itineraries need to be only partially similar, and therefore more matching options are created. In this paper, we develop an optimal matching approach that matches riders to drivers, taking into account multi-modal routing options to model competition and collaboration between multiple modes of transport. We allow for transfers between modes and between multiple drivers. We model this as a path-based integer programming problem and we develop a simulated annealing algorithm to efficiently solve realistic large-scale instances of the problem.</div><div>Our analysis indicates that a single transfer hub can reduce significantly the average generalized cost of riders and the total vehicle hours traveled by creating efficient matches. As opposed to previous studies, our work shows that ride-sharing not only attracts former public transport users but also former private car users. By allowing for intermodal transfers and by choosing the cost parameters such that transfers are favorable, itineraries where commuters use their car first, before sharing a ride on the second part of their journey, becomes an appealing alternative. Multi-modal ride-matching with transfers has the potential to increase ride-sharing, reduce the number of vehicle hours traveled in private cars, and reduce the number of cars that are present in urban areas during peak hours of congestion.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105274"},"PeriodicalIF":7.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid e-bike sharing system design problem considering multiple types of facilities","authors":"Jiatong Song ,&nbsp;W.Y. Szeto ,&nbsp;Baicheng Li ,&nbsp;Yi Wang ,&nbsp;Xingbin Zhan","doi":"10.1016/j.trc.2025.105267","DOIUrl":"10.1016/j.trc.2025.105267","url":null,"abstract":"<div><div>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.</div><div>In this paper, we 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 our proposed hybrid solution method, which is based on genetic algorithm coupled with our proposed capacity-setting heuristic. The lower-level problem is transformed into a fixed demand equivalent problem and solved by the self-regulated averaging method. We 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.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105267"},"PeriodicalIF":7.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electric versus diesel: Green supply chain network design with carbon footprint labeling","authors":"Ensieh Ghaedyheidary,&nbsp;Samir Elhedhli","doi":"10.1016/j.trc.2025.105277","DOIUrl":"10.1016/j.trc.2025.105277","url":null,"abstract":"<div><div>Transportation electrification and carbon footprint labeling are strong indicators of environmental commitment in green supply chains. We study this framework and assess its environmental and financial sustainability. We consider cradle-to-gate operations, from manufacturing to retail, mandate the use of electric trucks whenever their range allows, and impose a cap on product carbon footprints as would be advertised on a carbon label. We optimize the locations of distribution centers, the allocation of demand, and the transportation choices between diesel trucks and electric trucks. We account for the nonlinear relationship between emissions and payload for diesel trucks, focus on two representative functional forms- concave and convex- and propose a mixed-integer nonlinear optimization model to minimize costs and CO2-equivalent emissions. We use Lagrangean relaxation to decompose the model by echelon and isolate the convex and concave nonlinearity in an easy-to-solve subproblem. We then design a Lagrangean heuristic based on the solution of one of the subproblems, which has proven efficient and near-optimal. Based on a case study, we evaluate the impact of the emission function and the carbon label on the supply chain network, as well as the trade-off between the use of diesel trucks and electric trucks. We find that the relationship between emissions and payload for diesel trucks significantly influences the adoption of electric trucks. When concave, as would be the case for steady driving conditions, long hauls, well-maintained infrastructure, and light traffic, conventional diesel trucks continue to be the cost-efficient option, especially when electric truck mileage costs are high and the cap on unit emissions is elevated. In contrast, when diesel emissions are convex, corresponding to challenging driving conditions such as urban delivery, congested road networks, stop-and-go traffic, and degraded road infrastructure, transportation emissions dominate total emissions, diesel truck usage decreases, and electric trucks become a better choice even if the cap on unit emissions is high and diesel trucks are cheaper to operate. Furthermore, extending the range of electric trucks increases their usage under convex emissions but not under concave emissions, especially when the cap on carbon footprint is not tight.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105277"},"PeriodicalIF":7.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Goal-based neural physics vehicle trajectory prediction model","authors":"Rui Gan ,&nbsp;Haotian Shi ,&nbsp;Pei Li ,&nbsp;Keshu Wu ,&nbsp;Bocheng An ,&nbsp;Junwei You ,&nbsp;Linheng Li ,&nbsp;Junyi Ma ,&nbsp;Chengyuan Ma ,&nbsp;Bin Ran","doi":"10.1016/j.trc.2025.105283","DOIUrl":"10.1016/j.trc.2025.105283","url":null,"abstract":"<div><div>Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated errors and uncertainties. Additionally, balancing accuracy with interpretability in the prediction is another challenging issue in predicting vehicle trajectory. To address these challenges, this paper proposes a <strong>G</strong>oal-based <strong>N</strong>eural <strong>P</strong>hysics Vehicle Trajectory Prediction Model (<strong>GNP</strong>). The GNP model simplifies vehicle trajectory prediction into a two-stage process: determining the vehicle’s goal and then choosing the appropriate trajectory to reach this goal. The GNP model contains two sub-modules to achieve this process. The first sub-module employs a multi-head attention mechanism to accurately predict <strong>goals</strong>. The second sub-module integrates a deep learning model with a physics-based social force model to progressively predict the complete trajectory using the generated goals. The GNP demonstrates state-of-the-art long-term prediction accuracy compared to four baseline models. We provide interpretable visualization results to highlight the multi-modality and inherent nature of our neural physics framework. Additionally, ablation studies are performed to validate the effectiveness of our key designs. The source code for this work are available at: <span><span>https://github.com/mcgrche/GNP-Goal-based-Neural-Physics-Vehicle-Trajectory-Prediction-Model</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105283"},"PeriodicalIF":7.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-type traffic sensor location problem for origin–destination estimation considering spatiotemporal correlation and sensor failure","authors":"Weiwei Sun ,&nbsp;Hu Shao ,&nbsp;Junlin Li ,&nbsp;Ting Wu ,&nbsp;Emily Zhu Fainman","doi":"10.1016/j.trc.2025.105288","DOIUrl":"10.1016/j.trc.2025.105288","url":null,"abstract":"<div><div>The challenge of locating traffic sensors for origin–destination (OD) demand estimation involves determining the optimal number, location, and type of sensors needed to accurately gauge the traffic flow between OD pairs within an urban road network. However, analysis of traffic big data reveals that OD demand exhibits spatiotemporal correlation, indicating interconnected traffic flows between various OD pairs during different time periods. Additionally, it is important to acknowledge that sensors are prone to failure, with failure patterns varying over time and among different sensor types. In this paper, we propose new robust models for multi-type sensor location that consider the spatiotemporal correlation of OD demands and sensor failures. We estimate the mean and covariance of OD demands for different OD pairs cross various time periods to address spatiotemporal correlation, while establishing upper bounds on estimation errors to mitigate reliance on true OD demands. Furthermore, based on its bathtub curve characteristic, we employ the Weibull distribution to characterize the time-varying sensor failure rate, and utilize a nonlinear least squares method to estimate the parameters of failure rate function. Subsequently, we establish combination location models for count and automatic vehicle identification (AVI) sensors in scenarios with either no existing sensors or pre-existing sensors respectively, with the goal of minimizing upper bounds on error in both OD mean and covariance estimation while concurrently considering the spatiotemporal correlation of OD demand and time-varying sensor failure rate. Given the bi-objective nature and non-linear characteristics of these proposed models, we develop a non-dominated sorting genetic algorithm as a solution approach. Finally, we present numerical examples demonstrate how factors such as the spatiotemporal correlation of OD demands, time-varying sensor failure rate, sensor type, and budget constraints influence the sensor location strategy. The results confirm that our proposed models and algorithms deliver enhanced precision and faster convergence speed.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105288"},"PeriodicalIF":7.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conflict probability based strategic conflict resolution for UAS traffic management considering Reasonable-Time-To-Act principle","authors":"Yiwen Tang ,&nbsp;Yan Xu ,&nbsp;Renli Lv ,&nbsp;Gokhan Inalhan","doi":"10.1016/j.trc.2025.105276","DOIUrl":"10.1016/j.trc.2025.105276","url":null,"abstract":"<div><div>This paper introduces a strategic conflict resolution method for Unmanned Aircraft System Traffic Management (UTM) that incorporates conflict probability estimation. The method discusses a two-step conflict detection process and three conflict resolution approaches: First-Come First-Served (FCFS), Reasonable Time To Act (RTTA), and an optimisation technique. The conflict detection process begins with coarse screening, which identifies intersecting Operational Volumes (OV) with potential conflicts, followed by precise conflict probability estimation. Intersections exceeding the predefined probability threshold from safety perspective are classified as conflicts, while others are deemed acceptable. By reallocating take-off time slot, the conflict resolution approaches have different priorities: the FCFS method adheres to traditional practices, the RTTA concept prioritises fairness, and the optimisation technique enhances efficiency. Numerical experiments are conducted in three-dimensional airspace environment under varying traffic densities. Six representative case studies linked by two threads, i.e., efficiency and fairness, thoroughly assess the proposed methods. Results indicate that integrating conflict probability significantly reduces delays, and further reductions can be achieved through the optimisation technique. The fairness issue inherit in the FCFS rule for flight processing is effectively addressed through the implementation of the RTTA principle.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105276"},"PeriodicalIF":7.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video-Based experiments better unveil societal biases towards ethical decisions of autonomous vehicles","authors":"Vladimir Maksimenko ,&nbsp;Xinwei Li ,&nbsp;Eui-Jin Kim ,&nbsp;Prateek Bansal","doi":"10.1016/j.trc.2025.105284","DOIUrl":"10.1016/j.trc.2025.105284","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) encounter moral dilemmas when determining whom to sacrifice in unavoidable crashes. To increase the trustworthiness of AVs, policymakers need to understand public judgment on how AVs should act in such ethically complex situations. Previous studies have evaluated public perception about these ethical matters using picture-based surveys and reported societal biases, i.e., systematic variations in ethical decisions based on the socioeconomic characteristics (e.g., gender) of the individuals involved. For instance, females may prioritise saving a female pedestrian in AV-pedestrian incidents. We investigate if these biases stem from personal beliefs or emerge during experiment engagement and if the presentation format affects bias manifestation. Analysing neural responses in moral experiments measured using electroencephalography (EEG) and behaviour model parameters, we find that video-based scenes better unveil societal biases than picture-based scenes. These biases emerge when the subject interacts with experimental information rather than being solely dictated by initial preferences. The findings support the use of realistic video-based scenes in moral experiments. These insights can inform data collection standards to shape socially acceptable ethical AI policies.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105284"},"PeriodicalIF":7.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesising activity participations and scheduling with deep generative machine learning","authors":"Fred Shone,&nbsp;Tim Hillel","doi":"10.1016/j.trc.2025.105273","DOIUrl":"10.1016/j.trc.2025.105273","url":null,"abstract":"<div><div>Using a deep generative machine learning approach, we synthesise human activity participations and scheduling; i.e. the choices of what activities to participate in and when. Activity schedules are a core component of many applied transport, energy, and epidemiology models. Our data-driven approach directly learns the distributions resulting from human preferences and scheduling logic without the need for complex interacting combinations of sub-models and custom rules. This makes our approach significantly faster and simpler to operate than existing approaches to synthesise or anonymise schedule data. We additionally contribute a novel schedule representation and a comprehensive evaluation framework. We evaluate a range of schedule encoding and deep model architecture combinations. The evaluation shows our approach can rapidly generate large, diverse, novel, and realistic synthetic samples of activity schedules.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105273"},"PeriodicalIF":7.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating practical last-mile delivery routes using a data-informed insertion heuristic","authors":"Hesam Rashidi ,&nbsp;Mehdi Nourinejad ,&nbsp;Matthew Roorda","doi":"10.1016/j.trc.2025.105278","DOIUrl":"10.1016/j.trc.2025.105278","url":null,"abstract":"<div><div>Couriers often deviate from pre-planned delivery routes due to practical realities that routing algorithms may overlook. We statistically demonstrate that, in addition to travel time, factors like turn sharpness, backtracking distance, and neighbourhood visit timing influence a driver’s navigational choices and propose a Data-informed Insertion Heuristic (DIIH) for Travelling Salesman Problems (TSPs), which considers a custom cost function inferred from historical routes. The DIIH is trained on the dataset from Amazon’s Last-mile Research Challenge, which contains historical TSP instances classified into one of three qualities: high, medium, or low, indicating the satisfaction level of Amazon’s logistics planners of a route based on productivity, courier experience, and customer satisfaction levels. We train an energy-based model to predict the likelihood of a route being of high quality. Compared to existing benchmarks, the DIIH generates 22.4% and 24.1% additional high-quality solutions than the Amazon challenge winner and courier-performed routes, respectively. This improvement comes with an increase of 20.6% and 13.9% in the median travel time. While optimizing purely for travel time would result in shorter routes, we account for both travel time and human preferences, which explains the observed tradeoff. We show that the probabilistic evaluation of a route measured by the energy-based model developed in this study is a promising metric for estimating a routing algorithm’s practical performance.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105278"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic fundamental diagram modeling of mixed traffic flow: A data-driven approach","authors":"Xiaohui Zhang ,&nbsp;Kaidi Yang ,&nbsp;Jie Sun ,&nbsp;Jian Sun","doi":"10.1016/j.trc.2025.105279","DOIUrl":"10.1016/j.trc.2025.105279","url":null,"abstract":"<div><div>The integration of automated vehicles (AVs) into existing traffic of human-driven vehicles (HVs) poses significant challenges in modeling and optimizing mixed traffic flow. Existing research on the impacts of AVs often neglects the stochastic nature of traffic flow that gets further complicated by the introduction of AVs, and mainly relies on unrealistic assumptions, such as oversimplified headway or specific car-following (CF) models. The under-utilization of empirical AV datasets further exacerbates these issues, raising concerns about the realism and applicability of existing findings. To address these limitations, this paper presents a novel data-driven framework to model the stochastic fundamental diagram (SFD) of mixed traffic flow using AV trajectory datasets. Specifically, we learn the CF behavior of different leader–follower pairs (HV following AV, HV following HV, AV following HV, AV following AV) from data, unified by a conditional distribution, using the mixture density network (MDN). By formulating the platoon as a joint distribution through Markov chain modeling and incorporating all possible platoon arrangements, we then derive the SFD of mixed traffic flow. Using the NGSIM I-80 dataset, which enables aggregating the empirical fundamental diagram, we validate the proposed framework by demonstrating high consistency with the empirical result. We then apply the framework to the Waymo dataset to evaluate the impact of real-world AVs on traffic flow. The results indicate that larger AV penetration rates lead to decreased mean capacity and critical density while reducing capacity uncertainty, due to the conservative yet stable behavior of current AVs. Overall, this work establishes a general probabilistic modeling framework for mixed traffic flow, enabling the input of real-world AV trajectory datasets and output of the SFD under given AV penetration rates and AV spatial distributions. The proposed framework further facilitates assessing and comparing mixed traffic management strategies, with significant implications for future traffic system design and policy-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105279"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}