Transportation Research Part C-Emerging Technologies最新文献

{"title":"Dynamic adjustment strategy of electric bus operations: A spatial branch-and-bound method with acceleration techniques","authors":"Yin Yuan ,&nbsp;Shukai Li ,&nbsp;Andrea D’Ariano ,&nbsp;Tommaso Bosi ,&nbsp;Lixing Yang","doi":"10.1016/j.trc.2025.105003","DOIUrl":"10.1016/j.trc.2025.105003","url":null,"abstract":"<div><div>Electric bus systems frequently encounter operational instability, resulting in delays, bunching and disturbed charging schemes. Advanced technologies like sensoring and wireless connectivity strongly support dynamic adjustments to improve the stability of electric bus systems, fostering flexibility responsiveness to changing operating conditions. This article explores the dynamic adjustment problem of electric bus operations to jointly generate bus charging schemes and timetable adjustments in a real-time decision-making process. We propose a mixed-integer nonlinear programming model for each decision stage, explicitly considering factors such as vehicle overtaking, passenger load, capacity limitations, and charging behaviors. To efficiently solve this problem, we design a spatial branch-and-bound method with multiple acceleration techniques of logical inference for bound contraction, bilinear-specific branching, and parallel computation. Indeed, the original mixed-integer nonlinear programming problem can be split into a series of mixed-integer quadratic programming problems with reduced domains. The acceleration techniques proposed could be easily customized to address other mixed-integer nonlinear programs in such branch-and-bound-based schemes. Computational experiments validate the effectiveness of the proposed adjustment strategy yielding feasible solutions that enhance headway regularity, energy savings and service quality. Additionally, our solution method efficiently tackles real-world scenarios of significant complexity, with up to 12 vehicles running 8 loops on a route comprising 54 stops, with an average of 5.11-seconds computational time, outperforming both the common commercial solver and standard spatial branch-and-bound approaches in computational speed.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 105003"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096393","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":"Vehicle routing in one-way carsharing service with ridesharing options: A variable neighborhood search algorithm","authors":"Jiaqi Guo ,&nbsp;Jiancheng Long ,&nbsp;W.Y. Szeto ,&nbsp;Weimin Tan ,&nbsp;Sisi Jian","doi":"10.1016/j.trc.2024.104983","DOIUrl":"10.1016/j.trc.2024.104983","url":null,"abstract":"<div><div>The widespread adoption of one-way carsharing systems faces a significant hurdle in the form of vehicle imbalance. To address this challenge, this study proposes a novel hybrid operator-user-based relocation scheme that integrates one-way carsharing systems with ridesharing options, enabling users to complete their trips by sharing carsharing vehicles with others. This integration necessitates the concurrent optimization of vehicle relocation routing and ridesharing matching strategies by carsharing operators. The underlying problem, termed the vehicle relocation and ridesharing matching problem, is formulated as a mixed-integer linear program with the objective of maximizing total system profit. Given the NP-hard nature of the vehicle relocation and ridesharing matching problem, a variable neighborhood search algorithm is developed for its solution. The algorithm incorporates an efficient route evaluation scheme to improve the efficiency of the algorithm. Numerical experiments demonstrate that the proposed solution method is capable of producing high-quality solutions within short computing time. We also show that the mutual benefits of the proposed integrated scheme for both carsharing operators and users, including increased profitability, reduced travel costs, and improved service quality.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104983"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096516","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":"Incorporating lane-change prediction into energy-efficient speed control of connected autonomous vehicles at intersections","authors":"Maziar Zamanpour ,&nbsp;Suiyi He ,&nbsp;Michael W. Levin ,&nbsp;Zongxuan Sun","doi":"10.1016/j.trc.2024.104968","DOIUrl":"10.1016/j.trc.2024.104968","url":null,"abstract":"<div><div>Connected and autonomous vehicles (CAVs) possess the capability of perception and information broadcasting with other CAVs and connected intersections. Additionally, they exhibit computational abilities and can be controlled strategically, offering energy benefits. One potential control strategy is real-time speed control, which adjusts the vehicle speed by taking advantage of broadcasted traffic information, such as signal timings. However, the optimal control is likely to increase the gap in front of the controlled CAV, which induces lane changing by other drivers. This study proposes a modified traffic flow model that aims to predict lane-changing occurrences and assess the impact of lane changes on future traffic states. The primary objective is to improve energy efficiency. The prediction model is based on a cell division platform and is derived considering the additional flow during lane changing. An optimal control strategy is then developed, subject to the predicted trajectory generated for the preceding vehicle. Lane change prediction estimates future speed and gap of vehicles, based on predicted traffic states. The proposed framework outperforms the non-lane change traffic model, resulting in up to 13% energy savings when lane changing is predicted 4–6 s in advance.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104968"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095605","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":"Combining choice and response time data to analyse the ride-acceptance behavior of ride-sourcing drivers","authors":"Mana Meskar ,&nbsp;Rico Krueger ,&nbsp;Filipe Rodrigues ,&nbsp;Shirin Aslani ,&nbsp;Mohammad Modarres","doi":"10.1016/j.trc.2024.104977","DOIUrl":"10.1016/j.trc.2024.104977","url":null,"abstract":"<div><div>This paper investigates the ride-acceptance behavior of drivers on ride-sourcing platforms, considering drivers’ freedom to accept or reject ride requests. Understanding drivers’ preferences is vital for ride-sourcing services to improve the matching of requests to drivers. To this end, we obtained a unique dataset from a major ride-sourcing platform in Iran. This dataset provides comprehensive details of driver and ride characteristics for both successful and unsuccessful matchings. We investigate the ride-acceptance behavior of drivers using a hierarchical drift–diffusion model, which captures the dependency between drivers’ choices and response times. This dependency implies that response time, in addition to the request acceptance or rejection decision, contains valuable information about drivers’ preferences which allows us to better comprehend drivers’ ride-acceptance behaviors. Furthermore, we conduct a thorough comparison between the drift–diffusion model and the logit model, considering their predictive ability, parameter estimates, and elasticities. Within the drift–diffusion model framework, we also derive time-dependent elasticities of acceptance probability and elasticity of drivers’ response times. Our results demonstrate that ride fare, ride duration to request origin, and rainfall volume have the most impact on drivers’ ride-acceptance decisions. The insights derived from this study can be utilized to enhance platform matching algorithms and strategies, thereby improving the efficiency of ride-sourcing platforms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104977"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095606","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":"Near time-optimal trajectory optimisation for drones in last-mile delivery using spatial reformulation approach","authors":"Y.Y. Chan ,&nbsp;Kam K.H. Ng ,&nbsp;Tianqi Wang ,&nbsp;K.K. Hon ,&nbsp;Chun-Ho Liu","doi":"10.1016/j.trc.2024.104986","DOIUrl":"10.1016/j.trc.2024.104986","url":null,"abstract":"<div><div>Seeking a computationally efficient and time-optimal trajectory for drones is crucial for saving time and energy costs, especially in the field of drone parcel delivery. Still, last-mile drone delivery is a challenge in urban environments, due to the existence of complex spatial constraints arising from high-rise buildings and the inherent non-linearity of the system dynamics. This paper presents a three-stage method to address the trajectory optimisation problem in a constrained environment. First, the kinematics and dynamics of the quadcopter are reformulated in terms of spatial coordinates, which enables the explicit evaluation of the progress of the path. Second, an efficient flight corridor generation algorithm is presented based on the transverse coordinates of the spatial reformulation. Third, the nonlinear model predictive control (NMPC)-based optimal control problem with obstacle avoidance is formulated for solving the time-optimal trajectory. Compared to the true time-optimal trajectory, the flight time of the near time-optimal trajectory is 3.10% longer than the true time-optimal trajectory, but with a 92.5% reduction in computation time. Numerical simulations based on an illustrative scenario as well as a real-world urban environment are conducted. Results demonstrate the effectiveness of the proposed method in generating near time-optimal trajectory but with a reduced computational burden.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104986"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096046","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":"Toward low-burden travel survey: Identifying travel modes from GPS tracks fusing individual histories and enumerated annotations","authors":"Jiaqi Zeng ,&nbsp;Yulang Huang ,&nbsp;Meng Zhang ,&nbsp;Wenbin Yao ,&nbsp;Sheng Jin ,&nbsp;Dianhai Wang","doi":"10.1016/j.trc.2024.104975","DOIUrl":"10.1016/j.trc.2024.104975","url":null,"abstract":"<div><div>GPS-based travel surveys, coupled with automatic Travel Mode Identification (TMI) techniques, have emerged as effective tools for capturing travel details while reducing participant annotation burdens. However, existing models typically identify travel modes on a per-trip basis without considering individual travel regularity. More importantly, incorrect identification increases modification burdens, an issue that has not been adequately assessed or addressed. We propose a novel TMI scheme for travel surveys: participants enumerate their used travel modes before TMI, and the History and Annotation Informed Network (HAIN) model integrates individual historical travel information and enumerated annotations to infer the current travel mode chain, thereby improving accuracy and reducing the need for modifications. The designed history and annotation fusion modules in HAIN are plug-and-play and can operate separately. Additionally, we introduce the “number of edits” to quantitatively assess user annotation burden. We divide data by travelers and conduct three-fold cross-validation to approximate real-world scenarios. Results show that the accuracy of the state-of-the-art model is 83.14%; it reaches 86.3% when identifying trips sequentially with previously inferred histories; using corrected histories improves accuracy to 88.26%; and incorporating enumerated annotations raises it to 96.03%. Correspondingly, the annotation burdens are reduced to 43.8%, 39.9%, 36.6%, and 24.3% of what they would be without TMI. The two fusion modules also enhance the performance of baseline models. The history fusion improves model robustness when incorrect annotations occur. Comprehensive experiments indicate that the proposed scheme significantly enhances data collection efficiency and improves user experience.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104975"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096498","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":"Enhancing public transit adoption through personalized incentives: a large-scale analysis leveraging adaptive stacking extreme gradient boosting in China","authors":"Haoyang Yan ,&nbsp;Xiaolei Ma ,&nbsp;Bing Liu ,&nbsp;Erlong Tan ,&nbsp;Yujie Li ,&nbsp;Zirui Ni ,&nbsp;Tian-Liang Liu","doi":"10.1016/j.trc.2024.104992","DOIUrl":"10.1016/j.trc.2024.104992","url":null,"abstract":"<div><div>Motivating individuals to utilize public transportation through financial strategies, including both rewards and penalties, has been acknowledged as an effective approach to manage traffic demand and mitigate congestion-related issues. Personalized travel rewards, in contrast to economic sanctions like road tolls, tend to be more socially accepted. Nonetheless, insights into the effectiveness of personalized incentives remain limited, often constrained by studies that rely on small, non-representative samples of travelers. This study seeks to identify the variables that prompt individuals to switch to public transportation, drawing on extensive quasi-experimental data from a widespread public transit incentive program featured in one of China’s the largest navigation apps. This data encompasses the sociodemographic details of users, as well as their local and long-distance travel patterns. Both a binary Logit model and an adaptive stacking extreme gradient boosting (AS-XGB) model are applied to interpret and predict the changes in users’ public transit usage. Besides gender, job type and preferred travel mode, incentive reward category is found to be one of the significant determinants. In particular, rewards such as breakfast bread or travel vouchers have proven more effective than other types of incentives, like supermarket coupons or tissue gift bags. Female participants, individuals without children, and those who used public transportation in the week prior to receiving the incentives showed a higher propensity to embrace these rewards. However, the influence of education level, car ownership status, or preferred travel mode largely varies as the city’s development level. For intercity travel, regardless of whether the user owns a car or not, her/his income level and education level both have significant impacts on the incentive effectiveness.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104992"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104805","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":"Cost-competitiveness analysis of mobile chargers in an electric vehicle parking and charging system","authors":"Yanling Deng ,&nbsp;Zhibin Chen ,&nbsp;Xi Lin","doi":"10.1016/j.trc.2024.104951","DOIUrl":"10.1016/j.trc.2024.104951","url":null,"abstract":"<div><div>Currently, mobile chargers (MCs) are gaining popularity owing to their flexibility and the potential to solve the widespread issue of chargers being occupied after the charging process has finished, due to delayed departures of electric vehicles (EVs). In this study, we investigate the adoption of MCs in the EV parking and charging system (EVPCS) and demonstrate its cost-competitiveness through comparison with fixed chargers (FCs). First, we propose a modified M/M/n/K queueing model with two-phase services to capture the EVs’ dwell-after-charging behavior. Then we present the steady-state analysis with a matrix analytical method to analyze the properties of the proposed models. To evaluate and compare the performances of these two types of charging facilities, several key measures like blocking probability, average queue length and delay, and chargers’ utilization rate are derived, and extensive experiments exploring diverse scenarios are obtained. Numerical results uncover that: (1) the EVPCS configuration and EVs’ arrival and service rates have distinct impacts on the performance metrics of different types of chargers; (2) the MC may lose its competitiveness if the EV arrival rate is relatively low along with a high charging service rate or when the charger proportion approaches 1 in a small-sized EVPCS; only when the system is overloaded would the MC have a higher level of service than the FC; (3) in terms of cost efficiency, MCs demonstrate better competitiveness than the low-powered FCs considering the equivalent service with higher consumer surplus, while competing with moderately to high-powered FCs, MCs have little superiority; MCs are more profitable to the operator when competing with either low or high-powered FCs, but not for moderately-powered FCs; yet, the MC’s charging powers is required to be at an acceptably moderate level to demonstrate its cost-competitiveness. Furthermore, analytical formulas have been developed to approximate the two-phase queueing model under certain scenarios, and their accuracy has been compared with the customized two-phase queueing model.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104951"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095601","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-agent trajectory prediction at unsignalized intersections: An improved generative adversarial network accounting for collision avoidance behaviors","authors":"Lei Zhao,&nbsp;Wei Zhou,&nbsp;Sixuan Xu,&nbsp;Yuzhi Chen,&nbsp;Chen Wang","doi":"10.1016/j.trc.2024.104974","DOIUrl":"10.1016/j.trc.2024.104974","url":null,"abstract":"<div><div>Accurate trajectory prediction for multiple agents (i.e., vehicles, bicyclists, and pedestrians) is the premise of launching proactive interventions, which can serve as an effective way to improve traffic safety at unsignalized intersections. The distinctive characteristic of unsignalized intersections lies in their disorderly traffic organization, prompting traffic agents to be extra vigilant towards other agents to prevent collisions. As such, the primary focus of multi-agent trajectory prediction lies in acquiring a deep understanding of their interactive behavior patterns when encountering potential collisions. To achieve this, this study proposes an improved generative adversarial network (GAN) that can properly model collision avoidance behaviors of multiple agents when predicting their trajectories. Specifically, attention pooling modules are employed to capture interactions among multiple agents. A graph convolution network (GCN) based collision extraction module is applied to identify potential collisions and model the collision avoidance behaviors of traffic agents. Experimental results on <em>InD</em> dataset demonstrate that the proposed framework attained a more accurate and reliable performance for multi-agent trajectory prediction. In different interactive scenarios, such as when vehicles yield or don’t yield, the results illustrated via the Distance-velocity (DV) diagram display a significant level of robustness. Furthermore, the conflict points, count of dangerous interactions, and Post-Encroachment Time, as computed from these predicted trajectories, also align well with the ground truth. This indicates that the proposed framework effectively captures the pattern of collision avoidance behaviors of multiple agents, which has potential to serve as an effective way to enhance traffic safety at unsignalized intersections.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104974"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096022","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":"Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment","authors":"Mengyun Xu ,&nbsp;Jie Fang ,&nbsp;Prateek Bansal ,&nbsp;Eui-Jin Kim ,&nbsp;Tony Z. Qiu","doi":"10.1016/j.trc.2024.104985","DOIUrl":"10.1016/j.trc.2024.104985","url":null,"abstract":"<div><div>Inferring the complete traffic flow time–space diagram using vehicle trajectories provides a holistic perspective of traffic dynamics at intersections to traffic managers. However, obtaining all vehicle trajectories on the road is infeasible. To this end, a novel framework that combines the conditional deep generative model and physics-based car-following model is proposed to reconstruct all vehicle trajectories from sparsely available connected vehicle (CV) trajectories at the intersection. The proposed framework has two novel components: Arrival Generative Adversarial Network (Arrival-GAN) and Trajectory-GAN. The Arrival-GAN reproduces stochastic vehicle arrival patterns by considering the interaction between adjacent intersections (e.g., signal control scheme) and the interaction between multiple vehicles from historical vehicle trajectories, circumventing the conventionally adopted unrealistic assumptions of uniform vehicle arrivals. The Trajectory-GAN model takes the baseline trajectory deduced by the physics-based car-following model as prior information and refines it by dynamically adapting driving behavior in response to the varying traffic conditions in a data-driven manner. This hybrid approach leverages the advantages of data-driven (i.e., flexibility) and theory-driven approaches (i.e., interpretability) complementarily. The proposed framework outperforms conventional benchmark models in the simulated arterial network and the real-world datasets, reconstructing a complete time–space diagram at intersections with markedly enhanced accuracy, particularly in low-traffic-density scenarios. This study showcases the potential of utilizing CV data and physics-informed deep learning to improve our understanding of traffic dynamics, empowering traffic managers with novel insights for efficient intersection management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"171 ","pages":"Article 104985"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096047","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}