Transportation Research Part C-Emerging Technologies最新文献

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Using probabilistic clustering techniques as a specification tool for capturing heterogeneity in choice models 使用概率聚类技术作为捕获选择模型异质性的规范工具
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-17 DOI: 10.1016/j.trc.2025.105289
Panagiotis Tsoleridis, Charisma F. Choudhury, Stephane Hess
{"title":"Using probabilistic clustering techniques as a specification tool for capturing heterogeneity in choice models","authors":"Panagiotis Tsoleridis,&nbsp;Charisma F. Choudhury,&nbsp;Stephane Hess","doi":"10.1016/j.trc.2025.105289","DOIUrl":"10.1016/j.trc.2025.105289","url":null,"abstract":"<div><div>In the era of big data, data-driven methods have emerged as strong competitors to traditional econometric models for analysing choice behaviour. In particular, data-driven models offer flexible classification methods that are well-suited to capturing the heterogeneity among decision makers and improving model fit. A key limitation of the purely data-driven models, however, is the difficulty in the calculation of welfare measures, such as the value of travel time estimates (VTT) that are essential for cost–benefit analyses. This motivates the current study which focuses on combining data mining based segmentation approaches used in ML with traditional discrete choice models (DCM) to get the best of both - a clustering-based component to capture the heterogeneity among the travellers and a utility-based choice component that is suitable for quantifying policy-relevant measures, such as VTT estimates. In the proposed hybrid framework, travellers are probabilistically allocated into clusters based on their degree of similarity from each cluster and cluster-specific random-utility-based mode choice models are estimated simultaneously. The proposed hybrid framework is tested on 2 RP datasets (a GPS diary and a traditional household survey) and on 3 different choice contexts, providing a range of different sample sizes and data complexity. The performance of the proposed hybrid model (H-LCCM) is compared with that of the traditional latent class choice models (LCCM), where both the class membership and mode choice components are based on utility-based frameworks and two other state-of-the-art ML-assisted LCCM frameworks. Results indicate that H-LCCM outperforms the remaining specifications in the majority of the contexts examined, while offering a more scalable approach for contexts with a large number of observations (which is the case for big data sources) and/or with large choice sets (which is typical in spatial choice contexts). The proposed framework is practically applicable for policy-making as it allows the calculation of VTT estimates, therefore not sacrificing the microeconomic interpretability of traditional DCMs. The results are promising, especially in the current era of big data and are expected to contribute to the emerging literature looking at cross-synergies between traditional econometric approaches and new data-driven methods.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105289"},"PeriodicalIF":7.6,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858217","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}
引用次数: 0
A traffic control strategy for freeway merging zones cooperating safety and efficiency in the intelligent connected environment of mixed vehicles 混合动力汽车智能互联环境下高速公路合流区安全高效协同交通控制策略
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-12 DOI: 10.1016/j.trc.2025.105298
Lang Zhang, Heng Ding, Zeyang Cheng, Xiaoyan Zheng, Weihua Zhang
{"title":"A traffic control strategy for freeway merging zones cooperating safety and efficiency in the intelligent connected environment of mixed vehicles","authors":"Lang Zhang,&nbsp;Heng Ding,&nbsp;Zeyang Cheng,&nbsp;Xiaoyan Zheng,&nbsp;Weihua Zhang","doi":"10.1016/j.trc.2025.105298","DOIUrl":"10.1016/j.trc.2025.105298","url":null,"abstract":"<div><div>The manner and intensity of vehicle interactions in a mixed-vehicle traffic flow differ from those in a typical traffic flow. This difference leads to greater potential conflicts and decreased efficiency in freeway merging zones, which involve a large amount of vehicle crossing behaviour. To avoid the deterioration of traffic status, cooperative control of safety and efficiency for mixed-vehicle traffic flow using connected and automated vehicles (CAVs) in freeway merging zones is proposed. First, a multi-objective nonlinear mixed-integer program model for cooperative safety and efficiency is presented at the vehicle level to optimize CAV’s behavioural decisions using historical predicted data. Second, a Transformer neural network is adopted to forecast the traffic state under different control weights, accounting for the dynamic characteristics of the traffic system. An adaptive weighting model is constructed to choose the optimal solution from the Pareto frontier derived from the multi-objective problem. To ensure the feasibility of vehicle-level decisions and to facilitate system-level optimization, CAVs are capable of sharing and coordinating their behaviour decisions through iterations. A typical scenario involving a two-lane freeway merging area is analysed, and the results show that the cooperative control strategy can effectively optimize the traffic state. Even at 20% CAV penetration rates, this strategy reduces total parking delays by 48.7% and time-integrated time-to-collision (TIT) by 72.2%.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105298"},"PeriodicalIF":7.6,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828218","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}
引用次数: 0
Urban last-mile delivery services with time windows and parcel lockers: Model formulation and branch-and-price algorithm 具有时间窗和包裹寄存柜的城市最后一英里快递服务:模型制定和分支价格算法
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-10 DOI: 10.1016/j.trc.2025.105280
Xiaoning Zang , Li Jiang , Qiang Meng
{"title":"Urban last-mile delivery services with time windows and parcel lockers: Model formulation and branch-and-price algorithm","authors":"Xiaoning Zang ,&nbsp;Li Jiang ,&nbsp;Qiang Meng","doi":"10.1016/j.trc.2025.105280","DOIUrl":"10.1016/j.trc.2025.105280","url":null,"abstract":"<div><div>Parcel lockers contribute to reducing delivery time and costs, providing logistics companies with solutions to address the challenges of last-mile delivery services. In this paper, we propose a routing problem with parcel lockers that incorporates a compensation-oriented reward strategy. The problem assumes that logistics companies can influence customer preferences through reward incentives, thereby obtaining flexible delivery solutions. To formulate this problem, we proposed a mixed-integer linear programming model, followed by a set-partitioning model. To solve the latter model, we developed a branch-and-price algorithm, whose subproblem is a variant of the elementary shortest path problem with resource constraints. This subproblem requires making decisions about node selection, allocation, and sequencing. We also presented a tailored bidirectional labeling algorithm with new label extension and dominance rules to solve the subproblem. Finally, we generated two types of instances derived from benchmark instances and conducted extensive experiments based on the instances to evaluate the performance of the BP algorithm.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105280"},"PeriodicalIF":7.6,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809469","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}
引用次数: 0
A sector-specific probabilistic approach for 4D aircraft trajectory generation 四维飞机轨迹生成的特定扇区概率方法
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-06 DOI: 10.1016/j.trc.2025.105291
Nick Pepper , George De Ath , Ben Carvell , Amy Hodgkin , Tim Dodwell , Marc Thomas , Richard Everson
{"title":"A sector-specific probabilistic approach for 4D aircraft trajectory generation","authors":"Nick Pepper ,&nbsp;George De Ath ,&nbsp;Ben Carvell ,&nbsp;Amy Hodgkin ,&nbsp;Tim Dodwell ,&nbsp;Marc Thomas ,&nbsp;Richard Everson","doi":"10.1016/j.trc.2025.105291","DOIUrl":"10.1016/j.trc.2025.105291","url":null,"abstract":"<div><div>Generating realistic four-dimensional trajectories is a fundamental challenge in air traffic control (ATC) that is relevant to both operational tasks and to effective simulation of airspace for the purposes of training controllers and designing new airspaces and/or procedures. Traditional trajectory generation methods are deterministic and use physics-based models with well-calibrated physical parameters and speed schedules. However, these models require knowledge of the clearances issued to aircraft in order to produce a full trajectory. Tools which can marginalise over these clearances to generate a four-dimensional trajectory are valuable in simulations as they emulate the behaviour of human controllers in background sectors, while also reflecting the level of uncertainty present in the system. Consequentially, this work proposes a probabilistic method for generating 4D aircraft trajectories that are specific to a sector of airspace, incorporating multiple routes and allowing local procedures such as co-ordinated entry and exit points to be modelled. The proposed model couples a model for generating plausible aircraft ground tracks with data-driven climb and descent models specific to an aircraft’s (ICAO) wake turbulence category. A simple algorithm combines the lateral and vertical trajectories together to produce a four-dimensional (4D) trajectory. A busy sector in the United Kingdom’s upper airspace was the focus of the study, which used a dataset comprising one month of aircraft surveillance data. It was found that the proposed model offered improved modelling of aircraft performance and the lateral path followed by aircraft compared to existing, deterministic methods of trajectory generation.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105291"},"PeriodicalIF":7.6,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780130","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}
引用次数: 0
Mitigating metropolitan carbon emissions with dynamic eco-driving at scale 通过大规模动态生态驾驶减少都市碳排放
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-06 DOI: 10.1016/j.trc.2025.105146
Vindula Jayawardana , Baptiste Freydt , Ao Qu , Cameron Hickert , Edgar Sanchez , Catherine Tang , Mark Taylor , Blaine Leonard , Cathy Wu
{"title":"Mitigating metropolitan carbon emissions with dynamic eco-driving at scale","authors":"Vindula Jayawardana ,&nbsp;Baptiste Freydt ,&nbsp;Ao Qu ,&nbsp;Cameron Hickert ,&nbsp;Edgar Sanchez ,&nbsp;Catherine Tang ,&nbsp;Mark Taylor ,&nbsp;Blaine Leonard ,&nbsp;Cathy Wu","doi":"10.1016/j.trc.2025.105146","DOIUrl":"10.1016/j.trc.2025.105146","url":null,"abstract":"<div><div>The sheer scale and diversity of transportation make it a formidable sector to decarbonize. Here, we consider an emerging opportunity to reduce carbon emissions: the growing adoption of semi-autonomous vehicles, which can be programmed to mitigate stop-and-go traffic through intelligent speed commands and, thus, reduce emissions. But would such <em>dynamic eco-driving</em> move the needle on climate change? A comprehensive impact analysis has been out of reach due to the vast array of traffic scenarios and the complexity of vehicle emissions. Such an analysis would require careful modeling of many traffic scenarios and solving an eco-driving problem at each one of them - a challenge that has been out of reach for previous studies. We address this challenge with large-scale scenario modeling efforts and by using multi-task deep reinforcement learning with a carefully designed network decomposition strategy. We perform an in-depth prospective impact assessment of dynamic eco-driving at 6,011 signalized intersections across three major US metropolitan cities, simulating a million traffic scenarios. Overall, we find that vehicle trajectories optimized for emissions can cut city-wide intersection carbon emissions by 11%–22%, without harming throughput or safety, and with reasonable assumptions, equivalent to the national emissions of Israel and Nigeria, respectively. We find that 10% eco-driving adoption yields 25%–50% of total reduction, and nearly 70% of the benefits come from 20% of intersections, suggesting near-term implementation pathways. However, the composition of this high-impact subset of intersections varies considerably across different adoption levels, with minimal overlap, calling for careful strategic planning for eco-driving deployments. Moreover, the impact of eco-driving, when considered jointly with projections of vehicle electrification, hybrid vehicle adoption, and travel growth, remains significant. More broadly, this work paves the way for large-scale analysis of traffic externalities, such as time, safety, and air quality, and the potential impact of solution strategies. Visual details can be found on the project page <span><span>https://vindulamj.github.io/eco-drive</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105146"},"PeriodicalIF":7.6,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780128","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}
引用次数: 0
Estimating the value of safety against road crashes: A stated preference experiment on route choice of food delivery riders 估计道路交通事故的安全价值:一个关于外卖骑手路线选择的陈述偏好实验
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-06 DOI: 10.1016/j.trc.2025.105272
Kuldeep Kavta , Shadi Sharif Azadeh , Yousef Maknoon , Yihong Wang , Gonçalo Homem de Almeida Correia
{"title":"Estimating the value of safety against road crashes: A stated preference experiment on route choice of food delivery riders","authors":"Kuldeep Kavta ,&nbsp;Shadi Sharif Azadeh ,&nbsp;Yousef Maknoon ,&nbsp;Yihong Wang ,&nbsp;Gonçalo Homem de Almeida Correia","doi":"10.1016/j.trc.2025.105272","DOIUrl":"10.1016/j.trc.2025.105272","url":null,"abstract":"<div><div>The rapid growth of the online food delivery industry has led to a significant increase in the number of delivery riders navigating urban streets, predominantly using bikes and e-bikes. This growth has been accompanied by a concerning rise in crashes involving these riders, posing a critical challenge for city authorities and policymakers. Promoting safer riding behavior, such as choosing safer routes while delivering food, can potentially reduce crash risks. With this motivation, this paper aims to evaluate the effectiveness of strategies that encourage riders to choose safer routes and estimate the value riders place on reducing the risk of road crashes. The paper presents a stated preference experiment conducted with food delivery riders in Amsterdam and Copenhagen to assess two targeted strategies: ’safety information’ and ’monetary incentives’, designed to encourage riders toward selecting safer routes. The results from the route choice model show that presenting information about safety against crashes on different routes and offering monetary incentives can effectively motivate riders to choose safer routes, even if these are longer. The trade-offs riders make between safer and shorter routes were quantified by calculating the Value of Risk Reduction (VRR) and Willingness to Accept (WTA) indicators, which offer valuable insights into riders’ safety preferences. These indicators highlight how much riders value risk reduction and the compensation required to choose safer routes. Furthermore, the findings reveal that factors related to riders’ working arrangements and socio-demographic profiles significantly influence their route choice decisions. The paper concludes with a discussion about the practical challenges associated with implementing the strategies to enhance rider safety and proposing potential solutions that can be useful for food delivery platforms and policymakers.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105272"},"PeriodicalIF":7.6,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780129","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}
引用次数: 0
Modeling car-following behaviors considering driver heterogeneity: A multi-regime stochastic framework 考虑驾驶员异质性的汽车跟随行为建模:一个多区域随机框架
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-05 DOI: 10.1016/j.trc.2025.105282
Shubo Wu , Dong Ngoduy , Zhengbing He , Yajie Zou , Jian Sun
{"title":"Modeling car-following behaviors considering driver heterogeneity: A multi-regime stochastic framework","authors":"Shubo Wu ,&nbsp;Dong Ngoduy ,&nbsp;Zhengbing He ,&nbsp;Yajie Zou ,&nbsp;Jian Sun","doi":"10.1016/j.trc.2025.105282","DOIUrl":"10.1016/j.trc.2025.105282","url":null,"abstract":"<div><div>Car-following behavior exhibits stochastic characteristics influenced by the inherent randomness of human drivers. Stochastic models have been extensively developed to capture the probabilistic nature of car-following behavior dynamics. However, the time-varying nature driven by driver heterogeneity has not been adequately studied. To this end, this paper proposes a stochastic modeling framework that incorporates multi-regime car-following models with a Bayesian calibration approach to account for the driver heterogeneity in human car-following behaviors. More specifically, our framework employs a K-means clustering algorithm to categorize human drivers into three driving styles and leverages a hierarchical Dirichlet process-hidden semi-Markov model (HDP-HSMM) to segment car-following sequences into diverse driving regimes, thereby capturing driver heterogeneity. According to the segmented driving regimes, three distinct hierarchies of multi-regime Bayesian intelligent driver models (denoted pooled, hierarchical, and unpooled B-IDM) are developed to capture the time-varying nature of car-following behaviors across diverse driving regimes. These models are well-calibrated using a Bayesian approach with car-following trajectory data extracted from the Waymo open motion dataset and Lyft level-5 dataset. Deterministic and stochastic simulations are performed to evaluate the effectiveness of the proposed stochastic modeling framework. The experimental results demonstrate significant differences in car-following behaviors across various driving styles and driving regimes. The proposed framework effectively represents these heterogeneous car-following behaviors through the developed multi-regime hierarchical B-IDM with time-varying parameters. Additionally, the stochastic simulation achieves a more accurate representation than the deterministic simulation in replicating the dynamics of human car-following behaviors.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105282"},"PeriodicalIF":7.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771531","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}
引用次数: 0
Crash event detection using acoustic conformer 使用声学共形器检测碰撞事件
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-05 DOI: 10.1016/j.trc.2025.105275
Zubayer Islam, Mohamed Abdel-Aty
{"title":"Crash event detection using acoustic conformer","authors":"Zubayer Islam,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.trc.2025.105275","DOIUrl":"10.1016/j.trc.2025.105275","url":null,"abstract":"<div><div>Crash events identification and prediction plays a vital role in understanding safety conditions for transportation systems. While existing systems use traffic parameters correlated with crash data to classify and train these models, we propose the use of a novel sensory unit that can also accurately identify crash events: microphone. Audio events can be collected and analyzed to classify events such as crash. In this paper, we have demonstrated the use of an Acoustic Conformer, a convolution augmented transformer, for road event classification. The conformer is able to apprehend global features with a transformer while local features are captured by the Convolution module. Important audio parameters such as Mel Frequency Cepstral Coefficients (MFCC), log Mel-filterbank energy spectrum and Fourier Spectrum were used as feature set. Additionally, the dataset was augmented with more sample data by the use of audio augmentation techniques such as time and pitch shifting. Together with the feature extraction this data augmentation can achieve reasonable accuracy. Four events such as crash, tire skid, horn and siren sounds can be accurately identified giving indication of a road hazard that can be useful for traffic operators or paramedic. The proposed methodology can reach 83% f1-score with a recall of 85%.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105275"},"PeriodicalIF":7.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771530","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}
引用次数: 0
Adaptive and flexible rail transit network service dispatching as a partially observable Markov decision process 作为部分可观察马尔可夫决策过程的自适应柔性轨道交通网络服务调度
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-05 DOI: 10.1016/j.trc.2025.105286
Shou-yi Wang , Andy H.F. Chow , Cheng-shuo Ying
{"title":"Adaptive and flexible rail transit network service dispatching as a partially observable Markov decision process","authors":"Shou-yi Wang ,&nbsp;Andy H.F. Chow ,&nbsp;Cheng-shuo Ying","doi":"10.1016/j.trc.2025.105286","DOIUrl":"10.1016/j.trc.2025.105286","url":null,"abstract":"<div><div>This paper presents a novel adaptive train scheduling framework with flexible fleet sizes for routing and scheduling in network-wide rail transit services. This framework aims to minimize both passenger waiting times and operating costs driven by prevailing passenger demand. The train scheduling problem is formulated as a partially observable Markov decision process (POMDP) to reflect the practicality in training and real-world applications. To address the computational challenges associated with the train scheduling problem, deep reinforcement learning techniques are applied to seek potential optimal solutions to the optimization problem. The proposed train scheduling framework is tested using real-world scenarios and the data collected from the Hong Kong Light Rail Transit (LRT) network. The experiment results demonstrate that the proposed train scheduling framework using flexible fleet sizes can effectively reduce passenger waiting time and operating costs. This study contributes to the real-time routing and scheduling of network-wide rail transit services by advanced optimization technology.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105286"},"PeriodicalIF":7.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771527","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}
引用次数: 0
A multi-task Transformer with mixture-of-experts for personalized periodic predictions of individual travel behavior in multimodal public transport 多模式公共交通中个人出行行为个性化周期预测的多任务混合专家变压器
IF 7.6 1区 工程技术
Transportation Research Part C-Emerging Technologies Pub Date : 2025-08-05 DOI: 10.1016/j.trc.2025.105287
Haoning Xi , Zhiqi Shao , David A. Hensher , John D. Nelson , Huaming Chen , Kasun Wijayaratna
{"title":"A multi-task Transformer with mixture-of-experts for personalized periodic predictions of individual travel behavior in multimodal public transport","authors":"Haoning Xi ,&nbsp;Zhiqi Shao ,&nbsp;David A. Hensher ,&nbsp;John D. Nelson ,&nbsp;Huaming Chen ,&nbsp;Kasun Wijayaratna","doi":"10.1016/j.trc.2025.105287","DOIUrl":"10.1016/j.trc.2025.105287","url":null,"abstract":"<div><div>Integrated multimodal public transport (PT) systems are reshaping urban mobility by providing personalized travel experiences tailored to individual users. A critical challenge in realizing personalized mobility is predicting users’ periodic travel behaviors to capture each user’s evolving travel preferences and patterns. Big data and AI have opened new opportunities to accurately predict individual travel behavior, which is a critical initial step toward effective planning of personalized mobility bundle subscriptions and improvement of mobility services. This study proposes a novel framework, <span>PTBformer-MMoE</span>, for personalized periodic prediction of individual travel behavior, specifically predicting each user’s monthly mode-specific travel frequency class (classification tasks) and each user’s monthly expected travel fare (regression task), using the user’s most recent travel records. Within the multi-gate mixture-of-experts (MMoE) framework, each expert network is realized by a <span>PTBformer</span>, and each gate determines the weighted contributions of expert outputs relevant to a specific task tower. The <span>PTBformer</span> integrates two key modules, i.e., a Multi-mode Transformer employing multi-feature self-attention for continuous time-series travel data; and an OD Transformer capturing OD-specific travel features with multi-OD self-attention. Evaluated on a multimodal (bus, rail, ferry, and tram) dataset with over 0.96 billion travel records of 1.58 million users in Queensland, Australia, during 01/2021<span><math><mo>−</mo></math></span>01/2023, the proposed <span>PTBformer-MMoE</span> demonstrates state-of-the-art performance in predicting each user’s monthly mode-specific travel frequency class and monthly expected travel fare compared to 9 baseline models, setting a new benchmark for individual travel behavior predictions. The predictive capabilities of <span>PTBformer-MMoE</span> demonstrate its significant potential for real-world applications such as personalized mobility subscriptions, targeted recommendations, and optimized demand management, ultimately paving the way toward data-driven and user-centric multimodal PT systems.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"179 ","pages":"Article 105287"},"PeriodicalIF":7.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771528","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}
引用次数: 0
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