{"title":"A Kriging-based optimization method for meeting point locations to enhance flex-route transit services","authors":"Mingyang Li, Jinjun Tang, J. Zeng, Helai Huang","doi":"10.1080/21680566.2023.2195984","DOIUrl":null,"url":null,"abstract":"As a promising on-demand transportation mode in low-demand areas, flex-route transit, has attracted much attention in the transportation research field. However, unexpectedly high demand levels caused by travel uncertainty impact the reliability and development of flex-route transit services. Although the meeting point strategy can deal with this problem effectively, selecting a location for the meeting points can substantially influence the performance of this strategy. In this study, meeting point location selection is modeled as a simulation-based optimization (SO) problem, and a Kriging-based global optimization method using a Pareto-based multipoint sampling strategy (KGO-PS) is proposed to solve this problem. Through comparison of several typical benchmark functions with other counterparts, the effectiveness of KGO-PS has been verified. Moreover, a real-life flex-route transit service is employed to construct the SO problem, and the optimization results show that the proposed algorithm can improve the performance of flex-route transit services under unexpectedly high demand levels.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportmetrica B-Transport Dynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/21680566.2023.2195984","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
As a promising on-demand transportation mode in low-demand areas, flex-route transit, has attracted much attention in the transportation research field. However, unexpectedly high demand levels caused by travel uncertainty impact the reliability and development of flex-route transit services. Although the meeting point strategy can deal with this problem effectively, selecting a location for the meeting points can substantially influence the performance of this strategy. In this study, meeting point location selection is modeled as a simulation-based optimization (SO) problem, and a Kriging-based global optimization method using a Pareto-based multipoint sampling strategy (KGO-PS) is proposed to solve this problem. Through comparison of several typical benchmark functions with other counterparts, the effectiveness of KGO-PS has been verified. Moreover, a real-life flex-route transit service is employed to construct the SO problem, and the optimization results show that the proposed algorithm can improve the performance of flex-route transit services under unexpectedly high demand levels.
期刊介绍:
Transportmetrica B is an international journal that aims to bring together contributions of advanced research in understanding and practical experience in handling the dynamic aspects of transport systems and behavior, and hence the sub-title is set as “Transport Dynamics”.
Transport dynamics can be considered from various scales and scopes ranging from dynamics in traffic flow, travel behavior (e.g. learning process), logistics, transport policy, to traffic control. Thus, the journal welcomes research papers that address transport dynamics from a broad perspective, ranging from theoretical studies to empirical analysis of transport systems or behavior based on actual data.
The scope of Transportmetrica B includes, but is not limited to, the following: dynamic traffic assignment, dynamic transit assignment, dynamic activity-based modeling, applications of system dynamics in transport planning, logistics planning and optimization, traffic flow analysis, dynamic programming in transport modeling and optimization, traffic control, land-use and transport dynamics, day-to-day learning process (model and behavioral studies), time-series analysis of transport data and demand, traffic emission modeling, time-dependent transport policy analysis, transportation network reliability and vulnerability, simulation of traffic system and travel behavior, longitudinal analysis of traveler behavior, etc.