{"title":"为城市日常通勤的 \"毛细血管 \"赋能:基于储物柜的电动自行车电池更换的电池部署分析","authors":"Xiaolei Xie, Xu Dai, Zhi Pei","doi":"10.1287/trsc.2022.0132","DOIUrl":null,"url":null,"abstract":"In densely populated Asian countries, e-bikes have become a new supernova in daily urban transportation. To facilitate the operations of e-bike-based mobility, the present paper studies the management of the battery deployment for the e-bike battery-swapping system, where the unique features of e-bike riding are considered. Given the pedal-assisted mode, e-bike users could abandon waiting and return to the station later on without too much range anxiety. However, because of the time-varying nature of the customer arrival and the complicated user behaviors, the battery quantity at each station is altered to guarantee the designated service level. However, little research has been done on the operations management of the e-bike battery-swapping system. To bridge the gap, we propose a nonstationary queueing network model to characterize the customer behaviors during the battery-swapping service. Then we develop a closed-form delayed infinite-server fluid approximation for the battery deployment of the one-time-loop scenario under various quality-of-service targets. In addition, we handle the infinite-time-loop scenario with the simulation-based iterative staffing algorithm. In the simulation study, we observe that the proposed battery deployment algorithms can help stabilize the system performance in terms of abandonment probability and expected delay in the face of time-varying demand and complex customer behaviors. Moreover, we reveal that the number of return loops correlates with the service level targets on the battery deployment decision. Furthermore, a time gap exists between the demand and the optimal battery deployment, making proactive battery management in the system possible.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271222, 71871203, 71872093, 72271137, L1924063], and the National Social Science Fund of China [Grant 21&ZD128].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0132 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"67 1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Empowering the Capillary of the Urban Daily Commute: Battery Deployment Analysis for the Locker-Based E-bike Battery Swapping\",\"authors\":\"Xiaolei Xie, Xu Dai, Zhi Pei\",\"doi\":\"10.1287/trsc.2022.0132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In densely populated Asian countries, e-bikes have become a new supernova in daily urban transportation. To facilitate the operations of e-bike-based mobility, the present paper studies the management of the battery deployment for the e-bike battery-swapping system, where the unique features of e-bike riding are considered. Given the pedal-assisted mode, e-bike users could abandon waiting and return to the station later on without too much range anxiety. However, because of the time-varying nature of the customer arrival and the complicated user behaviors, the battery quantity at each station is altered to guarantee the designated service level. However, little research has been done on the operations management of the e-bike battery-swapping system. To bridge the gap, we propose a nonstationary queueing network model to characterize the customer behaviors during the battery-swapping service. Then we develop a closed-form delayed infinite-server fluid approximation for the battery deployment of the one-time-loop scenario under various quality-of-service targets. In addition, we handle the infinite-time-loop scenario with the simulation-based iterative staffing algorithm. In the simulation study, we observe that the proposed battery deployment algorithms can help stabilize the system performance in terms of abandonment probability and expected delay in the face of time-varying demand and complex customer behaviors. Moreover, we reveal that the number of return loops correlates with the service level targets on the battery deployment decision. Furthermore, a time gap exists between the demand and the optimal battery deployment, making proactive battery management in the system possible.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271222, 71871203, 71872093, 72271137, L1924063], and the National Social Science Fund of China [Grant 21&ZD128].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0132 .\",\"PeriodicalId\":51202,\"journal\":{\"name\":\"Transportation Science\",\"volume\":\"67 1\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1287/trsc.2022.0132\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1287/trsc.2022.0132","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Empowering the Capillary of the Urban Daily Commute: Battery Deployment Analysis for the Locker-Based E-bike Battery Swapping
In densely populated Asian countries, e-bikes have become a new supernova in daily urban transportation. To facilitate the operations of e-bike-based mobility, the present paper studies the management of the battery deployment for the e-bike battery-swapping system, where the unique features of e-bike riding are considered. Given the pedal-assisted mode, e-bike users could abandon waiting and return to the station later on without too much range anxiety. However, because of the time-varying nature of the customer arrival and the complicated user behaviors, the battery quantity at each station is altered to guarantee the designated service level. However, little research has been done on the operations management of the e-bike battery-swapping system. To bridge the gap, we propose a nonstationary queueing network model to characterize the customer behaviors during the battery-swapping service. Then we develop a closed-form delayed infinite-server fluid approximation for the battery deployment of the one-time-loop scenario under various quality-of-service targets. In addition, we handle the infinite-time-loop scenario with the simulation-based iterative staffing algorithm. In the simulation study, we observe that the proposed battery deployment algorithms can help stabilize the system performance in terms of abandonment probability and expected delay in the face of time-varying demand and complex customer behaviors. Moreover, we reveal that the number of return loops correlates with the service level targets on the battery deployment decision. Furthermore, a time gap exists between the demand and the optimal battery deployment, making proactive battery management in the system possible.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271222, 71871203, 71872093, 72271137, L1924063], and the National Social Science Fund of China [Grant 21&ZD128].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0132 .
期刊介绍:
Transportation Science, published quarterly by INFORMS, is the flagship journal of the Transportation Science and Logistics Society of INFORMS. As the foremost scientific journal in the cross-disciplinary operational research field of transportation analysis, Transportation Science publishes high-quality original contributions and surveys on phenomena associated with all modes of transportation, present and prospective, including mainly all levels of planning, design, economic, operational, and social aspects. Transportation Science focuses primarily on fundamental theories, coupled with observational and experimental studies of transportation and logistics phenomena and processes, mathematical models, advanced methodologies and novel applications in transportation and logistics systems analysis, planning and design. The journal covers a broad range of topics that include vehicular and human traffic flow theories, models and their application to traffic operations and management, strategic, tactical, and operational planning of transportation and logistics systems; performance analysis methods and system design and optimization; theories and analysis methods for network and spatial activity interaction, equilibrium and dynamics; economics of transportation system supply and evaluation; methodologies for analysis of transportation user behavior and the demand for transportation and logistics services.
Transportation Science is international in scope, with editors from nations around the globe. The editorial board reflects the diverse interdisciplinary interests of the transportation science and logistics community, with members that hold primary affiliations in engineering (civil, industrial, and aeronautical), physics, economics, applied mathematics, and business.