{"title":"Activity-Based Shared Mobility Model for Smart Transportation","authors":"San Yeung, H. M. A. Aziz, S. Madria","doi":"10.1109/MDM.2019.00126","DOIUrl":null,"url":null,"abstract":"The shared mobility model of transportation services in cities has gained significant attention due to the proliferation of on-demand ride-sharing applications and the advancement of autonomous driving technologies. In this paper, a new shared mobility model is proposed accommodating the activity attribute of users' trip requests. Our key goal is to determine the minimum fleet size required to satisfy all on-demand requests while minimizing the total travel costs. Since this is an NP-hard problem, the model leverages a set of novel heuristic-based components including the clustering-based formation of ride-sharing groups, carpool-like schedule and ridesharing schedule generation, and clique-based trip integration. All work together to obtain the set of energy-efficient shared route schedules. The proposed model can also be extended for a heterogeneous vehicle fleet configuration (e.g. vehicles of various capacity and functionality) to work for different types of trip activities.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDM.2019.00126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The shared mobility model of transportation services in cities has gained significant attention due to the proliferation of on-demand ride-sharing applications and the advancement of autonomous driving technologies. In this paper, a new shared mobility model is proposed accommodating the activity attribute of users' trip requests. Our key goal is to determine the minimum fleet size required to satisfy all on-demand requests while minimizing the total travel costs. Since this is an NP-hard problem, the model leverages a set of novel heuristic-based components including the clustering-based formation of ride-sharing groups, carpool-like schedule and ridesharing schedule generation, and clique-based trip integration. All work together to obtain the set of energy-efficient shared route schedules. The proposed model can also be extended for a heterogeneous vehicle fleet configuration (e.g. vehicles of various capacity and functionality) to work for different types of trip activities.