Directionality-centric bus transit network segmentation for on-demand public transit

作     者：Perera, Thilina Wijesundera, Deshya Wijerathna, Lahiru Srikanthan, Thambipillai 

作者机构：Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore Singapore Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa Sri Lanka 

出 版 物：《IET INTELLIGENT TRANSPORT SYSTEMS》 (IET Intel. Transport Syst.)

年 卷 期：2020年第14卷第13期

页      面：1871-1881页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 

基　　金：National Research Foundation, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme Technical University of Munich at TUMCREATE 

主　　题：road vehicles road traffic resource allocation minimisation vehicle routing on-demand public transit ODPT systems vehicle fleet flexible routes fleet travel distance heuristic routing algorithms route computation time problem complexity network segmentation techniques parallel computing route quality road network distances directionality-centric bus transit network segmentation technique parallel computation dynamic fleet allocation algorithm fleet utilisation flexible route computation high-capacity ride-sharing 

摘      要：The recent growth in real-time, high-capacity ride-sharing has made on-demand public transit (ODPT) a reality. ODPT systems serving passengers using a vehicle fleet that operates with flexible routes, strive to minimise fleet travel distance. Heuristic routing algorithms have been integrated in ODPT systems in order to improve responsiveness. However, route computation time in such algorithms depends on problem complexity and hence increases for large scale problems. Thus, network segmentation techniques that exploit parallel computing have been proposed in order to reduce route computation time. Even though computation time can be reduced using segmentation in existing techniques, it comes at the cost of degradation of route quality due to static demarcation of boundaries and disregarding real road network distances. Thus, this work proposes, a directionality-centric bus transit network segmentation technique that exploits parallel computation capable of computing routes in near real-time while providing high scalability. Additionally, a dynamic fleet allocation algorithm that exploits proximity and flexibility to minimise vehicle detours while maximising fleet utilisation is proposed. Experimental evaluations on a real road network confirm that the proposed method achieves notable speed-up in flexible route computation without compromising route quality compared to a widely used unsupervised learning technique.