Abstract
Shared autonomous mobility-on-demand systems hold great promise for improving the efficiency of urban transportation, but are challenging to implement due to the huge scheduling search space and highly dynamic nature of requests. This paper presents a novel optimal schedule pool (OSP) assignment approach to optimally dispatch high-capacity ride-sharing vehicles in real time, including: (1) an incremental search algorithm that can efficiently compute the exact lowest-cost schedule of a ride-sharing trip with a reduced search space; (2) an iterative online re-optimization strategy to dynamically alter the assignment policy for new incoming requests, in order to maximize the service rate. Experimental results based on New York City taxi data show that our proposed approach outperforms the state-of-the-art in terms of service rate and system scalability.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Publisher | IEEE |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-7281-9077-8 |
| ISBN (Print) | 978-1-7281-9078-5 |
| DOIs | |
| Publication status | Published - 18 Oct 2021 |
| Event | 2021 IEEE International Conference on Robotics and Automation (ICRA) - International Convention and Exhibition Center, Xi’an , China Duration: 30 May 2021 → 5 Jun 2021 |
Publication series
| Name | IEEE International Conference on Robotics and Automation (ICRA) |
|---|---|
| Publisher | IEEE |
Conference
| Conference | 2021 IEEE International Conference on Robotics and Automation (ICRA) |
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| Abbreviated title | ICRA 2021 |
| Country/Territory | China |
| City | Xi’an |
| Period | 30/05/21 → 5/06/21 |
Keywords
- space vehicles
- shedules
- scalability
- urban areas
- optimal scheduling
- dynamic scheduling
- real time systems
Access to Document
- LiC2021Optimal
This is the Author Accepted Manuscript of the following proceeding Cheng Li; David Parker; Qi Hao, Optimal Online Dispatch for High-Capacity Shared Autonomous Mobility-on-Demand Systems 202110.1109/ICRA48506.2021.9561281 © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.