Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2019
Abstract
Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) paths – where each zone path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone paths to optimize the objective. The key advantage of our approach is that these zone paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Transportation
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling: Berkeley, CA, July 11-15
First Page
528
Last Page
538
Publisher
AAAI Press
City or Country
Menlo Park, CA
Citation
LOWALEKAR, Meghna; VARAKANTHAM, Pradeep; and JAILLET, Patrick.
ZAC: A Zone pAth Construction approach for effective real-time ridesharing. (2019). Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling: Berkeley, CA, July 11-15. 528-538.
Available at: https://ink.library.smu.edu.sg/sis_research/5110
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.aaai.org/ojs/index.php/ICAPS/article/view/3519
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Transportation Commons