Abstract
This paper considers real-time dispatching for large-scale ride-sharing services over a rolling horizon. It presents RTDARS which relies on a column-generation algorithm to minimize wait times while guaranteeing short travel times and service for each customer. Experiments using historic taxi trips in New York City for instances with up to 30,000 requests per hour indicate that the algorithm scales well and provides a principled and effective way to support large-scale ride-sharing services in dense cities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. 114(3), 462–467 (2017)
Bent, R., Van Hentenryck, P.: Waiting and relocation strategies in online stochastic vehicle routing. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 2007, pp. 1816–1821. Morgan Kaufmann Publishers Inc., San Francisco (2007). http://dl.acm.org/citation.cfm?id=1625275.1625569
Bent, R.W., Van Hentenryck, P.: Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6), 977–987 (2004)
Berbeglia, G., Cordeau, J.F., Laporte, G.: A hybrid tabu search and constraint programming algorithm for the dynamic dial-a-ride problem. INFORMS J. Comput. 24(3), 343–355 (2012)
Bertsimas, D., Jaillet, P., Martin, S.: Online vehicle routing: the edge of optimization in large-scale applications (2018)
Cordeau, J.F., Laporte, G.: The dial-a-ride problem: models and algorithms. Ann. Oper. Res. 153(1), 29–46 (2007)
Jain, S., Van Hentenryck, P.: Large neighborhood search for dial-a-ride problems. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 400–413. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_31
NYC: Nyc taxi & limousine commission - trip record data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
OpenStreetMap contributors: Planet dump (2017). https://planet.osm.org. https://www.openstreetmap.org
Ota, M., Vo, H., Silva, C., Freire, J.: A scalable approach for data-driven taxi ride-sharing simulation. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 888–897, October 2015
Ota, M., Vo, H., Silva, C., Freire, J.: Stars: simulating taxi ride sharing at scale. IEEE Trans. Big Data 3(3), 349–361 (2017)
RITMO app introduces on-demand mass transit at U-M, with plans to expand. Concentrate (2018). www.secondwavemedia.com/concentrate/innovationnews/ritmorollout0443.aspx
Acknowlegments
This research was partly supported by Didi Chuxing Technology Co. and Department of Energy Research Grant 7F-30154. We would like to thank the reviewers for their detailed comments and suggestions which dramatically improved the paper, and the program chairs for a rebuttal period that was long enough to run many experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Riley, C., Legrain, A., Van Hentenryck, P. (2019). Column Generation for Real-Time Ride-Sharing Operations. In: Rousseau, LM., Stergiou, K. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2019. Lecture Notes in Computer Science(), vol 11494. Springer, Cham. https://doi.org/10.1007/978-3-030-19212-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-19212-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19211-2
Online ISBN: 978-3-030-19212-9
eBook Packages: Computer ScienceComputer Science (R0)