Data analytics and optimization for assessing a ride sharing system

Armant, Vincent; Horan, John; Mahbub, Nahid; Brown, Kenneth N.

Data analytics and optimization for assessing a ride sharing system

Files

ida15.pdf(2.38 MB)

Accepted Version

Date

2015-10

Authors

Armant, Vincent

Horan, John

Mahbub, Nahid

Brown, Kenneth N.

Publisher

Springer International Publishing AG

Published Version

10.1007/978-3-319-24465-5_1

Abstract

Ride-sharing schemes attempt to reduce road traffic by matching prospective passengers to drivers with spare seats in their cars. To be successful, such schemes require a critical mass of drivers and passengers. In current deployed implementations, the possible matches are based on heuristics, rather than real route times or distances. In some cases, the heuristics propose infeasible matches; in others, feasible matches are omitted. Poor ride matching is likely to deter participants from using the system. We develop a constraint-based model for acceptable ride matches which incorporates route plans and time windows. Through data analytics on a history of advertised schedules and agreed shared trips, we infer parameters for this model that account for 90% of agreed trips. By applying the inferred model to the advertised schedules, we demonstrate that there is an imbalance between riders and passengers. We assess the potential benefits of persuading existing drivers to switch to becoming passengers if appropriate matches can be found, by solving the inferred model with and without switching. We demonstrate that flexible participation has the potential to reduce the number of unmatched participants by up to 80%.

Keywords

Data analytics , Optimization , Ride sharing

Citation

ARMANT, V., HORAN, J., MAHBUB, N. & BROWN, K. N. 2015. Data Analytics and Optimisation for Assessing a Ride Sharing System. In: FROMONT, E., DE BIE, T. & VAN LEEUWEN, M. (eds.) Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Cham: Springer International Publishing.