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Data analytics and optimisation for assessing a ride sharing system

Authors: 

Vincent Armant, John Horan, Nahid Mabub, Ken Brown

Publication Type: 
Refereed Conference Meeting Proceeding
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\%.
Conference Name: 
Intelligent Data Analytics
Proceedings: 
The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015)
Digital Object Identifer (DOI): 
(to appear)
Publication Date: 
22/10/2015
Conference Location: 
France
Institution: 
National University of Ireland, Cork (UCC)
Project Acknowledges: 
Open access repository: 
No
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