CommuteShare: A Ridesharing Service for Daily Commuters Using Cross-Domain Urban Big Data

Abstract

Existing ridesharing services have focused on on-demand trip matching, which resembles traditional taxi dispatching. This may encourage more private vehicles on the road, which aggravate traffic congestions in peak hours rather than alleviating them. We propose CommuteShare, a novel ridesharing service for daily commuters that encourages long-term ridesharing among commuters with similar commuting patterns, to increase the traffic efficiency in peak hours. We first identify commuting private vehicles (CPVs) from traffic records and model their commuting patterns. We then design a dynamic model to formulate the intention level of a CPV driver to offer a ride based on the spatio-temporal convenience and dynamic traffic conditions. Based on the commuting patterns of the CPVs and the dynamic model of the CPV drivers, we propose a ridesharing algorithm to compute ridesharing matches among CPVs. We perform extensive experiments on three real-world cross-domain urban big datasets from a major city of China. Experimental results show that, using the proposed CommuteShare service, over 5,300 private vehicles can be reduced daily on average during morning peak hours, with a reduction of 7-minute average waiting time for the riders.