Ride-sharing is one of the solutions to the ever-increasing problem of congestion and air pollution in cities. However, its effectiveness in reducing private vehicles is contingent on its efficiency while maintaining economic viability from the perspective of both the operator and the passenger. We propose a ride-sharing framework that jointly takes into account the quality of sharing as well as the operator revenue while fixing the consumer cost. We formulate a weighted graph coloring optimisation problem that has the flexibility to incorporate factors that promote ride-sharing efficiency while maximizing the operator revenue. Application of our method on dataset of taxi rides in New York City, USA provides promising results vis-a-vis the state-of-the-art approaches. We illustrate that our framework has the potential to increase the occupancy of vehicles, enable sharing of bigger vehicles while assigning naturally shared rides. Further, when compared to there being no ride-sharing, on an average, the percentage of riders sharing rides and percentage of vehicles being shared is more than 85% and 75% respectively with over 60% reduction in the number of vehicles being used. The key insight obtained from this work is that incorporating quality and efficiency of shared rides has the potential to enhance operator revenue as well.
Improved Real Time Ride Sharing via Graph Coloring
2019-10-01
1964563 byte
Conference paper
Electronic Resource
English
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