A multi-objective optimization framework for on-line ridesharing systems
This work provides an incremental improvement in ridesharing optimization for ride-sharing companies and users by balancing driver earnings and rider delays.
This paper addresses the challenge of matching riders and drivers in online ridesharing systems to minimize rider delays and maximize driver earnings. The proposed algorithm, based on biogeography-based optimization, achieves competitive performance against state-of-the-art methods on the Beijing ridesharing dataset.
The ultimate goal of ridesharing systems is to matchtravelers who do not have a vehicle with those travelers whowant to share their vehicle. A good match can be found amongthose who have similar itineraries and time schedules. In thisway each rider can be served without any delay and also eachdriver can earn as much as possible without having too muchdeviation from their original route. We propose an algorithmthat leverages biogeography-based optimization to solve a multi-objective optimization problem for online ridesharing. It isnecessary to solve the ridesharing problem as a multi-objectiveproblem since there are some important objectives that must beconsidered simultaneously. We test our algorithm by evaluatingperformance on the Beijing ridesharing dataset. The simulationresults indicate that BBO provides competitive performancerelative to state-of-the-art ridesharing optimization algorithms.