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In this paper, we study the challenging problem of how to balance taxidistribution across a city in a dynamic ridesharing service. First, weintroduce the architecture of the dynamic ridesharing system and formallydefine the performance metrics indicating the efficiency of the system. Then,we propose a hybrid solution involving a series of algorithms: the CorrelatedPooling collects correlated rider requests, the Adjacency Ride-Matching basedon Demand Learning assigns taxis to riders and balances taxi distributionlocally, the Greedy Idle Movement aims to direct taxis without a currentassignment to the areas with riders in need of service. In the experiment, weapply city-scale data sets from the city of Chicago and complete a case studyanalyzing the threshold of correlated rider requests and the average onlinerunning time of each algorithm. We also compare our hybrid solution withmultiple other methods. The results of our experiment show that our hybridsolution improves customer serving rate without increasing the number of taxisin operation, allows both drivers to earn more and riders to save more pertrip, and all with a small increase in calling and extra trip time.

Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning