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

TL;DR

This paper presents a hybrid algorithmic approach to balance taxi distribution in city-scale dynamic ridesharing, improving service efficiency and driver earnings with minimal extra travel time.

Contribution

The paper introduces a novel hybrid solution combining demand learning and local balancing algorithms for taxi distribution in large-scale ridesharing systems.

Findings

Improves customer service rate without increasing fleet size

Enhances driver earnings and rider savings

Maintains low additional travel and call times

Abstract

In this paper, we study the challenging problem of how to balance taxi distribution across a city in a dynamic ridesharing service. First, we introduce the architecture of the dynamic ridesharing system and formally define the performance metrics indicating the efficiency of the system. Then, we propose a hybrid solution involving a series of algorithms: the Correlated Pooling collects correlated rider requests, the Adjacency Ride-Matching based on Demand Learning assigns taxis to riders and balances taxi distribution locally, the Greedy Idle Movement aims to direct taxis without a current assignment to the areas with riders in need of service. In the experiment, we apply city-scale data sets from the city of Chicago and complete a case study analyzing the threshold of correlated rider requests and the average online running time of each algorithm. We also compare our hybrid solution with multiple other methods. The results of our experiment show that our hybrid solution improves customer serving rate without increasing the number of taxis in operation, allows both drivers to earn more and riders to save more per trip, and all with a small increase in calling and extra trip time.