Between-Ride Routing for Private Transportation Services

TL;DR

This paper develops an efficient algorithm for determining optimal between-ride routing policies for drivers in private transportation services, aiming to maximize profits by considering real-world factors like demand, traffic, and pricing.

Contribution

It models the between-ride routing problem as a stochastic shortest path problem and provides an iterative method to find optimal, cycle-free routing policies.

Findings

Algorithm effectively finds optimal routing policies.

Policies improve driver profits considering demand and traffic.

Validated on Boston and NYC road network data.

Abstract

Spurred by the growth of transportation network companies and increasing data capabilities, vehicle routing and ride-matching algorithms can improve the efficiency of private transportation services. However, existing routing solutions do not address where drivers should travel after dropping off a passenger and before receiving the next passenger ride request, i.e., during the between-ride period. We address this problem by developing an efficient algorithm to find the optimal policy for drivers between rides in order to maximize driver profits. We model the road network as a graph, and we show that the between-ride routing problem is equivalent to a stochastic shortest path problem, an infinite dynamic program with no discounting. We prove under reasonable assumptions that an optimal routing policy exists that avoids cycles; policies of this type can be efficiently found. We present an iterative approach to find an optimal routing policy. Our approach can account for various factors, including the frequency of passenger ride requests at different locations, traffic conditions, and surge pricing. We demonstrate the effectiveness of the approach by implementing it on road network data from Boston and New York City.