Fast, Exact and Scalable Dynamic Ridesharing

TL;DR

This paper introduces a novel, fast, and exact algorithm for large-scale dynamic ridesharing that significantly outperforms existing methods in speed and scalability, enabling more efficient simulation and deployment of autonomous vehicle fleets.

Contribution

The paper presents a new algorithm based on contraction hierarchies with local buckets that finds exact solutions for ride request insertions much faster than current industry and academic methods.

Findings

30 times faster than existing algorithms

Scales better for large-scale scenarios

Reduces simulation time from days to hours

Abstract

We study the problem of servicing a set of ride requests by dispatching a set of shared vehicles, which is faced by ridesharing companies such as Uber and Lyft. Solving this problem at a large scale might be crucial in the future for effectively using large fleets of autonomous vehicles. Since finding a solution for the entire set of requests that minimizes the total driving time is NP-complete, most practical approaches process the requests one by one. Each request is inserted into any vehicle's route such that the increase in driving time is minimized. Although this variant is solvable in polynomial time, it still takes considerable time in current implementations, even when inexact filtering heuristics are used. In this work, we present a novel algorithm for finding best insertions, based on (customizable) contraction hierarchies with local buckets. Our algorithm finds provably exact solutions, is still 30 times faster than a state-of-the-art algorithm currently used in industry and academia, and scales much better. When used within iterative transport simulations, our algorithm decreases the simulation time for largescale scenarios with many requests from days to hours.