DLCSS: Dynamic Longest Common Subsequences

TL;DR

The paper introduces DLCSS, a dynamic algorithm for efficiently comparing routes to identify shared trips in autonomous vehicle networks, aiding fleet management and urban planning.

Contribution

It presents the DLCSS algorithm, a novel method for fast, cost-effective route comparison that dynamically focuses on shared segments, improving scalability and decision-making.

Findings

DLCSS significantly reduces computation time for route comparison.

The algorithm accurately estimates fleet sizes needed for shared mobility.

It enables better urban mobility planning and infrastructure scaling.

Abstract

Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility models. However, to evaluate, whether two or more route requests have the potential for a shared ride, is a compute-intensive task, if done by rerouting. In this work, we propose the Dynamic Longest Common Subsequences algorithm for fast and cost-efficient comparison of two routes for their compatibility, dynamically only incorporating parts of the routes which are suited for a shared trip. Based on this, one can also estimate, how many autonomous vehicles might be necessary to fulfill the local mobility demands. This can help providers to estimate the necessary fleet sizes, policymakers to better understand mobility patterns and cities to scale necessary infrastructure.