Towards a Socially Optimal Multi-Modal Routing Platform

TL;DR

This paper introduces proactive, socially considerate multi-modal routing algorithms that predict and optimize overall traffic flow, demonstrating improved system performance in urban transportation simulations.

Contribution

It proposes novel routing algorithms that incorporate social considerations and predictions to enhance urban mobility efficiency.

Findings

Improved traffic flow with socially considerate routing.

Effective even at low adoption levels.

Simulation results show system-level performance gains.

Abstract

The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becoming a more commonplace, particularly in urban environments. While such services may be deemed more convenient than riding public transit due to their on-demand nature, reports show that they do not necessarily decrease the congestion in major cities. One of the key problems is that typically mobility decision support systems focus on individual utility and react only after congestion appears. In this paper, we propose socially considerate multi-modal routing algorithms that are proactive and consider, via predictions, the shared effect of riders on the overall efficacy of mobility services. We have adapted the MATSim simulator framework to incorporate the proposed algorithms present a simulation analysis of a case study in Nashville, Tennessee that assesses the effects of our routing models on the traffic congestion for different levels of penetration and adoption of socially considerate routes. Our results indicate that even at a low penetration (social ratio), we are able to achieve an improvement in system-level performance.