Impact of Mobility-on-Demand on Traffic Congestion: Simulation-based Study

TL;DR

This study uses simulation to analyze how large-scale mobility-on-demand systems impact urban traffic congestion, revealing increased total travel distances and congestion levels due to empty trips.

Contribution

It provides the first detailed simulation-based assessment of large-scale station-based mobility-on-demand effects on traffic congestion in a European city.

Findings

38% of kilometers traveled are driven empty.

Mobility-on-demand increases total driven distance.

Traffic congestion levels rise due to additional trips.

Abstract

The increasing use of private vehicles for transportation in cities results in a growing demand for parking space and road network capacity. In many densely populated urban areas, however, the capacity of existing infrastructure is insufficient and extremely difficult to expand. Mobility-on-demand systems have been proposed as a remedy to the problem of limited parking space because they are able to satisfy the existing transportation demand with fewer shared vehicles and consequently require less parking space. Yet, the impact of large-scale vehicle sharing on traffic patterns is not well understood. In this work, we perform a simulation-based analysis of consequences of a hypothetical deployment of a large-scale station-based mobility-on-demand system in Prague and measure the traffic intensity generated by the system and its effects on the formation of congestion. We find that such a mobility-on-demand system would lead to significantly increased total driven distance and it would also increase levels of congestion due to extra trips without passengers. In fact, 38% kilometers traveled in such an MoD system would be driven empty.