Demand for shared mobility to replace private mobility using connected and automated vehicles

TL;DR

This study models the impact of shared connected and automated vehicles (SCAVs) on travel demand, traffic congestion, and welfare, showing increased SCAV usage and reduced congestion in Toronto.

Contribution

It develops an agent-based model with a dispatching system to analyze endogenous demand and fleet size changes of SCAVs and private CAVs in a real urban network.

Findings

SCAV demand increases by 43% over seven days

Private CAV demand decreases by 10%

Total travel time reduces by 7%, indicating less congestion

Abstract

We examine how introduction of Shared Connected and Automated vehicles (SCAVs) as a new mobility mode could affect travel demand, welfare, as well as traffic congestion in the network. To do so, we adapt an agent-based day-to-day adjustment process and develop a central dispatching system, which is implemented on an in-house traffic microsimulator. We consider a two-sided market in which demand and SCAV fleet size change endogenously. For dispatching SCAV fleet size, we take changing traffic conditions into account. There are two available transport modes: private Connected Automated Vehicles (CAVs) and SCAVs. The designed system is applied on downtown Toronto network using real data. The results show that demand of SCAVs goes up by 43 per cent over seven study days from 670 trips on the first day to 959 trips on the seventh day. Whereas, there is a 10 per cent reduction in private CAV demand from 2807 trips to 2518 trips during the same duration. Moreover, total travel time of the network goes down by seven per cent indicating that traffic congestion was reduced in the network.