Citation

Overtoom I, Correia G, Huang Y, Verbraeck A. Int. J. Transp. Sci. Technol. 2020; 9(3): 195-206.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ijtst.2020.03.009

PMID

unavailable

Abstract

New developments in the automotive world have the power to change mobility, but because of high uncertainties, municipalities are adopting a wait-and-see attitude. Nonetheless, autonomous, connected and shared vehicle technologies are in a far stage of development and it is only a matter of time before shared autonomous vehicles (SAVs) enter urban traffic. This research aims to provide insights into the congestion effects of SAVs on urban traffic, focusing on the differences in microscopic behaviour from conventional cars, and to investigate which easy-to-implement solutions a municipality could apply to facilitate the new mix of traffic. This was researched by performing a simulation study, using the traffic simulation package Vissim and a case study of a network in the city of The Hague during the morning peak in 2040. Several SAV market penetration scenarios were tested: 0%, 3%, 25%, 50% and 100% SAV usage by travellers. Additionally, two network designs were implemented: dedicated lanes for SAVs and kiss & ride (K&R)-facilities. From the results, it was clear that while the autonomous driving capabilities of SAVs help reduce traffic congestion, they also have a negative effect by stopping on the curbside to drop off passengers, forming bottlenecks for other road users, and by circulating on the network using low capacity links. Below the line, this adds up to an overall negative effect on urban traffic congestion according to our results. The dedicated lanes design was unsuccessful at reducing this congestion caused by SAVs. The K&R design, however, was successful at reducing delays, but only for SAV penetration rates higher than 25%. These exact effects are not generalizable due to limitations in network size and simulation software. However, the results can be seen as indicative for planning purposes. Similar effects could be expected in cities where transport network companies (TNCs) such as Uber become exceptionally popular with non-autonomous cars. The advice for municipalities is to closely monitor the situation and to account for SAVs (and TNCs) in each new infrastructural project.

Language: en

Keywords

Autonomous vehicles; Curb use; Shared autonomous vehicles; Simulation; Urban traffic