The potential impacts of automated cars on urban transport: An exploratory analysis

Objective: The concept of automated cars is rapidly becoming a reality, with a series of real world trial applications underway, and government predictions that automation will be introduced in the early 2020s. Yet there has still been very little analysis of the impacts of such developments on the performance of urban transport systems. These impacts are potentially complex. On the positive side, automation has the potential to increase road capacity, make driving available to more people, and reduce accidents and emissions. On the negative side, it could attract users away from public transport, walking and cycling, substantially increase traffic levels and stimulate urban sprawl. These impacts cannot currently be measured empirically and, by the time that they can, it will be too late to change the implementation model to rectify any resulting problems. Predictive assessments are therefore needed. The objective of this paper is to consider the possible impacts of automated vehicles, to predict their effects on the urban land use and transport system, and to discuss the policy implications. We focus specifically on automation of the car fleet, and do not consider the potential of automation of public transport or freight vehicles. This extended abstract presents our principal findings and is provided prior to publication of the full paper in 2019.

Methods: In the full paper we consider the current literature on the range of attributes of automated vehicles which might affect transport and land use patterns, and suggest potential outcomes for each over the period to 2050. These attributes include the proportion of automated vehicles in the car fleet, whether automated vehicles are privately purchased or publicly shared, the impacts of automation on network capacity, the reduced need to pay for and walk from parking places, the potential reduction in the value of in-vehicle time and the ability of those who cannot currently drive to use cars. We represent these attributes in an expanded causal link diagram of the urban land use and transport system, import those causal links into the MARS systems dynamics model, and test the impacts in a set of ten scenarios using an updated MARS model of Leeds.

Results: Based on our input assumptions, we find that kilometres travelled by car in 2050 could be over 50% higher than in the business as usual scenario. Public transport use could fall by 18%, threatening accessibility for those dependent on it, while walking and cycling could fall by 13%, reducing their health benefits. Overall person-km would rise, suggesting a tendency to urban sprawl, which is confirmed in subsequent tests. A requirement that all automated cars are made available as shared vehicles could reduce these adverse impacts somewhat, but the effects appear to be sensitive to the charge per km which is imposed.

Discussion: These results demonstrate the importance of understanding the scale of systems response to each of the attributes which we have considered. In terms of policy, it will clearly be important to manage the way in which automated cars are introduced into urban areas, if they are not to lead to a worsening of the urban environment, accessibility and health. A requirement to make all such vehicles part of shared fleets offers one way forward, but more work is needed to understand the way in which use of such fleets should be charged.