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This paper presents results from a simulation-based study which aimed to demonstrate the feasibility of using agent-based simulation tools to model the impacts of shared autonomous vehicles. First, the paper outlines a research framework for the development and evaluation of low carbon mobility solutions driven by two disruptive forces which are changing the mobility landscape and providing consumers with more choices to meet their transport needs: automated self-driving and on-demand shared mobility services. The focus of this paper is on development of rigorous models for understanding the demand for travel in the age of connected mobility, and assessing their impacts particularly under scenarios of autonomous or self-driving on-demand shared mobility. To demonstrate the feasibility of the approach, the paper provides initial results from a pilot study on a small road network in Melbourne, Australia. A base case scenario representing the current situation of using traditional privately owned vehicles, and two autonomous mobility on-demand (AMoD) scenarios were simulated on a real transport network. In the first scenario (AMoD1), it was assumed that the on-demand vehicles were immediately available to passengers (maximum waiting times is zero). This constraint was relaxed in the second scenario (AMoD2) by increasing the allowable passenger waiting times up to a maximum of 5 minutes. The results showed that using the AMoD system resulted in a significant reduction in both the number of vehicles required to meet the transport needs of the community (reduction of 43% in AMoD1, and 88% in AMoD2), and the required on-street parking space (reduction of 58% in AMoD1 and 83% in AMoD2). However, the simulation also showed that this was achieved at the expense of a less significant increase in the total VKT (increase of 29% in AMoD1 and 10% in AMoD2). The paper concludes by describing how the model is being extended, the remaining challenges that need to be overcome in this research, and outlines the next steps to achieve the desired outcomes.
Dia et al. (Sun,) studied this question.