Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities

Abstract

The growing demand for urban mobility highlights the need for relevant and sustainable solutions in cities worldwide. Thus, we develop and implement a framework to analyze the systemic impacts of future urban mobility trends and policies. We build on prior work in classifying the world's cities into 12 urban typologies that represent distinct land-use and behavioral characteristics by introducing a generalized approach for creating a detailed, simulatable prototype city that is representative of a given typology. We then generate and simulate two auto-dependent (largely US-specific) prototype cities via a state-of-the-art agent-based platform, SimMobility, for integrated demand microsimulation and supply mesoscopic simulation. We demonstrate the framework by analyzing the impacts of automated mobility on-demand (AMoD) implementation strategies in the cities based on demand, congestion, energy consumption and emissions outcomes. Our results show that the introduction of AMoD cannibalizes mass transit while increasing vehicle kilometers traveled (VKT) and congestion. In sprawling auto-dependent cities with low transit penetration, the congestion and energy consumption effects under best-case assumptions are similar regardless of whether AMoD competes with or complements mass transit. In dense auto-dependent cities with moderate transit modeshare, the integration of AMoD with transit yields better outcomes in terms of VKT and congestion. Such cities cannot afford to disinvest in mass transit, as this would result in unsustainable outcomes. Overall, this framework can provide insights into how AMoD can be sustainably harnessed not only in low-density and high-density auto-dependent cities, but also in other typologies.