Autonomous mobility on demand in SimMobility: Case study of the central business district in Singapore

Abstract

Autonomous mobility on demand (AMOD) has emerged as a promising solution for urban transportation. Compared to prevailing systems, AMOD promises sustainable, affordable personal mobility through the use of self-driving shared vehicles. Our ongoing research seeks to design AMOD systems that maximize the demand level that can be satisfactorily served with a reasonable fleet size. In this paper, we introduce an extension for SimMobility — a high-fidelity agent-based simulation platform — for simulating and evaluating models for AMOD systems. As a demonstration case study, we use this extension to explore the effect of different fleet sizes and stations locations for a station-based model (where cars self-return to stations) and a free-floating model (where cars self-park anywhere). Simulation results for evening peak hours in the Singapore Central Business District show that the free-floating model performed better than the station-based model with a “small number” of stations; this occurred primarily because return legs comprised “empty” trips that did not serve customers but contributed to road congestion. These results suggest that making use of distributed parking facilities to prevent congestion can improve the overall performance of an AMOD system during peak periods.