Network Impacts of Automated Mobility-on-Demand: A Macroscopic Fundamental Diagram Perspective

• URL: http://arxiv.org/abs/2011.05092v1
• Date: Tue, 10 Nov 2020 13:39:35 GMT
• Title: Network Impacts of Automated Mobility-on-Demand: A Macroscopic Fundamental Diagram Perspective
• Authors: Simon Oh, Antonis F. Lentzakis, Ravi Seshadri, Moshe Ben-Akiva
• Abstract summary: Automated Mobility on Demand (AMOD) is a promising solution that may improve future urban mobility. This paper investigates the network impacts of AMOD through high-fidelity activity- and agent-based traffic simulation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Technological advancements have brought increasing attention to Automated Mobility on Demand (AMOD) as a promising solution that may improve future urban mobility. During the last decade, extensive research has been conducted on the design and evaluation of AMOD systems using simulation models. This paper adds to this growing body of literature by investigating the network impacts of AMOD through high-fidelity activity- and agent-based traffic simulation, including detailed models of AMOD fleet operations. Through scenario simulations of the entire island of Singapore, we explore network traffic dynamics by employing the concept of the Macroscopic Fundamental Diagram (MFD). Taking into account the spatial variability of density, we are able to capture the hysteresis loops, which inevitably form in a network of this size. Model estimation results at both the vehicle and passenger flow level are documented. Environmental impacts including energy and emissions are also discussed. Findings from the case study of Singapore suggest that the introduction of AMOD may bring about significant impacts on network performance in terms of increased VKT, additional travel delay and energy consumption, while reducing vehicle emissions, with respect to the baseline. Despite the increase in network congestion, production of passenger flows remains relatively unchanged.

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