Microsimulation of Demand and Supply of Autonomous Mobility On Demand

Authored by Carlos Lima Azevedo, Katarzyna Marczuk, Sebastian Raveau, Harold Soh, Muhammad Adnan, Kakali Basak, Harish Loganathan, Neeraj Deshmunkh, Der-Horng Lee, Emilio Frazzoli, Moshe Ben-Akiva

Date Published: 2016

DOI: 10.3141/2564-03

Sponsors: Singapore National Research Foundation

Platforms: MATSim

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual's day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model SimMobility for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: (a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; (b) a midterm level that handles agents' activities and travel patterns; and (c) a short-term level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.

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