Integration of a Capacity-Constrained Workplace Choice Model Recent Developments and Applications with an Agent-Based Simulation in Singapore
Authored by Kay W Axhausen, Alexander Erath, Basil J Vitins
Date Published: 2016
DOI: 10.3141/2564-01
Sponsors:
Singapore National Research Foundation
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Mathematical description
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Abstract
Destination choice models can be embedded in transport and land use
models to understand travel and location choice behavior and to forecast
scenarios. Utility-maximizing destination choice models can account for
individual behavior and make them suitable for agent-based models, while
processing destination capacities is also in line with agent based
modeling. This paper addresses the possibility and impact of introducing
capacity constraints, their effect on choice behavior, and the
feasibility of applying an approach like this in agent-based
micro-simulations with individual characteristics for each agent. Here, a comprehensive workplace choice model and its application in a
large-scale simulation case study for Singapore are described; one
technical and one methodological achievement are highlighted. Technical
achievement benefits from recent computational advances; the workplace
choice model is estimated with a comprehensive utility function on a
large data set with 103 destinations. Reasonable model fit and robust
parameters are achieved while obviating sampling techniques; resulting
parameters are efficiently applied to the entire 5.4 million Singapore
population and validated with survey data. For methodological
innovation, capacity limitations are introduced at workplaces to avoid
oversaturation. A robust optimization method based on shadow prices is
proposed to accommodate capacity limitations at all workplaces during
the choice model application defined above. The proposed method
efficiently assigns commuters to unused workplaces while respecting
individual commuter preferences. Validation of the simulation results, by comparing travel time distributions for commuting trips reported in
travel diary data, shows that the model fits well with observed data.
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