Abstract
This article presents an agent-based travel demand model, where agents react to transport supply across all mobility choices. Long-term choices include mobility tool ownership and work locations. Daily travel patterns are simulated at the individual level by sequentially combining activity frequency, activity durations and destinations as well as a rule-based time-of-day scheduling. A key to success in this novel approach is balancing individual preferences of travelers with system constraints. The model incorporates two types of constraints: 1) capacity constraints of the transport infrastructure. 2) natural time and space constraints during the execution of individual 24-hour day plans. Model results are validated against empirical observations of travel demand in Switzerland. The article concludes with a perspective for further research and development in the field of applied agent-based modeling.
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