An activity chain optimization method with comparison of test cases for different transportation modes

Abstract

In order to provide optimal choice of activity locations and travel time reduction, a daily activity chain optimization method has been elaborated, which includes temporal and spatial flexibility of the activities. In the course of the optimization process, possible alternatives are searched using a modified version of the Traveling Salesman Problem with Time Window. The method is extended with a genetic algorithm to provide an optimal order of activities as the minimum of the predefined cost function (i.e. travel time). The multiobjective optimization can efficiently mitigate the travel time of the users. In order to provide some insight regarding the performance of the optimization algorithm, application-oriented simulations were performed on a real-world transportation network using real travel time information. Three transportation modes were considered: car, public transport, and combined (public transport with car-sharing). The simulation results demonstrate that the optimization is able to reduce travel time by 20–30%.

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