Time-dependent personal tour planning and scheduling in metropolises

This paper addresses the problem of time-dependent tour planning in complex and large urban areas that may be of importance for different groups of people. The problem is determination of chronological sequences of attractive points during a specific period via several modes of transportation system. The proposed approach adopted a nested architecture in which tour planning routine calls multimodal shortest path subroutine to generate an itinerary based on user preferences and restrictions of interesting points. Two adapted genetic algorithms were employed in the engine of both blocks. In these algorithms, chromosomes with variable lengths and particularly defined evolutionary stages are used. The proposed process has been tested over the dataset of city of Tehran. The evaluation consists of preparation of 400 tours with different initial points, start time, and tour durations. It was assumed that just three modes of walking, bus, and subway are used to travel between points of interest. Moreover, some tests are applied to dataset to illustrate the adaptability and time-dependency nature of method. The experimental results and related indices such as optimality ratios show that the proposed algorithm can find optimum tour according to introduced constraints.