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Travel Itinerary Planning in Public Transportation Network Using Activity-Based Modeling

Keywords: activity-based modeling , multimodal public transportation network , time constrained travel salesman problem , Itinerary planning , GIS

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This study presents a new geospatial information system-based solution that assists people in trip planning to reach multiple destinations considering opening hours of points of interest and duration needed to perform multiple activities using multimodel public transportation media. People encounter with complexities in finding optimum paths in a multimodel public transportation system as they need to consider interactions of different modes of transportations in a more specific and constrained spatio-temporal structure. In this situation people’s context in the form of their activities diary becomes an important issue affecting trip planning and selecting mode of transportation. Activities diary constrains the spatio-temporal order of activities and enables us to study the effect of activities in transportation planning. In this study, the impact of activity diaries and spatio-temporal dimensions of activities in trip planning and optimum path findings are discussed. A new algorithm is proposed for planning itinerary considering network, time and duration constraints. The algorithm assumes that the activities are mandatory and the locations and duration of each activity are fixed. The travelers’ destinations, time and duration constraints provide the input of the algorithm. Using connectivity rules and policies, Dijkstra algorithm for finding the shortest paths is selected. The results determine the possible order of activities and their start and end time. The possible sequences of activities optimized based on minimizing waiting time during the activities i.e., time that person neither is doing an activity nor is traveling between activity locations and must wait till activity can be undertaken according to the time constraints. Finally, efficiency of the algorithm is determined according to the time complexity as a function of number of activity locations in the worst and normal cases. It has been proved that the time complexity of the algorithm depends on the third power of number of activity locations.


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