Abstract
The important function of the Intelligent Transportation System (ITS) is to collect and disseminate certain information from different locations of the road network. The information includes traffic safety, current experienced travel times, or other information the travelers are interested in. The key point in optimizing a travel time lies at locating a shortest path under various predefined constraints or limits. Due to the constraints such as travel time limits, sightseeing-spot visit sequence and priority, and travel types, etc., the solutions are often difficult and time consuming to reach optimal. In this paper, we proposed Computational Intelligence for Personal Travel Scheduling Algorithm (CIPTS), which has two constraints, travel day and time limits. Using tree model to obtain the shortest paths and then merging the paths. CIPTS exhibits its effectiveness in resolving the personalized travel scheduling problems and compare with the Efficient Spot Tour(EST) method in our experiments. The CIPTS is promising to be a useful and realistic automatic tool for performing practical travel planning tasks. The efficiency of CIPTS improvement is even more significant when the number of sightseeing spots to be scheduled grows greater.
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Hsu, LF., Hsu, CC., Lin, TD. (2015). Computational Intelligence for Personalized Travel Scheduling System. In: Camacho, D., Kim, SW., Trawiński, B. (eds) New Trends in Computational Collective Intelligence. Studies in Computational Intelligence, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-319-10774-5_20
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DOI: https://doi.org/10.1007/978-3-319-10774-5_20
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