Multi-constrained optimal path search algorithms

Abstract

The problem of trip planning has received wide concerns in recent years. More and more people require the service of automatically confirming the optimal tour path. When users assign the source, the destination and the permitted time of the tour, how do we help them find the optimal path with the maximum popularity score? The multi-constrained optimal path search solutions have been used to solve the trip planning problem widely. However, when the permitted time is not enough to visit all attractions in any path, existing methods can not find the path satisfying the constraints. The time and the popularity score are different when we select the different attractions to visit in the same path. So we propose a new search rule to answer above issue, making a choice on any node (visit or pass by) according to the tradeoff between permitted time and popularity score of attractions. As our search rule need to make a choice on any attraction (visit or pass by), the search cost will be larger. This problem is NP hard. In this paper, we propose an exact algorithm to find the optimal path in relatively small data sets, and we also present a heuristic algorithm which is efficient and scalable to large data sets. The experimental results on real data sets reveal that our algorithms are able to find the optimal path efficiently. © 2014 Springer International Publishing Switzerland.