Personalized Daily Travel Planning Based on Two-Stage Spatiotemporal Network

Nowadays tourism transportation has become a hot topic of research, and the rapid development of Internet technology has overloaded information, which has made it impossible to provide services with different preferences for different users. Therefore, personalized tourism transportation has become the current mainstream trend. According to the different preferences of travelers for money and travel time, based on the analysis of mainstream tourism services, and combined with multi-source traffic data, this paper proposes a mathematical model for personalized travel planning. This paper proposes a two-stage spatiotemporal network solution algorithm. In the first stage, based on the set of travel attractions given by the traveler, the shortest path algorithm is used to plan an approximate optimal path that meets the traveler’s preferences and to implement connection of multiple travel modes. The second stage is combined with the spatiotemporal network to achieve daily travel planning between multiple attractions. The two-stage spatiotemporal network algorithm is feasible for solving path planning problems, and can simplify route planning problems with time windows, which provides a useful reference for future personalized travel planning recommendations.