ABSTARCT :
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 travellers 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 traveller, the shortest path algorithm is used to plan an approximate optimal path that meets the traveller’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.
EXISTING SYSTEM :
Tourism transportation is a central issue in various studies at present [1], and it involves many aspects such as tourism, multiple transportation modes, and travel decisions. Currently, mainstream tourism service providers have provided users with a large number of tourism transportation services, but it is indeed impossible to intelligently plan travel itineraries based on users’ own needs. Custom travel services still need to be done manually. It takes a lot of manpower and time, so the problem of trip planning is a topic that travellers pay attention to, but it is a theoretical problem.
PROPOSED SYSTEM :
many scholars use space-time networks to conduct in-depth research in various fields. In 2017, Chen Jingwei, Liu Ming. proposed a time-space network model of the operational system of automated-vehicles. By using the technique of the time-space network, this paper described in detail the movements of the passengers and the automated vehicles in the road network. The transformation of the original road network into a static space-time network reduced the complexity of the new model [7]; In 2018, Sai Qiuyue et al. designed a discrete space-time network construction algorithm for abnormal flights, and proposed a feasible path generation algorithm based on the constructed discrete-time network[8] ; In 2019, Zhang Zheming et al. built a spatiotemporal state network integrated with the crew rules to control the network size and simplify the complexity of mathematical models. It is proved that this method can not only effectively solve the problem of high-speed railway passenger traffic planning, but also has a certain effect when solving large-scale mixed time problems [9]; Cao Yang et al. extended the understanding of the itinerary planning problem from a spatial perspective to a tourist activity perspective. From the spatiotemporal coupling relationship and reconstruction mode of tourism nodes, the multidimensional attributes such as time, space, and topic involved in the travel were organically organized, and then the travel’s spatiotemporal chain was proposed. The conceptual model and the method of space-time convergence of the stroke elements.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
