Dynamic path planning based on adaptable Ant colony optimization algorithm

In recent years, many tourist attractions have suffered heavy traffic problems during vacations or holidays due to the convenience of various kinds of transportation and the growing popularity of leisure activities. However, according to some common experience, different recreational attractions are not necessary located along a single route in a given tourist area. If all the recreational attractions in a given area are arranged in sequence based on the order to visit, visitors may suffer schedule delays due to traffic jams occurring in early spots of this sequence.This study presents an Adaptable Ant Colony Optimization Algorithm(AACO) to solve the path arrangement problem. This study combines a consideration of leisure itineraries and the practical operation of a route planning model in order to establish a dynamic planning system that can instantaneously provide optimal route information. The goal is to create a system that can consider visitor’sattractions preferences, and uses Google Maps API to assist in tailoring travel routes for each visitor. The experimental results show that this system can plan the route according to the desire level of tourists to scenic spots in a short period of time. The sum of the desired values calculated by this algorithm is greater than that of the original ant optimization algorithm (ACO) and the improved ant optimization algorithm (IACO). The results show that the mean total desired value of improvedACO algorithm is 25.53% higher; the average travel time is reduced by 34.32%; and the average computing time is reduced by 25.53% than that of the traditional ant optimization algorithm (ACO). We find that the algorithm can effectively improve the travel route planning problem.