Q-learning algorithm and echo state network based riding tour route planning method

Abstract

The invention discloses a method for planning an individualized optimal riding tour route according to the requirement of a user. The method comprises the following steps: firstly taking the quality of a scenic spot into consideration, predicating the dynamic quality of the scenic spot according to an echo state network, acquiring the comprehensive quality of the scenic spot in combination with the static quality of the scenic spot; then taking the user experience quality in the riding process into consideration, planning an overview of a city, and characterizing the infrastructure of the city along the density of landmark buildings. Each directive scenic spot cluster is subjected to Q-value training and iteration until convergence through a Q-learning algorithm according to the user preference; and then the global optimal route is planned according to the iterated Q value. The invention further provides a specific knot inserting algorithm to meet the requirement of part of users hoping to have a few specific scenic spots in the route, the algorithm ensures that the global optimality of the route is not broken, and the tourism experience of riding of the user is guaranteed.

Description

Technical field [0001] The present invention relates to the field of travel route planning methods, in particular to a cycling travel route planning method based on Q-learning algorithm and echo state network. Background technique [0002] Considering the new scene of cycling tourism, a comprehensive plan is inevitably needed when cycling tourism. A large amount of data needs to be excavated, taking into account various factors, and planning an optimal travel route according to tourists' preferences. Then perfecting the travel plan will be very time-consuming and labor-intensive. Therefore, the emergence of a software that can intelligently plan cycling travel routes for tourists will bring good news to a large number of cycling travel enthusiasts. [0003] The existing mainstream tourism planning mechanisms are classified into the following two types: route planning methods based on mining scenic spot information and analyzing scenic spot quality, and route planning methods based...

AI Technical Summary

Problems solved by technology

However, they do not take into account the differences in the probability of tourists visiting various attractions at different times, which reduces the feasibility of the plan

In addition, the traditional planning method basically uses a greedy algorithm to screen scenic spots, which directly leads to the inability to guarantee global optimality, and cannot perfectly meet the special requirements of some tourists.

Images