Evolutionary artificial intelligence algorithms for the one-way road orientation planning problem with multiple venues: An example of evacuation planning in Taiwan

Abstract

Introduction: In large-scale events such as concerts and sports competitions, participants often leave the venue at the same time to return to their respective destinations. Improper traffic planning and traffic light operation usually lead to traffic congestion and road chaos near the sites. Rapid evacuation of participants has become an important issue. Objectives: In this work, a one-way road orientation planning problem with multiple venues is studied in which all roads near the venues are to be scheduled into a one-way orientation with strong connectivity to increase the evacuation efficiency of participants. Methods: In accordance with Robbins’ theorem and a random sequence of integers, an encoding scheme based on module operator is presented to construct a strongly connected graph and plan a one-way orientation for all roads. The proposed encoding scheme is further embedded into four artificial intelligence approaches, namely, grey wolf optimization, immune algorithm, genetic algorithm, and particle swarm optimization, to solve the one-way road orientation planning problem such that the total distance of all vehicles from venues to their destinations is minimized. Results: Numerical results of test problems with multiple venues in Taiwan are provided and analyzed. As shown, all four algorithms can obtain the best solution for the test problems. Conclusions: The new presented encoding scheme with four algorithms can be used to effectively solve the one-way road orientation planning problem for the evacuation of participants. Moreover, grey wolf optimization is superior to the other three algorithms and particle swarm optimization is faster than the other three algorithms.