Optimal Route Planning System for Logistics Vehicles Based on Artificial Intelligence

Wu-Chih Hu,
Hsin-Te Wu,
Hsin-Hung Cho,
Fan-Hsun Tseng,


E-commerce businesses have enjoyed drastic growth in sales numbers; subsequently, logistics businesses, who stands at the end of this commercial chain, have been receiving a rising number of service requests. The system utilizes Google Maps and its “multiple destination” function to search for possible routes. The routes undergo a multilayer perceptron model for traffic condition simulation. Finally, the system applies Dijkstra’s algorithm to identify the optimal route. The proposed system bears the following features: (1) a multilayer perceptron model is created using the history of traffic conditions; (2) the route planning considers traffic condition prediction based on the expected time of travel; (3) the optimal route is calculated using Dijkstra’s algorithm based on vehicle speed; (4) the system can include multiple destinations in its calculation, providing comprehensive travel plan for the logistics vehicle; (5) the system allows logistics business operators to keep track of their vehicles’ whereabouts and current traveling route. The system was implemented during experiments and proven to be feasible as well as effective in reducing idle driving and enhancing transportation efficiency. It is verified that the suggested system in this study has shown an outstanding performance from the experimental results; hence, the suggested system is capable of applying in realistic industry conditions.

Citation Format:
Wu-Chih Hu, Hsin-Te Wu, Hsin-Hung Cho, Fan-Hsun Tseng, "Optimal Route Planning System for Logistics Vehicles Based on Artificial Intelligence," Journal of Internet Technology, vol. 21, no. 3 , pp. 757-764, May. 2020.

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