Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches
Haopeng Wang, Zhenzhi Zhao, Yingying Ma, Hao Wu, Fei Bao
Abstract
Nowadays, urban areas are experiencing heavy traffic, and governments are implementing various policies to manage it. For example, in China, trucks are prohibited from entering urban areas during the daytime to reduce traffic congestion. However, we have found that this policy is not cost-efficient for logistics, which includes gas fees, air pollution fees, and wear and tear expenses, as it cannot adjust to real-time traffic conditions. To minimize logistics costs in real-time, we propose DeepPlan, a deep-learning-based model that optimizes urban planning. Our model calculates the optimal route for each truck based on real-time traffic data in urban areas. We learned the optimal route from the trace data of taxi drivers who are experienced in minimizing logistics costs. Our experimental results show that DeepPlan outperforms existing urban plans by 25% and works well in various circumstances, including different weather and unexpected events.