Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Haopeng Wang, Zhenzhi Zhao (), Yingying Ma, Hao Wu and Fei Bao
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Haopeng Wang: School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China
Zhenzhi Zhao: School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China
Yingying Ma: School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China
Hao Wu: School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, China
Fei Bao: School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China

Sustainability, 2023, vol. 15, issue 11, 1-19

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.

Keywords: sustainable urban planning; logistics cost; machine learning; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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