Simulation Study on the Coupling Relationship between Traffic Network Model and Traffic Mobility under the Background of Autonomous Driving

Dengzhong Wang, Tongyu Sun (), Anzheng Xie and Zhao Cheng
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Dengzhong Wang: College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Tongyu Sun: College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Anzheng Xie: Hangzhou Urban and Rural Construction Development Research Institute, Hangzhou 310016, China
Zhao Cheng: Hangzhou Juliang Engine Network Technology Co., Ltd., Hangzhou 311100, China

Sustainability, 2023, vol. 15, issue 2, 1-15

Abstract: Autonomous driving technology will bring revolutionary changes to the development of future cities and transportation. In order to study the impact of autonomous driving on urban transportation networks, this paper first summarizes the development status of autonomous driving technology, and then three space–traffic network coupling models are proposed based on the differences of speed and space, which are the traditional difference type, scale variation type, and slow-guided type. On this basis, a new 4 * 4 km grid city model is constructed. Based on the MATSim multi-agent simulation method, the traffic parameters of the three models are studied. The results show that under the same traffic demand, the service scale and level of the three traffic networks are significantly different. The optimal service level of the traditional differential type is 2.15 times the efficiency of the slow-guided type. Under the same demand and road network mode, the travel speed of the autonomous driving mode is 1.7–2.8 times that of the traditional mode. Under the same lane area ratio, the travel speed of traditional driving is much smaller than that of autonomous driving, which is about 2.6–3.6 times greater than the former. The research conclusion has certain reference significance for formulating urban spatial development strategies and policies under autonomous driving environments and for promoting the sustainable development of urban transportation.

Keywords: autonomous driving; traffic network model; traffic mobility; traffic simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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