Congestion and energy consumption of heterogeneous traffic flow mixed with intelligent connected vehicles and platoons

Junwei Zeng, Yongsheng Qian, Jiao Li, Yongzhi Zhang and Dejie Xu

Physica A: Statistical Mechanics and its Applications, 2023, vol. 609, issue C

Abstract: In the future, there will inevitably be a mixed driving situation of intelligent connected vehicles and traditional manual driving vehicles. How intelligent connected vehicles affect traditional traffic flow has attracted increasing attention. Based on the different characteristics of these two types of vehicles a two-lane heterogeneous traffic flow cellular automata model of expressway is proposed, and the effects of whether intelligent connected vehicles form platoons and the size of platoons on traffic flow congestion and energy consumption are simulated. The results show that: as the proportion of intelligent connected vehicles and the size of platoons increase, the maximum traffic capacity can be effectively improved. Besides, with the increase of the mixed ratio, the larger the platoon size, the higher the average speed of traffic flow. The platoon can alleviate even eliminate the congestion faster and more thoroughly. However, in the high-density fully intelligent connected vehicles environment, the larger platoon size leads a negative impact on the traffic flow in the form of “moving bottleneck”. When the platoon size is 4, it can maximize the positive effect of the platoon on the traffic flow. Besides, increasing the proportion and shortening the reaction time of intelligent connected vehicles can effectively reduce the average energy consumption. And platoon mode is more conducive to reduce the average energy consumption than the discrete mode, but the platoon size should be controlled within a reasonable range, otherwise too larger platoon size will aggravate the energy consumption in traffic flow.

Keywords: Intelligent connected vehicle platoons; Energy consumption; Cellular automaton (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122008895

DOI: 10.1016/j.physa.2022.128331

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