Abstract
This paper attempts to disclose the features of the mixed traffic flow of manually driven vehicles (MVs) and autonomous vehicles (AVs). Considering dynamic headway, the mixed traffic flow was modelled based on the improved single-land cellular automata (CA) traffic flow model (DHD) proposed by Zhang Ningxi. The established CA model was adopted to obtain the maximum flow of the mixed traffic flow and was analyzed under different proportions of AVs. On this basis, the features of the mixed traffic flow were summarized. The main results are as follows: the proportion of AVs has a significant impact on the mixed traffic flow; when the proportion reached 0.6, the flow of the whole lane was twice that of the MV traffic flow. At a low density, the AV proportion has an obvious influence on mixed traffic flow. At a high density, the mixed traffic flow changed very little, as the AV proportion increased from 0 to 5. The reason is that the flow of the whole lane is constrained by the fact that MVs cannot move faster. However, when the AV proportion reached 0.8, the flow of the whole lane became three times that at the proportion of 0.6. At the speed of 126 km/h, the flow rate was 2.5 times the speed limit of 54 km/h. The findings lay a theoretical basis for the modelling of multilane mixed traffic flow.
Highlights
In traffic engineering, the traffic flow model is an important research tool to facilitate the understanding of complex traffic phenomena
Ngoduy [16] noticed the significant improvement to the capacity of the traffic system and travel time, when the autonomous vehicles (AVs) proportion in the mixed traffic flow reached 30%
Based on the DHD, this paper designs an improved cellular automata (CA) model, in which manually driven vehicles (MVs) update their positions by the rules of the NaSch model, while AVs update their positions by the dynamic headway rules. e designed model was applied to MATLAB simulation. e results show that the AV proportion has a significant impact on the mixed traffic flow; when the proportion reached 0.6, the flow of the whole lane was twice that of the MV traffic flow
Summary
The traffic flow model is an important research tool to facilitate the understanding of complex traffic phenomena. Anks to the constant updates of algorithms and sensing techniques, autonomous vehicles (AVs) are poised to make up an important part of road traffic In addition to their excellence in driving, AVs may bring a huge impact on the safety and efficiency of the operation of the traffic system [8,9,10]. Levin’s model only takes account of the driver’s reaction time, without considering the interaction between MVs and AVs. Sharma et al [14] and van Lint et al [15] simulated the driving behavior of drivers in the mixed traffic flow model and identified the features of mixed traffic flow but failed to measure the restriction effect of MVs on AVs. Ngoduy [16] noticed the significant improvement to the capacity of the traffic system and travel time, when the AV proportion in the mixed traffic flow reached 30%. The authors summarized the features of the mixed traffic flow
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