Multiclass traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles

2019 ◽  
Vol 126 ◽  
pp. 139-168 ◽  
Author(s):  
Jian Wang ◽  
Srinivas Peeta ◽  
Xiaozheng He
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Traffic Assignment ◽  
Mixed Traffic ◽  
Mixed Traffic Flow ◽  
Assignment Model

scholarly journals Control strategy for the optimization of mixed traffic flow with autonomous vehicles

2019 ◽  
Vol 52 (8) ◽  
pp. 227-232
Author(s):  
Balázs Németh ◽  
Zsuzsanna Bede ◽  
Péter Gáspár
Keyword(s):  
Traffic Flow ◽  
Control Strategy ◽  
Autonomous Vehicles ◽  
Mixed Traffic ◽  
Mixed Traffic Flow

scholarly journals Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xinghua Hu ◽  
Mengyu Huang ◽  
Jianpu Guo
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Theoretical Basis ◽  
Flow Model ◽  
Maximum Flow ◽  
Feature Analysis ◽  
Mixed Traffic ◽  
Mixed Traffic Flow ◽  
Ca Model

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.

scholarly journals Optimal Formation of Autonomous Vehicles in Mixed Traffic Flow

2020 ◽  
Vol 53 (2) ◽  
pp. 15204-15210 ◽  
Author(s):  
Keqiang Li ◽  
Jiawei Wang ◽  
Yang Zheng
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Mixed Traffic ◽  
Mixed Traffic Flow

Traffic Oscillation using Stochastic Lagrangian Dynamics: Simulation and Mitigation via Control of Autonomous Vehicles

2019 ◽  
Vol 2673 (7) ◽  
pp. 1-11
Author(s):  
Fangfang Zheng ◽  
Liang Lu ◽  
Ruijie Li ◽  
Xiaobo Liu ◽  
Youhua Tang
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Dynamics Simulation ◽  
Lagrangian Model ◽  
Mixed Traffic ◽  
Mixed Traffic Flow ◽  
Sample Paths ◽  
Stop And Go ◽  
The Impact

The phenomenon of stop-and-go waves is frequently observed in congested traffic. With the development of connected and autonomous vehicle (CAV) technologies, it is possible to reduce traffic oscillation via control of CAVs in a mixed traffic flow with both human drivers and autonomous vehicles (AVs). This paper introduces a stochastic Lagrangian model which is capable of simulating stop-and-go traffic considering the heterogeneity of drivers. The sample paths of the stochastic process are smooth without aggressive oscillation. The model is further extended to the mixed traffic flow condition, considering stochastic human driving behavior and deterministic behavior of AVs. With the proposed model, the variation of performance of AV control strategies can be quantified in addition to the average performance. A numerical example with a single lane circular road is used to investigate the impact of the AV control strategy on mitigating stop-and-go waves. Both qualitative and quantitative results show that the phenomenon of stop-and-go waves can be reduced significantly with only one AV, while the increase of AVs from 10% (two AVs) to 50% (10 AVs) offers just marginal improvement in relation to the ensemble-averaged performance and 95% confidence interval of the ensemble-averaged performance. The proposed simulation approach based on the stochastic Lagrangian model can effectively investigate the impact of AV control strategies on traffic oscillation, considering in particular the uncertainty of human driver behavior.

Sign in / Sign up

Export Citation Format

Share Document