Journal of System Simulation
Abstract
Abstract: In order to analyze the influence of speed control strategy of autonomous vehicle on the operation characteristics of traffic flow, a deterministic decision-making model for intersections with artificially driven vehicles considering the driver's influence on the acquisition of driving information is constructed. An automatic driving speed control strategy considering the influence of the speed of preceding vehicle is proposed, and the continuous Cellular Automata update rules for signalized intersections are constructed respectively. By introducing the different penetration rates of automatic driving, road saturation and control area length parameters, the influence of CAV speed control strategy on the traffic flow characteristics of signalized intersections is studied. The results show that the autonomous vehicle can significantly improve the traffic capacity of intersection, and the delay of traffic flow through the intersection area is significantly reduced. The implementation effect of the speed control strategy is also affected by the length of control area, which shows that following the increase of length of control area, the average vehicle delay gradually decreases and stabilizes.
Recommended Citation
Zhang, Jianxu; Hu, Shuai; and Jin, Hongyi
(2022)
"Modeling of Traffic Flow Velocity Control Strategy for Human-machine Mixed Driving at Signalized Intersections,"
Journal of System Simulation: Vol. 34:
Iss.
8, Article 5.
DOI: 10.16182/j.issn1004731x.joss.22-0307
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss8/5
First Page
1697
Revised Date
2022-05-18
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.22-0307
Last Page
1709
CLC
TP391
Recommended Citation
Jianxu Zhang, Shuai Hu, Hongyi Jin. Modeling of Traffic Flow Velocity Control Strategy for Human-machine Mixed Driving at Signalized Intersections[J]. Journal of System Simulation, 2022, 34(8): 1697-1709.
DOI
10.16182/j.issn1004731x.joss.22-0307
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