Cognition simulation and traffic flow characteristics analysis indriving distraction(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2018年02期
- Page:
- 87-96
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Cognition simulation and traffic flow characteristics analysis indriving distraction
- Author(s):
- ZHU Tong; HU Yueqi; ZHU Shihui; LIU Wei; LIU Haoxue
- （1. Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of 、Communication, Changan University， Xian 710064， Shaanxi, China; 2. School of Automobile，Changan University， Xian 710064， Shaanxi, China）
- Keywords:
- traffic engineering; traffic safety; driver; distraction; cognitive structure; ACTR; traffic flow
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to investigate the changes of drivers state and its effects on the traffic flow under the condition of executing vehicle secondary tasks, first of all, based on adaptive control of thoughtrational （ACTR） cognitive structure and DistractR software platform, four types of secondary tasks were established, and the distraction states of different drivers and the state of nonsecondary tasks were simulated cognitively. Obtaining the ratio of the drivers execution time and the distracting time during the performance of four types secondary tasks, and the driver distraction state database was established as the basic data for traffic flow simulation under secondary tasks conditions; then, based on the cellular automata traffic flow STCA model, the rules of deceleration were modified and the traffic flow simulation model considering the effects of vehicle secondary task was established. The joint simulation of cellular automata model and cognitive model was realized through model data exchange. Finally, cellular automata simulation was used to simulate the traffic flow situations of 0, 10%, 20% drivers during executing vehicle secondary tasks when they are driving with the four task types randomly selected. Experimental data show that the joint simulation of cellular automata and cognitive model can reflect the psychological difference of drivers and variation characteristics of traffic flow; the model modifies the rules of original cellular automata deceleration, and parts of model parameters can be obtained by calling database of drivers distraction states. Additionally, the vehicle secondary task causes obvious effects on the traffic flow to make the road maximize traffic capacity get reduced. The maximize traffic capacity reduced about 23.5% and 40.7% when 10%, 20% drivers executing secondary tasks, with the phenomena of increasing regional congestion and exacerbating queuing. Execution of vehicle secondary task not only causes the impact on the driving safety, but when drivers in the attentiondistraction state reach a certain proportion, the overall state of the traffic flow is also significantly effected. 1 tab, 8 figs, 27 refs.
Last Update: 2018-04-03