Modelling driver yielding decision at unmarked roadway(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2020年3期
- Page:
- 84-90
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Modelling driver yielding decision at unmarked roadway
- Author(s):
- -
- CHEN Peng, YU Jingliu, XIE Jingmin
- Keywords:
- traffic engineering; yielding decision; cloud model; driver behavior; unmarked roadway; rough set
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to consider driver cognitive uncertainty and obtain certainty yielding decision rules in the study of driver yielding decision, a method for driver yielding decision analysis at unmarked roadway based on cloud model and rough set was proposed. Firstly, the grading concepts of the factors affecting yielding decision such as distance and speed were divided, and the corresponding data sequence were established, then the reverse cloud generator was used to extract the digital features that characterize the grading concepts of the influencing factors, and then the membership concept discriminant method based on conditional cloud generator was used for the discretization of data such as distance and speed, and then the driver yielding decision table at unmarked roadway was established. Then, attribute reduction based on discernibility matrix and value reduction based on induction in the rough set theory were applied to reduce the decision table and extract the driver yielding decision rules at unmarked roadway. After the reduction, the conditional attributes were the distance between the approaching vehicle and the pedestrian, the speed of the approaching vehicle and the speed of the pedestrian. Twentythree driver yielding decision rules at the unmarked roadway were obtained, including seventeen certainty rules and six uncertainty rules. The meaning of certainty and uncertainty rules was illustrated. Finally, the proposed method was compared with the decision tree and Logistic regression method in the existing research by using the prediction accuracy rate and the area under the ROC curve. The results show that the prediction accuracy of the proposed method is 92.2%, which is higher than those of the two comparison methods by 3.9% and 1.3% respectively, and the area under the ROC curve of the proposed method is 0.968, which is higher than those of the two comparison methods by 1.1% and 3.2% respectively. The proposed method has good prediction performance and can obtain the simple and intuitive driver yielding decision rules at unmarked roadway, which can lay a foundation for traffic safety simulation. 4 tabs, 4 figs, 26 refs.
Last Update: 2020-06-03