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- Yang Bian, Kun Liang, Xiaohua Zhao, Haijian Li, and Liping Yang.
- Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China. Electronic address: bianyang@bjut.edu.cn.
- Accid Anal Prev. 2020 May 1; 139: 105498.
AbstractCrosswalk markings are a type of facility installed at the vehicle-pedestrian interaction locations and the function is to warn drivers to watch out for pedestrians crossing the street and improve safety for pedestrians. In Beijing, a type of new-designed crosswalk markings in China (NCMC) was installed. However, evaluating the effectiveness of this type of crosswalk markings was not conducted. Accordingly, the objective of this paper is to evaluate the effectiveness of this type of new-designed crosswalk markings. During the evaluation process, the vehicle-pedestrian interaction was considered; standard crosswalk markings in China (SCMC) were taken as a control group. In addition, empirical data were collected from a driving simulator, and nine evaluating indicators representing vehicle operating data, drivers' maneuvering data and drivers' subjective evaluation were proposed. In order to combine nine indicators, a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used in this study to achieve the premium degrees of these two types of crosswalk markings. The evaluation result showed that, for intersections with high or low pedestrian flow, the comprehensive effectiveness and influences on drivers' driving behaviors with presence of NCMC were better than those with presence of SCMC, no matter where vehicle-pedestrian interactions occurred. For intersections with no pedestrians, the comprehensive effectiveness and influences on drivers' driving behaviors with presence of NCMC were worse than those with presence of SCMC, no matter where vehicle-pedestrian interaction occurred. These results may provide references for facility installing and future development of standards.Copyright © 2020 Elsevier Ltd. All rights reserved.
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