| 引用本文: | 程国柱,程瑞,徐亮.公路小半径曲线段外侧车道路侧事故概率预测[J].哈尔滨工业大学学报,2021,53(3):178.DOI:10.11918/201912094 |
| CHENG Guozhu,CHENG Rui,XU Liang.Probabilistic prediction of roadside accidents in outer lane of small radius curve sections of highways[J].Journal of Harbin Institute of Technology,2021,53(3):178.DOI:10.11918/201912094 |
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| 摘要: |
| 为降低小半径曲线段路侧事故概率,选取道路线形指标(圆曲线半径、硬路肩宽度、纵坡坡度、超高横坡度、圆曲线加宽)、路面状况(路面附着系数)、交通特性(车速、车型)8个路侧事故风险因素进行PC-crash仿真试验,收集12 800组数据. 采用CHAID(Chi-squared automatic interaction detection)决策树技术识别了影响路侧事故发生的显著性风险因素,探讨各种风险因素之间交互作用对路侧事故的影响,并利用贝叶斯网络构建了路侧事故概率预测模型. 根据概率模型预测结果,提出了路侧事故多发路段判别方法,并进行案例验证. 研究结果表明:对路侧事故影响程度最大的显著性风险因素为车速,其次为圆曲线半径、车型、路面附着系数和硬路肩宽度；当80 km/h
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| 关键词: 交通工程 概率预测 PC-crash CHAID决策树 贝叶斯网络 路侧事故 |
| DOI：10.11918/201912094 |
| 分类号:U491.31 |
| 文献标识码:A |
| 基金项目:中央高校基本科研业务费专项资金(2572019AB26); 国家自然科学基金面上项目(51778063); 教育部人文社会科学研究规划基金(18YJAZH009); 重庆市交通运输工程重点试验室开放基金(2018TE05) |
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| Probabilistic prediction of roadside accidents in outer lane of small radius curve sections of highways |
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CHENG Guozhu1,2,CHENG Rui1,XU Liang3
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(1. School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China; 2. Chongqing Key Laboratory of Traffic & Transportation (Chongqing Jiaotong University), Chongqing 404100, China; 3. School of Civil Engineering, Changchun Institute of Technology, Changchun 130012, China)
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| Abstract: |
| To reduce the probability of roadside accidents in small curve sections of highways, eight roadside accident risk factors including road geometric design indexes (horizontal curve radius, hard shoulder width, longitudinal slope, superelevation slope, and widen value of curve), pavement condition (adhesion coefficient), and traffic characteristics (running speed and vehicle type) were chosen to carry out PC-crash simulation test, and a total of 12 800 accident data sets were collected. Chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors, and the comprehensive influence of the interaction of various factors on roadside accidents was discussed. These factors were then chosen as predictors of probability of roadside accidents in Bayesian network analysis to establish the probabilistic prediction model of roadside accidents. Finally, according to probabilistic prediction results, the identification method for roadside accidents black spots was proposed and verified through tests. Results show that running speed had the greatest effect on the occurrence of roadside accidents, followed by horizontal curve radius, vehicle type, adhesion coefficient, and hard shoulder width. When 80 km/h
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| Key words: traffic engineering probabilistic prediction PC-crash CHAID decision tree Bayesian network roadside accidents |
