| 基于有限混合零截尾事故预测模型的事故多发段判别方法 |
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| 引用本文： | 陈英,黄中祥,刘洋.基于有限混合零截尾事故预测模型的事故多发段判别方法[J].中国公路学报,2022,35(8):331-340. |
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| 作者姓名： | 陈英 黄中祥 刘洋 |
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| 作者单位： | 1. 长沙理工大学 建筑学院, 湖南 长沙 410076;2. 长沙理工大学 交通运输工程学院, 湖南 长沙 410114 |
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| 基金项目： | 国家自然科学基金项目(52102407,51978082);湖南省自然科学基金项目(2021JJ30706);湖南省研究生科研创新项目(CX20210744) |
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| 摘 要： | 为了准确判别事故多发段,有针对性地提出安全应对措施以提升道路交通的安全水平,针对零值缺失交通事故数据并考虑其异质性特点,在单零截尾负二项(ZTNB)模型的基础上建立有限混合零截尾事故预测模型(FMZTNB)。应用R软件对单零截尾负二项模型中的参数进行估计,采用马尔科夫链蒙特卡洛算法(MCMC)对FMZTNB预测模型参数进行求解,并采用Gelman-Rubin收敛统计量对抽样结果进行检查。选择事故风险水平分别为低、中和高的9个路段,分别用2种模型对交通事故次数进行预测。综合观测到的事故次数和相应的事故预测模型结果,采用经验贝叶斯方法对事故相对多发段进行判别。最后采用事故次数一致性检验、判别点段一致性检验和排序一致性检验3种检验方式对判别结果对比分析。结果表明:基于事故率的事故相对多发段判别方法存在较大的不一致性,基于零截尾负二项预测模型的路段事故相对多发判别结果明显优于基于传统负二项预测模型的结果。整体上,基于有限混合零截尾事故预测模型的事故相对多发路段的判别结果高于基于单零截尾负二项分布模型的判别结果。
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| 关 键 词： | 交通工程 数据异质性 事故预测模型 有限混合零截尾模型 事故多发段判别 交通安全 |
| 收稿时间： | 2021-03-11 |
Identifying Hotspots Based on Finite-mixture Zero-truncated Negative Binomial Model |
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| CHEN Ying,HUANG Zhong-xiang,LIU Yang.Identifying Hotspots Based on Finite-mixture Zero-truncated Negative Binomial Model[J].China Journal of Highway and Transport,2022,35(8):331-340. |
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| Authors: | CHEN Ying HUANG Zhong-xiang LIU Yang |
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| Affiliation: | 1. College of Architecture, Changsha University of Science & Technology, Changsha 410076, Hunan, China;2. School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China |
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| Abstract: | To accurately identify hotspots and propose safety countermeasures to improve the level of traffic safety,this paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure based on a previously developed zero-truncated negative binomial (ZTNB) model,considering the heterogeneity of zero-truncated traffic accident data.The R software package and Markov chain Monte Carlo (MCMC) method were used to estimate the parameters of the ZTNB and FMZTNB models,respectively.Further,Gelman-Rubin convergence statistics were used to verify the MCMC process.A comparison of nine sites with three different safety levels using the two models indicates that the proposed FMZTNB model is more accurate in predicting the number of crashes.This study further identified hotspots with different percentages of high risk using the crash rate,ZTNB,and FMZTNB models.The identification results were evaluated using three consistency tests:accident number,discriminant point,and rank consistency tests.The analyses results reveal that crash-rate-based hotspot identification is not reliable;ZTNB-based models provide significantly better results in identifying risky roadway segments.In general,the FMZTNB model outperformed the ZTNB model in ranking road segments. |
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| Keywords: | traffic safety data heterogeneity accident prediction model finite-mixture zero-truncated model hotspot identification traffic safety |
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