基于随机生存森林的城市快速路交通事件持续时间预测研究
Investigation of the Urban Expressway Traffic Incident Duration Prediction Based on Random Survival Forests
投稿时间:2016-09-21  修订日期:2017-06-21
DOI:10.11908/j.issn.0253-374x.2017.09.008     稿件编号:    中图分类号:TP311; TP391
 
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中文摘要
      交通事故、抛锚等交通事件对城市快速路的运行影响极大;准确预测交通事件的持续时间可有助于主动交通管理措施的实施,提升通行效率与安全。本研究采用随机生存森林模型开展交通事件持续时间分析,以克服传统决策树模型易过度拟合和传统生存分析需限制性假定及识别协变量交互作用的缺陷。研究基于上海城市快速路网交通事件数据,并结合道路几何线形、交通运行、天气状况等数据。原始数据库分为训练数据(80%)和测试数据(20%)。分析结果表明事件类型、路段长度、发生地点、剩余车道数、交通流量等变量对交通事件持续时间有显著影响;影响时间预测准确率结果表明随机生存森林模型预测精度显著优于随机森林的预测精度。
英文摘要
      Traffic Incidents such as crashes and vehicle break down have significant impacts on urban expressway operation. With a well-developed incident duration prediction model, the roadside service and operational efficiency of urban expressways could be improved. In this study, instead of utilizing frequently adopted decision tree and survival analysis method to establish the incident duration analysis model, random survival forests model is employed. The random survival forests model can not only overcome the disadvantage of over-fitting problems of decision tree algorithm, but also break through the limitation of restrictive assumptions and solve the problem of identifying interaction of the covariates in traditional survival analysis. This study is conducted based on traffic incident data of Shanghai urban expressways. The traffic incident data is combined with the road geometry data, traffic operation data, and weather condition information; where 80% data is used as training dataset and the remaining 20% as testing dataset. The results show that incident type, length of road, location, remained lane number and traffic volume have significant impacts on incident duration; and the prediction results based on testing dataset indicate that the random survival forests modelis more accurate than random forests model.
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