Assessing Weather, Environment, and Loop Data for Real-Time Freeway Incident Prediction:
Assessing Weather, Environment, and Loop Data for Real-Time Freeway Incident Prediction:
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评估天气、环境和环路数据以进行实时高速公路事故预测：
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
K. Balke
中科院分区：
文献类型：
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作者：
P. Songchitruksa;K. Balke
天气、环境和环路数据条件是实时高速公路事件预测的有前途的指标。通过使用天气和环境数据来预测选定事件类型的可能性的能力被检验。对环路检测器数据进行分析，以寻找对车道内事件预测有用的条件。用德克萨斯州奥斯汀选定的高速公路上的数据估计了非嵌套和嵌套的多项Logit模型。估计结果表明，能见度、时间和照明条件等因素是事故类型的显著决定因素，而5分钟平均占有率和车速变异系数是车道内高速公路事故的强烈预测因素。
Weather, environment, and loop data conditions are promising indicators for real-time freeway incident prediction. The ability to predict the likelihood of selected incident types by using weather and environment data was examined. Loop detector data were analyzed for conditions useful for in-lane incident prediction. Nonnested and nested multinomial logit models were estimated with data from selected freeways in Austin, Texas. The estimation results revealed that factors such as visibility, time of day, and lighting condition are significant determinants of incident type, whereas 5-min average occupancy and coefficient of variation in speed are strong predictors of in-lane freeway accidents.