恶劣天气下高速公路实时事故风险预测模型

Real time crash risk prediction model on freeways under nasty weather conditions

先提取了美国加州I-880N高速公路上一段长为23km路段的实时交通流数据、事故数据和气象数据。然后采用Logistic模型建立了基于交通流数据和气象数据的事故风险预测模型。研究结果表明:天气条件对事故风险有显著影响,在雨天和雾天的比值比(Odds ratios)分别为6.4和4.4时,事故风险性分别提高了5.4和3.4倍。最后建立了不含天气参数的事故风险预测模型,结果表明:含有天气参数的实时事故风险模型预测精度为71.7%,不含天气参数的模型预测精度为66.5%,表明天气条件可以显著提高实时事故风险模型的预测精度。

The real time traffic flow data, crash data, crash data and weather condition data were extracted and collected from a 23-kin segment of freeway 1-880 N in the state of California of the United States. A real time crash risk prediction model on freeways was built based on the traffic flow data and weather data using the Logistic regression model. The data analysis results showed that the weather condition variables had significant impact on the likelihood of crash occurrence on freeways. The odds ratios for rainy and foggy days were 6.4 and 4.4 respectively, indicating that the crash risks for rainy and foggy days were 5. 4 and 3. 4 times respectively higher than that for clear days. A Logistic regression model was also built based on only real-time traffic flow data for the comparison purpose. The analysis results show that the prediction accuracy of the model with weather wariables was 71.7%, while it was 66.5% without these variables. The weater condition wtriables significantly enhance the prediction accaracy of the real-time crash risk prediction model on freeway.