基于基因表达式编程的高架道路事故实时预测

Real⁃time crash prediction of elevated expressway based on gene expression programming algorithm

为在城市高架道路场景下有效预测交通事故,基于上海市延安高架道路交通流和事故数据,利用附加精英基因库和灭绝机制的改进型基因表达式编程算法,提出了高架道路事故预测经验公式。通过与传统建模方法的结果进行对比,验证了经验公式的预测精度和可理解性;在不进行重新训练和标定的前提下直接应用经验公式对其他高架道路的事故数据集进行预测,验证了其可移植性。结果表明:在延安高架道路数据集上,经验公式的预测性能较传统Logistics回归有较大提升,受试者工作特征曲线面积指标和F1-score指标达到与人工神经网络模型一致的水平,能正确识别74%的事故。经验公式在杭州市上塘高架道路数据集上的良好性能表明其具有基本的可移植性。综上,基因表达式编程算法针对事故风险预测问题兼顾了高精度和可理解性,并表现出可移植性,有助于建设低成本、高效率的事故预测系统。

In order to effectively predict crash on elevated expressway,taking Yan'an elevated expressway in Shanghai as the research object,based on its traffic flow and crash data,an improved Gene Expression Programming algorithm with additional elite gene bank and extinction mechanism was applied to dig out‘Crash Prediction Empirical Formula’.The prediction accuracy and interpretability of the empirical formula were verified by comparing with the results of machine learning and statistical analysis.The crash of another expressway was predicted by empirical formula without retraining and calibration,and the portability of the empirical formula was verified.The research results indicated that the prediction performance of the empirical formula on the Yan'an elevated expressway dataset is significantly improved compared with the traditional Logistics regression,and the receiver operating characteristic curve area and F1-score indexes are consistent with the artificial neural network model,identifying 74%of the crashes correctly.The good performance of the empirical formula on Hangzhou Shangtang elevated expressway dataset shows that the empirical formula has basic portability.In conclusion,the gene expression programming algorithm considers both high accuracy and interpretability for the crash risk prediction problem,and shows portability,which is helpful to establish a low-cost and efficient crash prediction system.