| 基于交通流稳定性系数的高速公路交通事故实时风险预测 |
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| 引用本文： | 刘星良,单珏,刘唐志,饶畅,刘通.基于交通流稳定性系数的高速公路交通事故实时风险预测[J].交通信息与安全,2022,40(4):71-81. |
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| 作者姓名： | 刘星良 单珏 刘唐志 饶畅 刘通 |
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| 作者单位： | 重庆交通大学交通运输学院 重庆 400064 |
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| 基金项目： | 国家自然科学基金项目52172341重庆市教育委员会项目KJQN202100718重庆市高校创新研究群体项目CXQT21022 |
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| 摘 要： | 预测交通事故实时风险时，存在大量指标变量，导致数据难以采集，不仅不利于构建预测模型，且带来的过拟合问题会降低模型预测可靠性。为了减少预测指标数量，提升预测模型可用性，降低预测模型过拟合影响，构建具有可解释性的2种交通流稳定性系数以简化指标集，分别为纵向交通流稳定系数和横向交通流稳定系数。采集西安市G3001高速公路交通事故与交通流历史数据，选用支持向量机、随机森林、Logistic回归模型，分别构建高速公路交通事故实时风险预测模型。通过改进的GI指数评估交通流稳定性系数的显著性，以检验其有效性；通过指标集在训练与测试数据中的预测精度、AUC值差异评估交通流稳定性系数对降低预测模型过拟合的作用，并通过训练耗时评估模型的计算效率，以检验新方法的可靠性。研究结果表明:2种交通流稳定性系数对应的改进GI指数分别为0.952和0.922，显著大于其他受试指标，与交通事故实时风险显著相关。在3种预测模型中，包含2种交通流稳定性系数的简化指标集在训练和测试数据中的预测精度分别为91.1%和90.5%，与完整指标集相近。2种指标集在训练与测试数据中的平均预测精度差异分别为0.69%和4.87%；平均AUC值差异分别为1.61%和5.87%；平均训练时间下降了15.2%。交通流稳定性系数大幅提高了预测模型的可靠性，同时显著提升了模型的计算效率。
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| 关 键 词： | 交通安全 交通事故 交通流稳定性系数 实时风险预测 可靠性 |
| 收稿时间： | 2022-04-22 |
Real-time Forecast Models for Traffic Accidents on Expressways Using Stability Coefficients of Traffic Flow |
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| Affiliation: | College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400064, China |
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| Abstract: | Real-time forecast models for traffic accidents requires a large number of variables, which causes difficulties in data collection and decreases reliability of the model due to overfitting. Two interpretable variables, vertical and horizontal stability coefficients of traffic flow, are proposed to simplify the set of variables, which can facilitate the implementation of forecast models for traffic accidents and reduce the effects of overfitting. Three algorithms including support vector machine, random forest, and logistic regression are selected to develop real-time forecast models for traffic accidents on expressways, respectively. The experiments are conducted based on data of traffic accidents and historical traffic flow collected from the expressway G3001 in the city of Xi'an. In addition, the improved GI index is used to evaluate the significance of the proposed two stability coefficients of traffic flow. The effects of the two proposed coefficients on reducing overfitting is verified through comparing accuracies and AUC values of the set of variables in the test and training data.Besides, the computational efficiency is evaluated by the training time to verify the reliability of the developed models with the two coefficients. The results show that the improved GI indices of the models with horizontal and vertical stability coefficients of traffic flow are 0.952 and 0.922, respectively, which indicates that the proposed two coefficients are more significant for forecasting accidents on expressways than other variables. In the three models, the simplified set of variables based on the two coefficientshas prediction accuracy of 91.1% and 90.5%, respectively, in training and test data, which is similar to the original set of variables. The differences of average prediction accuracy between the simplified set of variables and the original set of variables are 0.69% and 4.87%, respectively. The difference of average AUC values between the two sets of variables are 1.61% and 5.87%, respectively. The average time cost of model training with the simplified set of variables decreases by 15.2%. Thus, the two proposed stability coefficients of traffic flow can improve both the reliability and the computational efficiency of the models. |
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