Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Shangyuancun, Haidian District, Beijing, China.
National Demonstration Center for Experimental Traffic and Transportation Education, School of Traffic and Transportation, Beijing Jiaotong University, Shangyuancun, Haidian District, Beijing, China.
PLoS One. 2019 Apr 5;14(4):e0214866. doi: 10.1371/journal.pone.0214866. eCollection 2019.
Conventional traffic crash analyzing methods focus on identifying the relationship between traffic crash outcomes and impact risk factors and explaining the effects of risk factors, which ignore the changes of roadway systems and can lead to inaccurate results in traffic crash predictions. To address this issue, an innovative two-step method is proposed and a support vector regression (SVR) model is formulated into state-space model (SSM) framework for traffic crash prediction. The SSM was developed in the first step to identify the dynamic evolution process of the roadway systems that are caused by the changes of traffic flow and predict the changes of impact factors in roadway systems. Using the predicted impact factors, the SVR model was incorporated in the second step to perform the traffic crash prediction. A five-year dataset that obtained from 1152 roadway segments in Tennessee was employed to validate the model effectiveness. The proposed models result in an average prediction MAPE of 7.59%, a MAE of 0.11, and a RMSD of 0.32. For the performance comparison, a SVR model and a multivariate negative binomial (MVNB) model were developed to do the same task. The results show that the proposed model has superior performances in terms of prediction accuracy compared to the SVR and MVNB models. Compared to the SVR and MVNB models, the benefit of incorporating a state-space model to identify the changes of roadway systems is significant evident in the proposed models for all crash types, and the prediction accuracy that measured by MAPE can be improved by 4.360% and 6.445% on average, respectively. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Furthermore, the results show that the proposed model has a more precise and synchronized response behavior to the high variations of the observed data, especially for the phenomenon of extra zeros.
传统的交通事故分析方法侧重于识别交通事故结果与影响风险因素之间的关系，并解释风险因素的影响，而忽略了道路系统的变化，这可能导致交通事故预测结果不准确。针对这一问题，提出了一种创新的两步法，并将支持向量回归（SVR）模型构建到状态空间模型（SSM）框架中，用于交通事故预测。首先，开发 SSM 以识别交通流量变化引起的道路系统动态演变过程，并预测道路系统中影响因素的变化。然后，使用预测的影响因素，在第二步中纳入 SVR 模型进行交通事故预测。采用来自田纳西州 1152 个路段的五年数据集来验证模型的有效性。所提出的模型的平均预测 MAPE 为 7.59%，MAE 为 0.11，RMSD 为 0.32。为了进行性能比较，还开发了 SVR 模型和多元负二项式（MVNB）模型来执行相同的任务。结果表明，与 SVR 和 MVNB 模型相比，所提出的模型在预测精度方面具有更好的性能。与 SVR 和 MVNB 模型相比，将状态空间模型纳入识别道路系统变化的优势在所有类型的事故中都明显明显，所提出的模型的预测精度可以平均提高 4.360%和 6.445%。除了提高精度之外，所提出的模型更稳健，预测结果保持更平滑的模式。此外，结果表明，所提出的模型对观测数据的高变化具有更精确和同步的响应行为，尤其是对于额外零值的现象。
