School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
Accid Anal Prev. 2023 Oct;191:107205. doi: 10.1016/j.aap.2023.107205. Epub 2023 Jul 4.
Secondary crashes occur within the spatial and temporal impact area of primary crashes, resulting in traffic delays and safety problems. While most existing studies focus on the likelihood of secondary crashes, predicting the spatio-temporal location of secondary crashes could offer valuable insights for implementing prevention strategies. This includes guiding the deployment of emergency response measures and determining appropriate speed limits. The main objective of this study is to develop a prediction method for the spatial and temporal locations of secondary crashes. A hybrid deep learning model SSAE-LSTM is proposed by combining stacked sparse auto-encoder (SSAE) and long short-term memory network (LSTM). Traffic and crash data on the California I-880 highway covering the period of 2017-2021 are collected. The identification of secondary crashes is performed by the speed contour map method. The time and distance gaps between primary and secondary crashes are modeled using multiple 5-minute interval traffic variables as inputs. Multiple models are developed for benchmarking purposes, including PCA-LSTM, which incorporates principal component analysis (PCA) and LSTM, SSAE-SVM, which incorporates SSAE and support vector machine (SVM), and back propagation neural network (BPNN). The performance comparison indicates that the hybrid SSAE-LSTM model outperforms the other models in terms of both spatial and temporal prediction. In particular, SSAE4-LSTM1 (with 4 SSAE layers and 1 LSTM layer) demonstrates superior spatial prediction performance, while SSAE4-LSTM2 (with 4 SSAE layers and 2 LSTM layers) excels in temporal prediction. A joint spatio-temporal evaluation is also conducted to measure the overall accuracy of the optimal models over different permitted spatio-temporal ranges. Finally, practical suggestions are provided for secondary crash prevention.
二次碰撞发生在一次碰撞的空间和时间影响区域内，导致交通延误和安全问题。虽然大多数现有研究都集中在二次碰撞的可能性上，但预测二次碰撞的时空位置可以为实施预防策略提供有价值的见解。这包括指导应急响应措施的部署和确定适当的限速。本研究的主要目的是开发一种用于预测二次碰撞时空位置的方法。通过将堆叠稀疏自动编码器（SSAE）和长短期记忆网络（LSTM）相结合，提出了一种混合深度学习模型 SSAE-LSTM。收集了 2017-2021 年期间加利福尼亚州 I-880 高速公路的交通和碰撞数据。通过速度等高线图方法识别二次碰撞。使用多个 5 分钟间隔的交通变量作为输入，对主、二次碰撞之间的时间和距离间隔进行建模。为了进行基准测试，开发了多个模型，包括 PCA-LSTM，它结合了主成分分析（PCA）和 LSTM、SSAE-SVM，它结合了 SSAE 和支持向量机（SVM）以及反向传播神经网络（BPNN）。性能比较表明，混合 SSAE-LSTM 模型在时空预测方面均优于其他模型。特别是 SSAE4-LSTM1（具有 4 个 SSAE 层和 1 个 LSTM 层）在空间预测性能方面表现出色，而 SSAE4-LSTM2（具有 4 个 SSAE 层和 2 个 LSTM 层）在时间预测方面表现出色。还进行了联合时空评估，以衡量不同允许时空范围内最优模型的整体准确性。最后，为二次碰撞预防提供了实用建议。
