A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data
A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data
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DOI：
10.1016/j.aap.2018.10.015
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发表时间：
2019-01-01
影响因子：
5.9
通讯作者：
Ukkusuri, Satish V.
中科院分区：
文献类型：
--
作者：
Bao, Jie;Liu, Pan;Ukkusuri, Satish V.
本研究的主要目标是研究深度学习方法如何通过利用多源数据集来帮助城市范围内的短期碰撞风险预测。本研究使用从纽约市的曼哈顿收集的数据来说明这一过程。收集了以下多个数据集：碰撞数据、大型出租车GPS数据、道路网络属性、土地利用特征、人口数据和天气数据。提出了一种时空卷积长短期记忆网络（STCL-Net）用于预测城市范围内的短期碰撞风险。总共进行了九个预测任务并进行了比较，包括分别具有8 x 3、15 x 5和30 x 10网格的每周、每日和每小时模型。结果表明，所提出的模型的预测性能下降的时空分辨率的预测任务的增加。此外，四个常用的计量经济学模型，和四个国家的最先进的机器学习模型被选为基准方法与建议的STCL网络的所有碰撞风险预测任务进行比较。对比分析表明，在一般情况下，建议STCL-Net优于基准方法的不同的碰撞风险预测任务，在更高的预测准确率和更低的误报率。实验结果表明，本文提出的时空深度学习方法能够更好地捕捉城市范围内短期碰撞风险预测的时空特征。此外，比较分析还表明，计量经济学模型在每周碰撞风险预测任务中的表现优于机器学习模型，而在日常碰撞风险预测任务中，它们的结果比机器学习模型更差。研究结果可以指导交通安全工程师为不同的碰撞风险预测任务选择合适的方法。
The primary objective of this study is to investigate how the deep learning approach contributes to citywide short-term crash risk prediction by leveraging multi-source datasets. This study uses data collected from Manhattan in New York City to illustrate the procedure. The following multiple datasets are collected: crash data, large-scale taxi GPS data, road network attributes, land use features, population data and weather data. A spatiotemporal convolutional long short-term memory network (STCL-Net) is proposed for predicting the city-wide short-term crash risk. A total of nine prediction tasks are conducted and compared, including weekly, daily and hourly models with 8 x 3, 15 x 5 and 30 x 10 grids, respectively. The results suggest that the prediction performance of the proposed model decreases as the spatiotemporal resolution of prediction task increases. Moreover, four commonly-used econometric models, and four state-of-the-art machine-learning models are selected as benchmark methods to compare with the proposed STCL-Net for all the crash risk prediction tasks. The comparative analyses suggest that in general the proposed STCL-Net outperforms the benchmark methods for different crash risk prediction tasks in terms of higher prediction accuracy rate and lower false alarm rate. The results verify that the proposed spatiotemporal deep learning approach performs better at capturing the spatiotemporal characteristics for the citywide short-term crash risk prediction. In addition, the comparative analyses also reveal that econometric models perform better than machine-learning models in weekly crash risk prediction tasks, while they exhibit worse results than machine-learning models in daily crash risk prediction tasks. The results can potentially guide transportation safety engineers to select appropriate methods for different crash risk prediction tasks.