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首页> 外文期刊>Accident Analysis & Prevention >A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions
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A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions

机译：基于短期内存的基于碰撞检测的框架，具有不同时间分辨率的交通数据的高速公路

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摘要

Traffic crash detection is a major component of intelligent transportation systems. It can explore inner relationships between traffic conditions and crash risk, prevent potential crashes, and improve road safety. However, there exist some limitations in current studies on crash detection: (1) The commonly used machine learning methods cannot simulate the evolving transitions of traffic conditions before crash occurrences; (2) Current models collected traffic data of only one temporal resolution, which cannot fully represent traffic trends in different time intervals. Therefore, this study proposes a Long short-term memory (LSTM) based framework considering traffic data of different temporal resolutions (LSTMDTR) for crash detection. LSTM is an effective deep learning method to capture the long-term dependency and dynamic transitions of pre-crash conditions. Three LSTM networks considering traffic data of different temporal resolutions are constructed, which can comprehensively indicate traffic variations in different time intervals. A fully-connected layer is used to combine the outputs of three LSTM networks, and a dropout layer is used to reduce overfitting and improve prediction performance. The LSTMDTR model is implemented on datasets of 1880-N and 1805-N in California, America. The results indicate that the LSTMDTR model can obtain satisfactory performance on crash detection, with the highest crash accuracy of 70.43 %. LSTMDTR models constructed on one freeway can be transferred to other similar freeways, with 65.12 % of crash accuracy on transferability. Compared with machine learning methods and LSTM models with one or two temporal resolutions, the LSTMDTR model has been validated to perform better on crash detection and transferability. A proper number of neurons in the LSTMDTR model should be determined in real applications considering acceptable detection performance and computation time. The dropout technique can reduce overfitting and improve the generalization ability of the LSTMDTR model, increasing crash accuracy from 64.49 % to 70.43 %.
机译：交通崩溃检测是智能交通系统的主要组成部分。它可以探索交通条件和碰撞风险之间的内部关系，防止潜在的崩溃，并提高道路安全性。但是，目前关于碰撞检测的研究中存在一些局限性：（1）常用的机器学习方法无法模拟碰撞发生前的交通状况的演变转换; （2）当前模型收集了仅一个时间分辨率的流量数据，这不能以不同的时间间隔完全代表交通趋势。因此，考虑用于崩溃检测的不同时间分辨率（LSTMDTR）的流量数据，提出了一种基于短期的内存（LSTM）框架。 LSTM是一种有效的深度学习方法，可捕获长期依赖性和动态转换的预碰撞条件。考虑不同时间分辨率的交通数据的三个LSTM网络是构造的，这可以全面地指示不同的时间间隔的交通变化。完全连接的层用于组合三个LSTM网络的输出，并且辍学层用于减少过度拟合并提高预测性能。 LSTMDTR模型在美国加利福尼亚州1880-N和1805-N的数据集上实施。结果表明，LSTMDTR模型可以在碰撞检测中获得令人满意的性能，最高碰撞精度为70.43％。在一个高速公路上建造的LSTMDTR模型可以转移到其他类似的高速公路，在可转移性上有65.12％的碰撞准确性。与具有一两个时间分辨率的机器学习方法和LSTM型号相比，LSTMDTR模型已被验证，以更好地执行碰撞检测和可转换性。考虑到可接受的检测性能和计算时间，应在实际应用中确定LSTMDTR模型中的适当数量的神经元。辍学技术可以减少过度拟合和提高LSTMDTR模型的泛化能力，从64.49％增加到70.43％的崩溃精度。

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