Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks
Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks
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DOI：
10.1109/bigdata47090.2019.9005995
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Sanchita Basak;A. Dubey;Bruno P. Leao
中科院分区：
文献类型：
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作者：
Sanchita Basak;A. Dubey;Bruno P. Leao
本文提出了一种数据驱动的方法，用于预测路段交通拥堵的传播，这是其邻近细分市场中拥塞的函数。过去，这个问题主要是通过对某些标准的物理现象进行建模的交通拥堵来解决的，而交通拥堵很难捕获这种动态和复杂系统的所有方式。尽管其他最近的作品重点是一次在整个网络上应用广义数据驱动技术，但它们通常忽略了交叉特征。相反，我们提出了一个相交级别连接的LSTM模型的整个城市整体，并提出了使用网络的预测来识别拥塞事件的机制。为了减少可能拥塞的搜索空间，我们利用了我们从过去的历史数据中学到的拥塞来源的邻近路段中的拥堵传播的可能性。我们验证了我们在美国纳什维尔的现实世界交通数据上的拥堵预测框架，并确定了任何拥塞来源的每个邻近细分市场的拥堵开始，平均精度为0.9269，平均召回率为0.9118，在十次拥塞事件中测试了0.9118。
This paper presents a data-driven approach for predicting the propagation of traffic congestion at road segments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events.