Traffic Density Based Travel-Time Prediction With GCN-LSTM
Traffic Density Based Travel-Time Prediction With GCN-LSTM
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DOI：
10.1109/itsc55140.2022.9922259
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Hiroki Katayama;Shohei Yasuda;T. Fuse
中科院分区：
文献类型：
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作者：
Hiroki Katayama;Shohei Yasuda;T. Fuse
近年来，由于探测车数据等各种观测数据的广泛存在，数据驱动的行程时间预测方法得到了积极的发展。在已有的大规模网络研究中，速度的时间序列数据被用作输入，这些数据可以直接从探测车数据中估计出来。然而，在自由流动状态下，速度的变化并不太依赖于车辆的数量。因此，它不能准确地表示该政权的交通状态。以交通密度作为输入，描述从自由流状态到拥堵状态的交通状态，以期在拥堵发生前更有效地了解自由流状态下交通状态的波动规律，为拥堵的早期检测做出贡献。在这项研究中，我们提出了一种新的旅行时间预测方法，该方法使用图卷积网络和长短期记忆的组合模型，并以空间内插密度为输入。使用日本阪神地区的实际观测数据进行的实证验证表明，密度输入在实现交通拥堵的早期检测和提高行程时间预测的准确性方面优于速度输入。
In recent years, data-driven travel-time prediction methods have been actively developed owing to the widespread availability of various observation data such as probe vehicle data. In most existing studies on large-scale networks, the time-series data of speed, which can be directly estimated from probe vehicle data, are used as input. However, in a free-flow regime, the change in speed does not depend much on the number of vehicles. Therefore, it cannot accurately represent the traffic states of the regime. Using traffic density as input, which can describe traffic states from the free-flow regime to the congested-flow regime, we expect to learn the fluctuation pattern of traffic states more efficiently in the free-flow regime before the occurrence of congestion and contribute to the early detection of congestion. In this study, we propose a new methodology for travel-time prediction using a combined model of graph convolutional networks and long short-term memory with spatially interpolated density as input. Empirical validation using real observation data from the Hanshin region, Japan, shows that the density input is superior to the speed input in achieving the early detection of traffic congestion and improving the accuracy of travel-time prediction.