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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Spatial–Temporal Deep Tensor Neural Networks for Large-Scale Urban Network Speed Prediction
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Spatial–Temporal Deep Tensor Neural Networks for Large-Scale Urban Network Speed Prediction

机译：用于大型城市网络速度预测的空间颞深度张力神经网络

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摘要

Real-time traffic speed prediction is an essential component of intelligent transportation systems applications on large-scale urban networks, e.g., proactive traffic management, advanced information provision, and prompt incident response. The family of traffic prediction models (e.g., convolutional neural networks) based on multi-detector speed diagrams in the time-space plane has been one of the most frequently used approaches for individual roads and the entire network. However, the predefined stacking sequence of traffic detectors along the spatial dimension of the speed diagram has a significant influence on the prediction performance, which makes network-wide speed prediction more challenging. To tackle the above challenge and better capture complicated traffic dynamics, we propose a novel speed prediction approach, named spatial-temporal deep tensor neural networks (ST-DTNN), for a large-scale urban network with mixed road types. Spatial and temporal dependencies of different road segments are simultaneously taken into account to improve the network-wide prediction accuracy. A scalable deep tensor is constructed for the ST-DTNN to eliminate the potentially negative impact caused by the manually stacking sequence of speed time series collected at different locations. Multi-step ahead traffic speeds can be simultaneously predicted based on probe data for a real-world large-scale urban network with hundreds of detectors installed on freeways, highways, and major/minor arterials. The results demonstrate the capability and effectiveness of the proposed ST-DTNN approach. Compared with the benchmark models, the ST-DTNN performs higher prediction accuracy during either peak or off-peak periods within an acceptable training time and has more stable prediction performance on the spatial scale. The proposed approach can be extended to develop network-wide traffic state monitoring, optimize routing in navigation services, and support congestion mitigation.
机译：实时交通速度预测是大型城市网络上智能交通系统应用的重要组成部分，例如，主动交通管理，高级信息提供和及时入射响应。基于时空平面中的多检测器速度图的交通预测模型（例如，卷积神经网络）是各个道路和整个网络的最常用方法之一。然而，沿速度图的空间维度的交通检测器的预定义堆叠序列对预测性能有显着影响，这使得网络范围的速度预测更具挑战性。为了解决上述挑战和更好的捕获复杂的交通动态，我们提出了一种新颖的速度预测方法，名为空间颞张张于神经网络（ST-DTNN），用于具有混合道路类型的大型城市网络。同时考虑不同的道路段的空间和时间依赖性以提高网络范围的预测精度。为ST-DTNN构建可伸缩的深张量，以消除由在不同位置收集的手动堆叠速度序列序列引起的潜在负面影响。可以基于具有在高速公路，高速公路和主要/次要动脉的数百个探测器的现实世界大型城市网络的探测数据同时预测多步前方的交通速度。结果证明了所提出的ST-DTNN方法的能力和有效性。与基准模型相比，ST-DTNN在可接受的训练时间内的峰值或偏远周期内执行更高的预测精度，并且在空间尺度上具有更稳定的预测性能。可以扩展所提出的方法以开发网络范围的交通状态监控，优化导航服务中的路由，并支持拥塞缓解。

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