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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting
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Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting

机译：通过正规化的张量分解学习时空潜在的交通因素：抵消缺失的价值和预测

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摘要

Intelligent transportation systems are a key component in smart cities, and the estimation and prediction of the spatiotemporal traffic state is critical to capture the dynamics of traffic congestion, i.e., its generation, propagation and mitigation, in order to increase operational efficiency and improve livability within smart cities. And while spatiotemporal data related to traffic is becoming common place due to the wide availability of cheap sensors and the rapid deployment of IoT platforms, the data still suffer some challenges related to sparsity, incompleteness, and noise which makes the traffic analytics difficult. In this article, we investigate the problem of missing data or noisy information in the context of real-time monitoring and forecasting of traffic congestion for road networks in a city. The road network is represented as a directed graph in which nodes are junctions (intersections) and edges are road segments. We assume that the city has deployed high-fidelity sensors for speed reading in a subset of edges; and the objective is to infer the speed readings for the remaining edges in the network; and to estimate the missing values in the segments for which sensors have stopped generating data due to technical problems (e.g., battery, network, etc.). We propose a tensor representation for the series of road network snapshots, and develop a regularized factorization method to estimate the missing values, while learning the latent factors of the network. The regularizer, which incorporates spatial properties of the road network, improves the quality of the results. The learned factors, with a graph-based temporal dependency, are then used in an autoregressive algorithm to predict the future state of the road network with a large horizon. Extensive numerical experiments with real traffic data from the cities of Doha (Qatar) and Aarhus (Denmark) demonstrate that the proposed approach is appropriate for imputing the missing data and predicting the traffic state. It is accurate and efficient and can easily be applied to other traffic datasets.
机译：智能交通系统是智能城市的关键组成部分，即捕捉交通拥堵动态的时空交通状态的估计和预测至关重要，即其生成，传播和缓解，以提高运营效率，提高居住能力智能城市。虽然由于廉价传感器的广泛可用性和IOT平台的快速部署而与交通有关的时空数据正在成为共同的地方，但数据仍然遭受与稀疏性，不完整性和噪声相关的一些挑战，这使得交通分析困难。在本文中，我们调查在城市中的道路网络交通拥堵的实时监测和预测中缺失数据或嘈杂信息的问题。道路网络表示为指示图，其中节点是结（交叉点）和边缘是道路段。我们假设该市部署了高保真传感器，以便在边缘的速度读取;目的是推断网络中剩余边缘的速度读数;并估计由于技术问题（例如，电池，网络等）而停止生成数据的传感器的段中缺失的值。我们提出了一系列道路网络快照的张量表示，并开发了正规化的分解方法来估计缺失值，同时学习网络的潜在因子。包含道路网络的空间特性的规范器，提高了结果的质量。然后，利用基于图形的时间依赖性的学到的因素以自回归算法使用，以预测具有大地平线的道路网络的未来状态。来自多哈（卡塔尔）和Aarhus（丹麦）城市的实际交通数据的广泛数值实验证明了所提出的方法是适合抵消缺失的数据并预测交通状态。它是准确且高效的，可以很容易地应用于其他交通数据集。

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