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Mining Road Network Correlation for Traffic Estimation via Compressive Sensing

机译：挖掘路网相关性用于基于压缩感知的交通量估算

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摘要

This paper presents a transport traffic estimation method which leverages road network correlation and sparse traffic sampling via the compressive sensing technique. Through the investigation on a traffic data set of more than 4400 taxis from Shanghai city, China, we observe nontrivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct representation bases to sparsely represent the traffic conditions of all road segments in a road network. With the trait of sparse representation, we propose a traffic estimation approach that applies the compressive sensing technique to achieve a city-scale traffic estimation with only a small number of probe vehicles, largely reducing the system operating cost. To validate the traffic correlation model and estimation method, we do extensive trace-driven experiments with real-world traffic data. The results show that the model effectively reveals the hidden structure of traffic correlations. The proposed estimation method derives accurate traffic conditions with the average accuracy as 0.80, calculated as the ratio between the number of correct traffic state category estimations and the number of all estimation times, based on only 50 probe vehicles' intervention, which significantly outperforms the state-of-the-art methods in both cost and traffic estimation accuracy.
机译：本文提出了一种利用路网相关性和通过压缩感知技术进行稀疏交通采样的运输交通估算方法。通过对来自中国上海市区的4400多辆出租车的交通数据进行调查，我们观察到了不同路段交通状况之间非平凡的交​​通相关性，并得出了捕捉这种关系的数学模型。经过数学处理后，这些模型可用于构建表示基础，以稀疏表示道路网络中所有路段的交通状况。鉴于稀疏表示的特点，我们提出了一种交通估算方法，该方法采用压缩传感技术，仅使用少量探测车辆即可实现城市规模的交通估算，从而大大降低了系统运营成本。为了验证流量相关模型和估算方法，我们对真实的流量数据进行了广泛的跟踪驱动实验。结果表明，该模型有效地揭示了交通关联的隐藏结构。提出的估算方法可得出准确的交通状况，其平均准确度为0.80，这是根据正确的交通状态类别估算次数与所有估算次数之间的比率得出的，仅基于50辆探测车的干预，其性能明显优于该状态成本和流量估算准确性方面的最先进方法。

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