Traffic volume information is critical for intelligent transportation systems. It serves as a key input to transportation planning, roadway design, and traffic signal control. However, the traffic volume data collected by fixed-location sensors, such as loop detectors, often suffer from the missing data problem and low coverage problem. The missing data problem could be caused by hardware malfunction. The low coverage problem is due to the limited coverage of fixed-location sensors in the transportation network, which restrains our understanding of the traffic at the network level. To tackle these problems, we propose a probabilistic model for traffic volume reconstruction by fusing fixed-location sensor data and probe vehicle data. We apply the probabilistic principal component analysis (PPCA) to capture the correlations in traffic volume data. An innovative contribution of this work is that we also integrate probe vehicle data into the framework, which allows the model to solve both of the above-mentioned two problems. Using a real-world traffic volume dataset, we show that the proposed method outperforms state-of-the-art methods for the extensively studied missing data problem. Moreover, for the low coverage problem, which cannot be handled by most existing methods, the proposed model can also achieve high accuracy. The experiments also show that even when the missing ratio reaches 80%, the proposed method can still give an accurate estimate of the unknown traffic volumes with only a 10% probe vehicle penetration rate. The results validate the effectiveness and robustness of the proposed model and demonstrate its potential for practical applications.
翻译:交通流量信息对智能运输系统至关重要。 它作为交通规划、道路设计和交通信号控制的关键投入,对交通规划、道路设计和交通信号控制至关重要。 但是,由固定地点传感器(如环形探测器)收集的交通量数据往往存在缺失的数据问题和低覆盖范围问题。 缺少的数据问题可能由硬件故障造成。 覆盖率低的问题是由于运输网络中固定地点传感器的覆盖范围有限,这限制了我们对网络一级交通流量的理解。 为了解决这些问题,我们建议采用固定地点传感器数据和探测车辆数据,为交通流量重建提供一个概率模型。 我们采用概率主要部件分析(PPCA)来捕捉交通量数据的相关性。 这项工作的一个创新贡献是,我们还将车辆数据调查纳入框架,从而使得该模型能够解决上述两个问题。 使用真实世界交通量数据集,我们证明拟议的方法在广泛研究的数据问题方面不符合最新技术方法。 此外,对于低覆盖范围问题,即使大多数现有车辆流量数据都无法处理,我们采用概率主要部分分析(PPMCA)来捕捉到交通流量数据中的关联性。 这项工作的一个创新贡献是,我们还将将车辆的准确性纳入框架中,因此,拟议的模型能够显示拟议的10号的准确性比率的准确率。 也能够显示拟议的方法的准确性能显示拟议的10号的准确率。