Traffic data reconstruction based on compressive sensing with neighbor regularization

AbstractAbstractThe production and collection of the mass traffic data in the vehicle network will result in wasted bandwidth and data transmission delay. The mobile edge computing (MEC) technology is able to reduce the transmission cost and provide fast interactive response. However, the amount of data between roadside units (RSUs) and MEC servers has not decreased. Compressive sensing (CS) technology can reduce the sampling frequency and reconstruct the signal with even fewer samples than the sampling theorem requires. Therefore, in this article, we use CS technology to reduce the data in the “last mile” of traffic edge computing network. We propose two traffic data reconstruction models, which use different neighbor regularization methods in the objective function of CS reconstruction model. Different from the previous studies, we leverage the low‐rank nature and the neighbor relationship between detectors to improve the reconstruction accuracy of different missing scenes. To validate the improved reconstruction models, we have carried out extensive experiments based on the actual traffic flow data of the freeway system under different loss rates and loss patterns. The missing data are caused by reduced sampling frequency and accidental transmission loss so that it is closer to the real fact. The experimental results show that even if the traffic data from the RSUs to MEC servers are decreased by 50%, the complete data can be reconstructed accurately. View Figure In this article, we propose two traffic data reconstruction models based on compressive sensing reconstruction model to reduce the amount of data collected by the roadside unit (RSU) detectors as well as provides accurate traffic status data for mobile edge computing (MEC) servers. The experimental results show that even if the traffic data from the RSUs to MEC servers are decreased by 50%, the complete data can be reconstructed accurately.