Subway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and revealing its potential impact are important and helpful for improving management strategy; e.g., subway emergency management can predict flow change under the condition of knowing specific policy and estimate traffic impact brought by some big events such as vocal concerts and ball games. In this paper, we propose a novel anomalies detection method of subway passenger flow. In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions to reveal the propagation law and potential impact of different anomaly events. The real flow data of Beijing subway are used for experiments. The experimental results show that the proposed method is effective for detecting anomalies of subway passenger flow in practices.

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In this paper, we investigate the problem of pilot optimization and channel estimation of two-way relaying network (TWRN) aided by an intelligent reflecting surface (IRS) with finite discrete phase shifters. In a TWRN, there exists a challenging problem that the two cascading channels from source-to-IRS-to-Relay and destination-to-IRS-to-relay interfere with each other. Via designing the initial phase shifts of IRS and pilot pattern, the two cascading channels are separated by using simple arithmetic operations like addition and subtraction. Then, the least-squares estimator is adopted to estimate the two cascading channels and two direct channels from source to relay and destination to relay. The corresponding mean square errors (MSE) of channel estimators are derived. By minimizing MSE, the optimal phase shift matrix of IRS is proved. Then, two special matrices Hadamard and discrete Fourier transform (DFT) matrix is shown to be two optimal training matrices for IRS. Furthermore, the IRS with discrete finite phase shifters is taken into account. Using theoretical derivation and numerical simulations, we find that 3-4 bits phase shifters are sufficient for IRS to achieve a negligible MSE performance loss. More importantly, the Hadamard matrix requires only one-bit phase shifters to achieve the optimal MSE performance while the DFT matrix requires at least three or four bits to achieve the same performance. Thus, the Hadamard matrix is a perfect choice for channel estimation using low-resolution phase-shifting IRS.