Discovering Spatial Contexts for Traffic Flow Prediction with Sparse Representation Based Variable Selection

A new methodology based on sparse representation is proposed to detect the relevant sensors for traffic flow prediction at a given sensor. It performs remarkably better than the least square fitting and the local spatial context based methods. Some interesting phenomena have been observed in the experiments: (1) In general, hundreds of sensors distributed on the whole road network are relevant to a prediction task, which implies a much wider correlation range than what was assumed previously. (2) The number of relevant sensors is subject to the targeted sensor undergoing prediction due to location-specific network topology. (3) The spatial correlation scale increases with the increment of time lag while the performance degradation is less than that of the local spatial context based methods. As the scope of human mobility is subject to time lag, identifying the varying spatial context against time lag is crucial for prediction.