1. A method for traffic flow prediction based on data mining on spatio-temporal correlations, comprising: (a) collecting raw data of traffic flows through sensors distributed at nodes located along a road network; (b) preprocessing the collected raw data into a valid form of traffic flow data; (c) establishing a prediction model, comprising: letting ν i j represent traffic volume data sampled at sensor j at time i; supposing that there are in total m sensors in a road network; denoting the state of the whole road network at time i as V i =[ν i 1 , ν i 2 ,. .. V i m ]; and using a linear regression model to predict the traffic volume data collected at senor j with time lag τ as follows: 
 ν i+τ j =V i w j wherein weights w j =[w 1 j , w 2 j ,. .. w k j ,. .. w m j ] T are parameters to be optimized and ν i+τ j is the predicted traffic volume; (d) mining spatio-temporal correlations, comprising: applying a sparse representation as an optimization method to obtain the parameters w j , wherein w j =[w 1 j , w 2 j ,. .. w k j ,. .. w m j ] T represent the spatio-temporal correlations between the traffic flow data from each sensor in the whole road network and the data from the target sensor j undergoing prediction; wherein when w k j =0, the data from sensor k are not correlated to the data from sensor j; wherein otherwise, w k j indicates the correlation degree between the data from sensor k and the data from sensor j, k=1,2,. .. ,m; and (e) performing traffic flow prediction by applying the spatio-temporal correlated data as the input to the prediction model.