Patent Document ID: 9558661
Application ID: 15197155

Base Claim:
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.

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Claim 2:
2. The method of claim 1 , wherein the objective to be optimized in the sparse representation at (d) is: w ^ j = arg ⁢ ⁢ min w j || w j ⁢ || 0 ⁢ ⁢ subject ⁢ ⁢ to ⁢ || Vw j - v j ⁢ || 2 2 = ∑ i = 1 n ⁢ ⁢ || V i ⁢ w j - v i j ⁢ || 2 ⁢ ≤ ɛ 0 wherein V=(V 1 T ,V 2 T , . . . V n T ) T denotes the traffic flow data collected from the sensors from time 1 to time n ; V i =[ν i 1 , ν i 2 , . . . , ν i m ] denotes traffic volumes achieved from the total m sensors at time i, i=1,2, . . . ,n; ν j =[ν τ+1 j , ν τ+2 j , . . . , ν n+τ j ] T denotes the traffic volumes collected from sensor j from time τ+1 to time τ+n; ∥w j ∥ 0 denotes the l 0 norm of w j , that is, the number of nonzero elements in vector w j ; and ε 0 denotes a predefined threshold to confine the prediction error ∥Vw j −ν j ∥ 2 , which is the l 2 norm of vector Vw j −ν j , that is, the squared root of the sum of the square of each element in the vector.