Realising the spatio‐temporal evolutionary pattern of urban traffic can give advice about making personal trip route planning and improving road construction. A novel pattern‐discovering model is presented to identify the traffic regularity and characteristics from spatial and temporal dimensions. To unveil this new method, there are two main parts as follows: first, by employing the constrained projected gradient of the non‐negative matrix factorisation algorithm, the original traffic data matrix is decomposed into the feature matrix and the weight matrix. Necessary constraints are newly added so that the resulting matrices are ensured to make practical sense for reflecting the traffic spatio‐temporal regular pattern. Then, the self‐organising maps network is further used to cluster the factorisation error into several classes representing the disparate traffic pattern of each time. In addition, the experiment is conducted on real historical data to verify the performance of the algorithm. The global urban traffic flow for a week is summarised through a set of basic patterns with related weight distribution. The well‐visualised result demonstrates that the authors method can achieve significant improvement in terms of computational efficiency and accuracy when compared with other widely‐used methods.
Mining the spatio‐temporal pattern using matrix factorisation: a case study of traffic flow
IET Intelligent Transport Systems ; 14 , 10 ; 1328-1337
2020-10-01
10 pages
Article (Journal)
Electronic Resource
English
spatiotemporal phenomena , traffic spatio‐temporal regular pattern , constrained projected gradient , spatio‐temporal pattern , self‐organising feature maps , road traffic , factorisation error , weight matrix , traffic engineering computing , personal trip route , matrix algebra , spatio‐temporal evolutionary pattern , nonnegative matrix factorisation algorithm , basic patterns , telecommunication network routing , data mining , global urban traffic flow , novel pattern‐discovering model , feature matrix , temporal dimensions , original traffic data matrix , disparate traffic pattern , spatial dimensions , matrix decomposition , traffic regularity , telecommunication traffic , road construction
Mining the spatio-temporal pattern using matrix factorisation: a case study of traffic flow
IET | 2020
|Method for traffic flow prediction based on spatio-temporal correlation mining
European Patent Office | 2017
|METHOD FOR TRAFFIC FLOW PREDICTION BASED ON SPATIO-TEMPORAL CORRELATION MINING
European Patent Office | 2016
|Spatio-Temporal AutoEncoder for Traffic Flow Prediction
IEEE | 2023
|