Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data
A K neighbor-RElim(KNR)algorithm and a sequential KNbr-RElim(SKNR)algorithm are proposed to mine traffic congestion association rules and congestion propagation spatio-temporal association rules by vehicle trajectory data in a large-scale road network.The KNR algorithm extends the spatial topology constraint based on the RElim algorithm.The KNR can be used to mine the road links prone to congestion from the large-scale trajectory dataset in a large-scale road network and quantify the strength of association for congested road links.The SKNR algorithm expands the time dimension in the form of sliding window and can be applied for mining the congestion propagation phenomenon which is difficult to observe directly in a large-scale road network and tracing the path of congestion propagation.The algorithms are illustrated and verified by the empirical results of the Chengdu road network with vehicle trajectory data.The results show the effectiveness and robustness of the proposed algorithms.
data miningassociation rulescongestion propagationtrajectory dataRElim algorithm
NETL
NSTL
万方数据
维普
