Identification and Evolutionary Pattern of Recurrent Congestion Area Based on Grid Division
In order to accurately identify the recurrent congestion areas,analyze their congestion propaga-tion directions,solve the congestion source and block the congestion propagation path,a method for identi-fying and analyzing the evolutionary pattern of recurrent congestion areas based on grid model and Fuzzy Core DBSCAN algorithm was proposed.Firstly,the urban road network was gridded and the number and weighted average speed of vehicle trajectories within grids were comprehensively analyzed com-bining taxi GPS data.A traffic congestion discrimination model was constructed to determine the con-gestion status within the grid.Then,the Fuzzy Core DBSCAN algorithm extended by combining fuzzy set theory and DBSCAN algorithm was used to identify the recurrent congestion areas.Three indica-tors,namely congestion scale,congestion propagation direction(i.e.the number and probability of propagation between two adjacent grids),and key grids,were selected to analyze the evolution pattern of recurrent congestion.Finally,an empirical analysis was carried out by taking the grid area within the second ring road of Harbin City,Heilongjiang Province as an example.The results showed that the congestion of the identified key grid G(14,13)in the recurrent congestion area was transmitted to the adjacent grids 85 times in a week,of which 27 times,or with 31.8%probability,spread northward along Xidazhi Street to grid G(15,14).Compared with the data of Baidu Map Open Platform,the accu-racy of congestion discrimination results based on grid model was 85%,and the identification and anal-ysis of congestion evolution patterns in recurrent congestion areas based on the Fuzzy Core DBSCAN algorithm was consistent with the time-varying process of traffic conditions on the Baidu Map Open Platform.It means that the fusion of Fuzzy Core DBSCAN algorithm and grid model can effectively identify the congestion propagation direction of key grids in recurrent congestion areas,which can pro-vide support for the formulation of scientific and reasonable congestion mitigation schemes.
urban trafficrecurrent congestiongrid modelFuzzy Core DBSCANtaxi GPS data
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