Abstract Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs), while existing approaches may not be suitable for big traffic data processing. To this end, this invention proposes a robust method for distributed traffic subarea division. Specifically, this invention presents a MapReduce-based Parallel Three-Phase K-Means (Par3PKM) algorithm for solving the traffic subarea divi sion problem on a widely adopted Hadoop distributed computing platform. We first modify the distance metric and initialization strategy of K-Means and then employ a MapReduce paradigm to redesign the optimized K-Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the bor ders of clustering results for each cluster. Finally, we divide the traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in one month using the proposed approach. The present invention has broad applications in big data analytics. DTSAD method Large-scale taxi trajectories Data preprocessing Parallel clustering Boundary identifying Traffic subarea Hadoop with MapReduce ArcGIS Division framework Figure 1 Select K Initialize centroids r ei eui------------------ of Iteration Compute the distance between each object and each cluster center Assign each object to its closest centroid Recompute the centroid of each cluster Criterion function N converges? I----------------------------------- ---------------- Figure 2
A Robust Method for Distributed Traffic Subarea Division
2020-10-01
Patent
Electronic Resource
English
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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