Exploiting Taxi Demand Hotspots Based on Vehicular Big Data Analytics

In the urban transportation system, the unbalanced relationship between taxi demand and the number of running taxis reduces the drivers' income and the levels of passengers' satisfaction. With the help of vehicular global positioning system (GPS) data, the taxi demand distribution of city can be analyzed to provide advice for drivers. A clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is suitable for discovering demand hotspots. However, the execution efficiency is still a big challenge when DBSCAN is applied on big databases. In this paper, we propose an improved density-based clustering algorithm called Grid and Kd-tree for DBSCAN (GD-DBSCAN), which integrates partitioning method with kd-tree structure to improve the computational performance of DBSCAN. Furthermore, this algorithm can take advantages of multi cores and shared memory to parallelize related functions. The experiment shows GD-DBSCAN is efficient, it has an improvement of at least 10% in performance compared with DBSCAN.