Cloud City Traffic State Assessment System Using a Novel Architecture of Big Data

Recently, big data are widely applied to different field. This work presents a cloud city traffic state assessment system using a novel architecture of big data. The proposed system provides the real-time bus location and real-time traffic situation, especially the real-time traffic situation nearby, through open data, GPS, GPRS and cloud technologies. With the high-scalability cloud technologies, Hadoop and Spark, the proposed system architecture is first implemented successfully and efficiently. Next, we utilize three clustering methods, DBSCAN, K-Means, and Fuzzy C-Means to find the area of traffic jam in Taichung city and moving average to find the area of traffic jam in Taiwan Boulevard which is the main road in Taichung city. Finally, experimental results show the effectiveness and efficiency of the proposed system services via an advanced web technology. In addition, some experimental results indicate that the computing ability of Spark is better than that of Hadoop.

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