Novel Real-Time System for Traffic Flow Classification and Prediction
YE Dezhong1, LV Haibing1, GAO Yun2, BAO Qiuxia2, and CHEN Mingzi2
( 1. ZTE Corporation, Shenzhen, Guangdong 518057, China;
2. Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China)
Traffic flow prediction has been applied into many wireless communication applications (e.g., smart city, Internet of Thing). With the development of wireless communication technologies and artificial intelligence, how to design a system for real-time traffic flow prediction and receive high accuracy of prediction are urgent problems for both researchers and equipment suppliers. This paper presents a novel real-time system for traffic flow prediction. Different from the single algorithm for traffic flow prediction, our novel system firstly utilizes dynamic time wrapping to judge whether traffic flow data has regularity, realizing traffic flow data classification. After traffic flow data classification, we respectively make use of XGBoost and wavelet transform-echo state network to predict traffic flow data according to their regularity. Moreover, in order to realize real-time classification and prediction, we apply Spark/Hadoop computing platform to process large amounts of traffic data. Numerical results show that the proposed novel system has better performance and higher accuracy than other schemes.
traffic flow prediction; dynamic time warping; XGBoost; echo state network; Spark/Hadoop computing platform