Big data‐driven machine learning‐enabled traffic flow prediction. Issue 9 (19th July 2018)

• Record Type: Journal Article
• Authors: Kong, Fanhui; Li, Jian; Jiang, Bin; Zhang, Tianyuan; Song, Houbing
• Other Names: Yalew Zelalem Jembre guestEditor.; Ahmed Syed Hassan guestEditor.; Choi Young‐June guestEditor.; Lloret Jaime guestEditor.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 30:Issue 9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-19
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3482
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network. Abstract : Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. This paper proposes a novel traffic flow prediction approach based on machine learning. Different from general deep learning models, LSTM network can learn long‐term dependence information. Experimental results show that the proposed Machine Learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.