Rainfall‐integrated traffic speed prediction using deep learning method. Issue 9 (10th August 2017)

• Record Type: Journal Article
• Authors: Jia, Yuhan; Wu, Jianping; Ben‐Akiva, Moshe; Seshadri, Ravi; Du, Yiman
• Is Part Of: IET intelligent transport systems. Volume 11:Issue 9(2017)
• Page Start: 531
• Page End: 536
• Publication Date: 2017-08-10
• Subjects: traffic information systems -- learning (artificial intelligence) -- time series -- environmental factors -- belief networks; rainfall‐integrated traffic speed prediction -- deep learning method -- traffic information prediction -- traffic research -- traffic operation -- traffic management -- road users -- traffic authorities -- traffic flow -- stochastic feature -- shallow model structure -- environmental factors -- rainfall influence -- deep belief network -- DBN -- long short‐term memory -- LSTM -- short‐term traffic speed prediction -- nontraffic input -- traffic detector data -- Beijing -- deep training -- deep testing -- time‐series characteristics; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2016.0257
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Traffic information prediction is one of the most essential studies for traffic research, operation and management. The successful prediction of traffic speed is increasingly significant for the benefits of both road users and traffic authorities. However, accurate prediction is challenging, due to the stochastic feature of traffic flow and shallow model structure. Furthermore, environmental factors, such as rainfall influence, should also be incorporated to improve accuracy. Inspired by deep learning, this paper investigates the performance of deep belief network (DBN) and long short‐term memory (LSTM) to conduct short‐term traffic speed prediction with the consideration of rainfall impact as a non‐traffic input. The deep learning models have the ability to learn complex features of traffic flow pattern under various rainfall conditions. To validate the performance of rainfall‐integrated DBN and LSTM, the traffic detector data from an arterial in Beijing are utilised for model training and testing. The experiment results indicate that with the combination input of speed and additional rainfall data, deep learning models have better prediction accuracy over other existing models, and also yields improvements over the models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time‐series characteristics of traffic speed data.