Traffic congestion prediction based on GPS trajectory data. (May 2019)

• Record Type: Journal Article
• Authors: Sun, Shuming; Chen, Juan; Sun, Jian
• Is Part Of: International journal of distributed sensor networks. Volume 15:Number 5(2019)
• Publication Date: 2019-05
• Subjects: GPS trajectory data -- map matching -- convolutional neural network -- recurrent neural network -- traffic congestion prediction; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147719847440
• Languages: English
• ISSNs: 1550-1329

Abstract

Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridge regression are used to perform congestion level prediction. According to the experimental results, deep learning models obtain higher accuracy in traffic congestion prediction compared with conventional machine learning models.