Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

• Published in: Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 17
• Main Authors: Scepanovic, Biljana, Wu, Wenguang, Gao, Zhibo, Zeng, Qiang, Yi, Kefu, Rong, Donglei, Hao, Wei, Wei, Chongfeng
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; Hindawi Limited; Hindawi-Wiley
• Subjects: Algorithms; Artificial intelligence; Back propagation; Back propagation networks; Data analysis; Data compression; Deep learning; Driving conditions; Efficiency; Learning; Long short-term memory; Machine learning; Neural networks; Prediction models; Predictions; Principal components analysis; Roads; Support vector machines; Test sets; Time series; Traffic congestion; Traffic delay; Traffic flow; Traffic information; Traffic models; Training; Transportation
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2020/8831521

With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.