Essien, A.E., Chukwkelu, G. and Giannetti, C. (2019) A Scalable Deep Convolutional LSTM Neural Network for Large-Scale Urban Traffic Flow Prediction Using Recurrence Plots. 2019 IEEE AFRICON, Accra, 25-27 September 2019, 1-7.
https://doi.org/10.1109/AFRICON46755.2019.9134031

has been cited by the following article:

• TITLE: Urban Traffic Flow Prediction Based on Spatio-Temporal Convolution Networks

  AUTHORS: Peng Zheng, Yuansong Li, Maoyan Lin, Youxin Hu

  KEYWORDS: Traffic Flow, Deep Learning, RNN, CNN

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.3, March 23, 2023

  ABSTRACT: Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features; 2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.