Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Navin Ranjan, Sovit Bhandari, Pervez Khan, Youn-Sik Hong and Hoon Kim
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Navin Ranjan: IoT and Big Data Research Center, Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
Sovit Bhandari: IoT and Big Data Research Center, Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
Pervez Khan: IoT and Big Data Research Center, Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
Youn-Sik Hong: Department of Computer Science and Engineering, Incheon National University, Incheon 22012, Korea
Hoon Kim: IoT and Big Data Research Center, Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea

Sustainability, 2021, vol. 13, issue 9, 1-26

Abstract: The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

Keywords: traffic congestion; congestion pattern modelling; convolutional autoencoder; traffic prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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