Abstract
Traffic congestion is a significant problem faced by large and growing cities that hurt the economy, commuters, and the environment. Forecasting the congestion level of a road network timely can prevent its formation and increase the efficiency and capacity of the road network. However, despite its importance, traffic congestion prediction is not a hot topic among the researcher and traffic engineers. It is due to the lack of high-quality city-wide traffic data and computationally efficient algorithms for traffic prediction. In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source online web service; Seoul Transportation Operation and Information Service (TOPIS), and (ii) a hybrid neural network architecture formed by combing Convolutional Neural Network, Long Short-Term Memory, and Transpose Convolutional Neural Network to extract the spatial and temporal information from the input image to predict the network-wide congestion level. Our experiment shows that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion prediction. Our model outperforms two other deep neural networks (Auto-encoder and ConvLSTM) in terms of computational efficiency and prediction performance.
Highlights
With the increase in the economy, rapid urbanization, and desire toward a private traveling [1], the traffic congestion level of most of the large and growing cities around the world has increased drastically, which directly affects the growth, development, and environment of the cities
For 10 minutes, prediction horizons, the Prediction Network (PredNet) achieves precision ranges from 77% to 94% with an average value of 86%, which is 10% and 12% more than ConvLSTM and Autoencoder for Jam congestion levels
For the Free congestion level, PredNet reaches an average precision of 82.9%, which is 0.6% and 10% higher than ConvLSTM and Autoencoder, respectively
Summary
With the increase in the economy, rapid urbanization, and desire toward a private traveling [1], the traffic congestion level of most of the large and growing cities around the world has increased drastically, which directly affects the growth, development, and environment of the cities. The web service like Google Traffic [11], Bing Map [12], Seoul Transportation Operation and Information Service (TOPIS) [13], and Baidu Map [14], publicly started to provide the accurate city-wide real-time traffic information such as congestion level and the average speed of the road segment These web services are public, accessible, and provide traffic information for most of the cities in the world, there is only a handful number of studies based on them. Inspired by the successful application of convolutional autoencoder and LSTM mentioned above, in this paper, we present an approach, which can learn both spatial and temporal relationships between the sequences of historical image data for traffic congestion prediction.
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