Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

TLDR

The study proposes a convolutional neural network that learns traffic as images to predict large‑scale, network‑wide traffic speed with high accuracy. Spatiotemporal traffic dynamics are encoded as two‑dimensional time‑space images, and a CNN extracts abstract traffic features to forecast network‑wide speed, evaluated on Beijing’s second ring road and north‑east network against four conventional algorithms and three deep‑learning models. The CNN outperforms all baselines, improving average accuracy by 42.91% while training in reasonable time, making it suitable for large‑scale transportation networks.

Abstract

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.