Sensors· 2017Q1
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
- 1,409citations
- Q1SCImago
- 2017year
Short summary
A novel deep convolutional neural network (CNN) treats traffic data as images, achieving a 42.91% average accuracy improvement in predicting network-wide traffic speeds compared to existing methods.
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Key points
- Traffic dynamics are transformed into 2D time-space matrices, visualized as images for CNN analysis.
- The proposed CNN method achieved an average accuracy improvement of 42.91% over benchmark algorithms.
- The approach demonstrated superior performance against ordinary least squares, k-NN, ANN, random forest, stacked autoencoder, RNN, and LSTM.
- The CNN model trains within a reasonable time, making it suitable for large-scale transportation networks.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Sensors, 2017 · DOI ↗
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Field: Building and Construction
Building and ConstructionEngineering