Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Beihang University · Beijing Jiaotong University · +1 more institution
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,…
Citation impact
- FWCI
- 123.09
- Percentile
- 100%
- References
- 41
Authors
6Topics & keywords
- Autoencoder
- Convolutional neural network
- Computer science
- Artificial intelligence
- Deep learning
- Traffic flow (computer networking)
- Artificial neural network
- Floating car data
- Sustainable cities and communities