Journal
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Volume 34, Issue 5, Pages 969-995Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2019.1697879
Keywords
Short-term traffic forecasting; spatial-temporal dependency; network topology; graph convolution; residual long short-term memory
Categories
Funding
- UK Economic and Social Research Council [ES/L011840/1]
- China Scholarship Council [201603170309]
- University College London
- EPSRC [EP/J004197/1] Funding Source: UKRI
- ESRC [ES/L011840/1] Funding Source: UKRI
Ask authors/readers for more resources
Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available