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Graph convolutional LSTM model for traffic delay prediction with uncertainty

Traffic flow in an urban environment exhibits a complex spatio-temporal in- teraction. The propogation of traffic flow through a transportation network depends on a number of factors, including the structure of the network and the time of day. Current analysis of this data by road controlling authorities is of- ten simplified and lacks a detailed understanding of how traffic moves through the network. A deep learning model which models both the spatial and tempo- ral interactions present in the data is able to capture complex patterns present in the data and allows for a more detailed understanding of traffic flow. A GC- LSTM model is explored for Hamilton City to predict traffic delay. It is found to have improved prediction accuracy over a standard LSTM by incorporating the spatial structure of the Hamilton road network. Additionally, Bayesian layers are integrated into the model to obtain a probability distribution over each prediction. By quantifying the uncertainty over each prediction, the de- cision making process based on the analysis can be carried out with a higher degree of confidence than a single point prediction from the model.
Type of thesis
Townsend, D. (2021). Graph Convolutional LSTM model for traffic delay prediction with uncertainty (Thesis, Master of Science (Research) (MSc(Research))). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/14575
The University of Waikato
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