  The transport literature is dense regarding short-term traffic predictions,
up to the scale of 1 hour, yet less dense for long-term traffic predictions.
The transport literature is also sparse when it comes to city-scale traffic
predictions, mainly because of low data availability. In this work, we report
an effort to investigate whether deep learning models can be useful for the
long-term large-scale traffic prediction task, while focusing on the
scalability of the models. We investigate a city-scale traffic dataset with 14
weeks of speed observations collected every 15 minutes over 1098 segments in
the hypercenter of Los Angeles, California. We look at a variety of
state-of-the-art machine learning and deep learning predictors for link-based
predictions, and investigate how such predictors can scale up to larger areas
with clustering, and graph convolutional approaches. We discuss that modelling
temporal and spatial features into deep learning predictors can be helpful for
long-term predictions, while simpler, not deep learning-based predictors,
achieve very satisfactory performance for link-based and short-term
forecasting. The trade-off is discussed not only in terms of prediction
accuracy vs prediction horizon but also in terms of training time and model
sizing.

Machine Learning;Machine Learning