Abstract:
Long-term traffic predictions are less dense than short-term ones. City-scale traffic prediction literature is also scarce due to data scarcity. This study examines the scalability of deep learning models for long-term large-scale traffic prediction. We analyze 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 investigate how clustering and graph convolutional approaches can scale up state-of-the-art machine learning and deep learning predictors for link-based predictions. We discuss how modeling temporal and spatial features into deep learning predictors can help long-term predictions, while simpler predictors perform well for link-based and short-term forecasting. Training time, model sizing, and prediction accuracy vs. prediction horizon are discussed.
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