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Accurate and reliable prediction of traffic measurements plays a crucial rolein the development of modern intelligent transportation systems. Due to morecomplex road geometries and the presence of signal control, arterial trafficprediction is a level above freeway traffic prediction. Many existing studieson arterial traffic prediction only consider temporal measurements of flow andoccupancy from loop sensors and neglect the rich spatial relationships betweenupstream and downstream detectors. As a result, they often suffer largeprediction errors, especially for long horizons. We fill this gap by enhancinga deep learning approach, Diffusion Convolutional Recurrent Neural Network,with spatial information generated from signal timing plans at targetedintersections. Traffic at signalized intersections is modeled as a diffusionprocess with a transition matrix constructed from the phase splits of thesignal phase timing plan. We apply this novel method to predict traffic flowfrom loop sensor measurements and signal timing plans at an arterialintersection in Arcadia, CA. We demonstrate that our proposed method yieldssuperior forecasts; for a prediction horizon of 30 minutes, we cut the MAPEdown to 16% for morning peaks, 10% for off peaks, and even 8% for afternoonpeaks. In addition, we exemplify the robustness of our model through a numberof experiments with various settings in detector coverage, detector type, anddata quality.

A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction