CompactETA: A Fast Inference System for Travel Time Prediction

Computing estimated time of arrival (ETA) is one of the most important services for online ride-hailing platforms like DiDi and Uber. With billions of service queries per day on such platforms, a fast inference ETA module ensures the efficiency of the overall decision system to guarantee satisfied user experience, as well as saving significant operating cost. In this paper, we develop a novel ETA learning system named as CompactETA, which provides an accurate online travel time inference within 100 microseconds. In the proposed method, we encode high order spatial and temporal dependency into sophisticated representations by applying graph attention network on a spatiotemporal weighted road network graph. We further encode the sequential information of the travel route by positional encoding to avoid the recurrent network structure. The properly learnt representations enable us to apply a very simple multi-layer perceptron model for online real-time inference. Evaluation of both offline experiments and online A/B testing verifies that CompactETA reduces the inference latency by more than 100 times compared to a state-of-the-art system, while maintains competing prediction accuracy.