HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering

Abstract

The Estimated Time of Arrival (ETA) that predict the travel time of a given GPS trajectory has been extensively used in route planning. Deep learning has been widely applied to ETA prediction. However, prediction tasks involve some challenges, such as small data size, low GPU’s precision, high training loss, and low accuracy. Herein, we present a new machine-learning algorithm called HyperETA for ETA prediction. HyperETA is based on a extraordinary clustering method, called Hypercube Clustering. We conducted experiments to compare HyperETA with a deep-learning-based method called DeepTTE by using taxi trajectories as a benchmark. Two variations of both methods were evaluated. The results indicated that HyperETA outperformed the deep-learning approach in terms of prediction accuracy.