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Abstract:
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Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones. TRIP is based on maximum a posteriori estimation in a Bayesian hierarchical model; it captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and scales efficiently in the size of the road network. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables.
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