Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions

Fitting distributions of travel-time in vehicle traffic is an important application of spatio-temporal data mining. While regression methods to forecast the expected travel-time are standard approaches of travel-time prediction, we need to estimate distributions of the travel-time when using stateof-the-art risk-sensitive route recommendation systems. The authors introduce a novel nonparametric density estimator of travel-time for each road or link. The new estimator consists of basis functions modeled as mixtures of gamma or log-normal density functions, a sparse link similarity matrix given as an approximate diffusion kernel on a link connectivity graph, and importance weights for each link. Unlike the existing nonparametric methods that are computationally intensive, the new estimator is stably applicable to larg