Predicting vehicular travel times by modeling heterogeneous influences
  between arterial roads
  Predicting travel times of vehicles in urban settings is a useful and
tangible quantity of interest in the context of intelligent transportation
systems. We address the problem of travel time prediction in arterial roads
using data sampled from probe vehicles. There is only a limited literature on
methods using data input from probe vehicles. The spatio-temporal dependencies
captured by existing data driven approaches are either too detailed or very
simplistic. We strike a balance of the existing data driven approaches to
account for varying degrees of influence a given road may experience from its
neighbors, while controlling the number of parameters to be learnt.
Specifically, we use a NoisyOR conditional probability distribution (CPD) in
conjunction with a dynamic bayesian network (DBN) to model state transitions of
various roads. We propose an efficient algorithm to learn model parameters. We
propose an algorithm for predicting travel times on trips of arbitrary
durations. Using synthetic and real world data traces we demonstrate the
superior performance of the proposed method under different traffic conditions.
