Empirical Evaluation of New Robust Travel Time Estimation Algorithms

Abstract

Travel times are key statistics for traffic performance, policy, and management evaluation purposes. Estimating travel times from local traffic speeds collected with loops or other sensors has been a relevant and lively research area. The most widespread and arguably most flexible algorithms developed for this purpose fall into the class of trajectory methods, which reconstruct synthetic vehicle trajectories on the basis of measured spot speeds and encompass various assumptions on which speeds prevail between traffic sensors. From these synthetic trajectories, average travel times can be deduced. This paper reviews and compares a number of these algorithms against two new trajectory algorithms based on spatiotemporal filtering of speed and 1/speed (slowness). On the basis of real data (from induction loops and an automated vehicle identification system), it is demonstrated that these new algorithms are more accurate (in terms of bias and residual error) than previous algorithms, and more robust with respect to increasing amounts of missing data.