A Non-Gaussian Kalman Filter With Application to the Estimation of Vehicular Speed

Abstract

Single inductive loop detectors (ILDs), which provide online measurements of traffic volume and occupancy, are widely used devices in road systems. Because of the nature of traffic flow, fast estimation and forecasting of vehicular speed using the data collected by an ILD are crucial to online road traffic management. Here we formulate statistical inference for vehicular speed as a dynamic generalized linear model with a reciprocal inverse Gaussian observational distribution. The formulation motivates us to extend the Gaussian Kalman filter to this non-Gaussian scenario. This results in a set of simple recursive formulas in which the current estimate of the parameter of interest is updated as a weighted harmonic average of the previous estimate and the current observation. By applying the developed non-Gaussian Kalman filter to analyze traffic data collected by an ILD, we provide a competitive alternative for estimating vehicular speed at a minimum computational cost.