Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

•Extend Newell’s three-detector model to incorporate various measurement error sources.•Use a probit model and Clark’s approximation to solve stochastic three-detector model.•Establish cumulative vehicle count-based state estimation models for using AVI and GPS data.•Quantify the uncertainty and value of information of traffic state estimates.

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.