Freeway Traffic State Estimation and Uncertainty Quantification based on Heterogeneous Data Sources: Stochastic Three-Detector Approach

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 microscopic traffic states on a homogeneous freeway segment. A multinomial probit model and an innovative use of Clark’s approximation method were introduced to extend Newell’s method to solve a stochastic threedetector problem. The mean and variance-covariance estimates of cumulative vehicle counts on both ends of a traffic segment were used as probabilistic inputs for the estimation of cell-based flow and density inside the space-time boundary and the construction of a series of linear measurement equations within a Kalman filtering estimation framework. We present an information-theoretic approach to quantify the value of heterogeneous traffic measurements for specific fixed sensor location plans and market penetration rates of Bluetooth or GPS floating car data.