A Linear Programming Model for Estimating High-Resolution Freeway Traffic States Using Vehicle Identification and Location Data

摘要

Traffic state estimation on freeway segments is widely studied as a complex non-linear and stochastic estimation problem. By capturing the essential forward and backward wave propagation characteristics through cumulative flow count variables, this paper develops a unified representation with a parsimonious explanation for traffic observations under free-flow, congested and dynamic transient conditions. New formulations are presented to utilize Bluetooth vehicle identification records and GPS vehicle location data on a freeway corridor with a merge/diverge. By further adding non-negativity and maximum discharge rate constrains, we construct a computationally efficient linear programming model to estimate traffic states, namely, density and traffic flow, through cumulative flow counts at each second. The proposed model is implemented and tested systematically based on a real-world NGSIM data set.