Generic Approaches to Estimating Freeway Traffic State and Percentage of Connected Vehicles With Fixed and Mobile Sensing
Mingming Zhao, Claudio Roncoli, Yibing Wang, Nikolaos Bekiaris‐Liberis, Jingqiu Guo, Senlin Cheng
Abstract
Three filtering-based approaches to freeway traffic state estimation are studied using measurements from connected vehicles and also a minimum number of fixed detectors. These approaches are: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method 1</i> based on EKF and the second-order traffic flow model METANET, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods 2</i> and 3 based on KF and the conservation equation that is driven by mean speed data of connected vehicles under a speed-uniformity assumption. Each method is capable of estimating segment traffic flow variables (speeds, densities, and flows) as well as segment market penetration rates (MPRs) of connected vehicles. The three methods are evaluated and compared in depth using NGSIM data with respect to their traffic state estimator design, data requirements, capabilities, limitations in the mixed sensing case. Recommendations are given about the choice of methods over the range of MPR.