Generic Approaches to Estimating Freeway Traffic State and Percentage of Connected Vehicles With Fixed and Mobile Sensing

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 8; pp. 13155 - 13177
• Main Authors: Zhao, Mingming, Roncoli, Claudio, Wang, Yibing, Bekiaris-Liberis, Nikolaos, Guo, Jingqiu, Cheng, Senlin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Connected vehicles; Conservation equations; Data models; filtering; Freeway traffic state estimation; Highways; market penetration rate; Mathematical models; mixed sensing; Real-time systems; Segments; Sensors; speed-uniformity assumption; State estimation; Traffic control; Traffic flow; traffic flow modelling; Traffic models; Traffic speed; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3121181

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: Method 1 based on EKF and the second-order traffic flow model METANET, Methods 2 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.