  Full-field traffic state information (i.e., flow, speed, and density) is
critical for the successful operation of Intelligent Transportation Systems
(ITS) on freeways. However, incomplete traffic information tends to be directly
collected from traffic detectors that are insufficiently installed in most
areas, which is a major obstacle to the popularization of ITS. To tackle this
issue, this paper introduces an innovative traffic state estimation (TSE)
framework that hybrid regression machine learning techniques (e.g., artificial
neural network (ANN), random forest (RF), and support vector machine (SVM))
with a traffic physics model (e.g., second-order macroscopic traffic flow
model) using limited information from traffic sensors as inputs to construct
accurate and full-field estimated traffic state for freeway systems. To examine
the effectiveness of the proposed TSE framework, this paper conducted empirical
studies on a real-world data set collected from a stretch of I-15 freeway in
Salt Lake City, Utah. Experimental results show that the proposed method has
been proved to estimate full-field traffic information accurately. Hence, the
proposed method could provide accurate and full-field traffic information, thus
providing the basis for the popularization of ITS.
