Commercial vehicles typically represent a small fraction of vehicular
traffic on most roadways. However, their influence on the economy,
environment, traffic performance, infrastructure, and safety are much
more significant than their diminutive numerical presence suggests.
This dissertation describes the development and prototype implementation
of a new high-fidelity inductive loop sensor and a ground-breaking
commercial vehicle classification system based on the vehicle inductive
signatures obtained from this sensor technology. This new sensor
technology is relatively easy to install and has the potential to yield
reliable and highly detailed vehicle inductive signatures for advanced
traffic surveillance applications.
The Speed PRofile INterpolation Temporal-Spatial (SPRINTS)
transformation model developed in this dissertation improves vehicle
signature data quality under adverse traffic conditions where
acceleration and deceleration effects can distort inductive vehicle
signatures. The axle classification model enables commercial vehicles
to be classified accurately by their axle configuration. The body
classification models reveal the function and unique impacts of the
drive and trailer units of each commercial vehicle.
Together, the results reveal the significant potential of this inductive
sensor technology in providing a more comprehensive commercial vehicle
data profile based on a unique ability to extract both axle
configuration information as well as high fidelity undercarriage
profiles within a single sensor technology to provide richer insight on
commercial vehicle travel statistics.
