Individual Truck Speed Estimation from Advanced Single Inductive Loops

Abstract

Trucks are an essential element in freight movements, transporting 73% of freight tonnage among all modes. However, they are also associated with severe adverse impacts on roadway congestion, safety, and air pollution. Truck speed by truck body types has been considered as an indicator of traffic conditions and roadway emissions. Even though vehicle speed estimation has been researched for decades, there exists a gap in estimating truck speeds particularly at the individual vehicle level. The wide diversity of vehicle lengths associated with trucks makes it especially challenging to estimate truck speeds from conventional inductive loop detector data. This paper presents a new speed estimation model which uses detailed vehicle signature data from single inductive loop sensors equipped with advanced detectors to provide accurate truck speed estimates. This model uses new inductive signature features that show a strong correlation with truck speed. A modified feature weighting K-means algorithm was used to cluster vehicle length related features into 16 specific groups. Individual vehicle speed regression models were then developed within each cluster. Finally, a multi-layer perceptron neural network model was used to assign single loop signatures to the pre-determined speed related clusters. The new model delivered promising estimation results on both a truck-focused dataset and a general traffic dataset.