Vehicle classification data are used for numerous transportation applications. Most of the classification data come from permanent in-pavement sensors or temporary sensors mounted on the pavement. Moving out of the right-of-way, this study develops a LIDAR (light detection and ranging) based classification system with the sensors mounted in a side-fire configuration next to the road. The first ...

The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, specification, or regulation. ABSTRACT This final report describes an advanced traffic surveillance technique ba...

In this paper, we design an attractivealgorithm aiming to classify moving targets includinghuman, animal, vehicle and drone, at groundsurveillance radar systems. The non-stationary reflectedsignal of the targets is represented with a novelmathematical framework based on behavior of thesignal components in reality. We further propose usingthe generalized linear chirp transform for the analysisst...

Individual vehicle information, especially, vehicle classification data play a key role in Advanced Traffic Management and Information Systems (ATMIS). In inductive loop and piezo-sensor fusion systems, traffic data such as the vehicle length and the distance between axles are used for vehicle classification. However, classification errors often occur in distinguishing passenger cars from small...

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Vehicle classification stations depend on accurate calibration and validation to yield meaningful results. Operating agencies spend millions of dollars to deploy vehicle classification stations to collect classified count data, yet very few of these stations are ever subjected to ...