Abstract
The article presents a measurement system that captures two components of a motor vehicle’s magnetic profile, which are associated with the real and imaginary part of the impedance of a narrow inductive loop sensor. The proposed algorithm utilizes both components of the impedance magnetic profile to detect vehicle axles, including lifted axles. Accuracies of no less than 71.8% were achieved for vehicles travelling with a lifted axle, and no less than 98.8% for other vehicles. The axle detection accuracy was determined during a series of experiments carried out under normal traffic conditions, using profile analysis, video footage and reference signals from an axle load detector on a total of 4000 vehicles.
Highlights
Research aimed at finding new systems and methods for measuring vehicle parameters is carried out in many research centers around the world [1,2,3,4,5,6,7,8,9]
Traffic characteristics are constantly updated and made available from multiple locations at the same time, which makes it possible to draw the right conclusions regarding the use of road infrastructure and to support emergency services when responding to road accidents [6]
The results have shown that narrow inductive loop (IL) sensors can be used for axle detection and for determining the number of axles
Summary
Research aimed at finding new systems and methods for measuring vehicle parameters is carried out in many research centers around the world [1,2,3,4,5,6,7,8,9]. Intelligent transportation systems (ITS) use these parameters to automatically classify vehicles, to determine traffic statistics and aggregated traffic characteristics, to verify traffic models, and for adaptive traffic control and the generation of messages for traffic participants. Statistics related to vehicle flow in a given area play a crucial role when developing road infrastructure such as petrol stations and parking lots. There is a growing demand for vehicle class information in traffic control systems such as automatic entry gates, and electronic toll collection and charging systems. Automatic classification is essential in Weigh-In-Motion systems, as the permissible axle load depends on the number and configuration of the axles of a vehicle as well as its class [7,8]
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