Marek Stencel

A vehicle classification based on inductive loop detectors

The class of vehicle is one of more important parameters in the process of road traffic measurement. Up to now, strip piezoelectric sensors and video systems have been used. The use of very cheap inductive loop detectors for vehicle classification is also possible. Such vehicle classification systems are based on magnetic profiles recorded from inductive loops. The magnetic profile is sensitive to the loop dimensions. This paper presents a discussion concerning the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes. As characteristics describing the magnetic profile of the vehicle have been used: magnetic profiles in time domain (normalized in amplitude), probability density function and magnetic profiles in vehicle length domain. For real time applications, the conversion of the measured signal into a vector of numerical parameters (a few only) is also proposed. The influence of loop dimensions on a chosen signal parameter was investigated. The case of extremely short loop (10 cm), which allows detection of the number of axles, was also analyzed.

Analysis of the temperature influences on the metrological properties of polymer piezoelectric load sensors applied in Weigh-in-Motion systems

This paper describes temperature influence on the uncertainty of weighing results in Weigh in Motion systems (WIM). The analysis bases on the long-term investigation of the WIM site, as well as on simulation tests conducted on sensor and conditioning circuit models. The drawn conclusions suggest, that this uncertainty may be significantly reduced if a proper temperature correction algorithm is applied.

Design and accuracy assessment of the multi-sensor weigh-in-motion system

In every country, road networks are one of the largest and highly expensive investment. Equally expensive are the consequences of not ensuring adequate road safety. This creates the need for an efficient and automatic system for the detection of overloaded vehicles [17]. One of the tools designed to measure the weight and individual axle loads of vehicles are the systems weighing vehicles in motion (WIM). Due to their measurement imprecision, WIM systems composed of two lines of sensors are only used as pre-selection systems. At present, the only viable option for reducing measurement uncertainty in dynamic weighing is to increase number of load sensors (Multi-Sensor systems; MS-WIM). The paper presents the results of research on a MS-WIM system equipped with 16 lines of load sensors. The conceptual objectives, system design, results of experimental evaluation of the systems accuracy, and the final conclusions based on a 6-year operating period, during which the system has been used under normal traffic conditions have been presented.

Eddy-Current Sensors with Asymmetrical Point Spread Function

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 × 2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm.

Signal parameterization vs. orthogonalization on example of vehicle's magnetic signature recognition [road traffic measurement]

Often the task of road traffic parameter measurement systems is the recognition of the class of vehicles moving on the road. The quality of such recognition depends on many factors: kind of sensor used, quality of measurement channel, recognition method used to analyze generated signals corresponding to vehicles. In the presented paper, an inductive loop sensor and vehicle magnetic signature signal are described. The paper presents two approaches to recognition. The first one consists in replacing the original signal with a vector of its parameters and looking for the minimum distance between parameter vectors and pattern vectors in each class of vehicle. A second approach relies on the search for minimum distance between the original tested signal and the first component of K-L (Karhunen-Loeve) expansion in every vehicle class. Results of both methods of recognition for the same set of signals are compared by a common, author defined, factor.

A comparison of the properties of conditioning systems cooperating with the eddy-current inductive loop sensor

The paper presents an analysis of different types of conditioning systems that are used with the inductive loop sensor. The purpose of such an analysis is to compare the quantity and the type of information obtained from the sensors and limitations of that information depending on a specific conditioning system. Streszczenie. W pracy przedstawiono analizę różnych typów układów kondycjonowania współpracujących z indukcyjnym czujnikiem pętlowym. Analiza ta ma na celu porównanie informacji pozyskiwanej z czujnika i jej ograniczenia wynikające ze stosowanego układu kondycjonowania. (Porównanie właściwości układów kondycjonowania współpracujących z wiroprądowymi czujnikami indukcyjnymi).