The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and K_z criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m.

Piezoelectric energy harvesters have been extensively researched for use with wireless sensors or low power consumption electronic devices. Most of the piezoelectric energy harvesters cannot generate enough power for potential applications. In this study, we explore the parameters, including gap and proof mass, that can affect the damping of the cantilever to optimize the design of the energy harvester. A finite analysis is conducted using COMSOL Multiphysics software. Usually, this type of simulation is performed using the loss factor. However, it is known that results from the loss factor produce models that do not fit the experimental data well. In fact, the result of output voltage using the loss factor is 50% higher than the real value, which is due to ignoring the adverse effect of a superimposing mechanical damping of different constituent materials. In order to build a true model, Rayleigh damping coefficients are measured to use in a simulation. This resulted in a closer fit of modeling and experimental data, and a 5 times better output voltage from the optimized energy harvester compared with using the smallest gap and mass.

Methods for estimating a car’s length are presented in this paper, as well as the results achieved by using a self-designed system equipped with two anisotropic magneto-resistive (AMR) sensors, which were placed on a road lane. The purpose of the research was to compare the lengths of mid-size cars, i.e., family cars (hatchbacks), saloons (sedans), station wagons and SUVs. Four methods were used in the research: a simple threshold based method, a threshold method based on moving average and standard deviation, a two-extreme-peak detection method and a method based on the amplitude and time normalization using linear extrapolation (or interpolation). The results were achieved by analyzing changes in the magnitude and in the absolute z-component of the magnetic field as well. The tests, which were performed in four different Earth directions, show differences in the values of estimated lengths. The magnitude-based results in the case when cars drove from the South to the North direction were even up to 1.2 m higher than the other results achieved using the threshold methods. Smaller differences in lengths were observed when the distances were measured between two extreme peaks in the car magnetic signatures. The results were summarized in tables and the errors of estimated lengths were presented. The maximal errors, related to real lengths, were up to 22%.

The proper operation of computing resources in a microprocessor-embedded system plays a key role in reducing computing time. Processing the variable amount of collected data in real-time improves the performance of a microprocessor-embedded system. In this regard, a vehicle’s speed measurement system is no exception. The computing time for evaluating any speed value is expected to be reduced as much as possible. Four computational methods, including cross-correlation, are discussed. An exemplary pair of recorded signals presenting the change in magnetic field magnitude is analyzed. The sample delay values are compared. The results of the evaluated speed and the execution time of the program code are presented for each method based on a dataset of 200 randomly driven vehicles. The results of the performed tests confirm that the cross-correlation-based methods are not always reliable in situations when the sample size is small, i.e., it is a segment of the impulse response caused by a driving vehicle.

With rapidly increasing traffic occupancy, intelligent transportation systems (ITSs) are a vital feature for urban areas. This paper analyses methods for estimating long (L > 10 m) vehicle speed and length using a self-developed system, equipped with two anisotropic magneto-resistive (AMR) sensors, and introduces a method for verifying the results. A well-known cross-correlation method of magnetic signatures is not appropriate for calculating the vehicle speed of long vehicles owing to limited resources and a long calculation time. Therefore, the adaptive signature cropping algorithm was developed and used with a difference quotient of a magnetic signature. An additional piezoelectric polyvinylidene fluoride (PVDF) sensor and video camera provide ground truth to evaluate the performances. The prototype system was installed on the urban road and tested under various traffic and weather conditions. The accuracy of results was evaluated by calculating the mean absolute percentage error (MAPE) for different methods and vehicle speed groups. The experimental result with a self-obtained data set of 600 unique entities shows that the average speed MAPE error of our proposed method is lower than 3% for vehicle speed in a range between 40 and 100 km/h.

Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are placed on a road lane. The piezoelectric polyvinylidene fluoride (PVDF) sensors are also mounted and used as a reference device. The methods applied in the research are well-known: the fixed threshold-based method and the adaptive two-extreme-peak detection method. However, the improved accuracy of estimating the length by using one of the methods, which is based on computing the difference quotient of a time-discrete signal (representing the changes in the magnitude of the magnetic field of the Earth), is observed. The obtained results, i.e., the speed and length of a vehicle, are presented for various values of the increment Δn used in numerical differentiation of magnetic field magnitude data. The results were achieved in real traffic conditions after analyzing a data set M = 290 of vehicle signatures. The accuracy was evaluated by calculating MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) for different classes of vehicles. The MAE is within the range of 0.52 m–1.18 m when using the appropriate calibration factor. The results are dependent on the distance between sensors, the speed of vehicle and the signal processing method applied.