scholarly journals Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5234 ◽  
Author(s):  
Markevicius ◽  
Navikas ◽  
Idzkowski ◽  
Miklusis ◽  
Andriukaitis ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Intelligent Transportation Systems ◽  
Polyvinylidene Fluoride ◽  
Mean Squared Error ◽  
Numerical Differentiation ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Vehicle Speed ◽  
Estimation Errors ◽  
Data Set

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.

scholarly journals Analysis of Methods for Long Vehicles Speed Estimation Using Anisotropic Magneto-Resistive (AMR) Sensors and Reference Piezoelectric Sensor

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3541
Author(s):  
Vytautas Markevicius ◽  
Dangirutis Navikas ◽  
Donatas Miklusis ◽  
Darius Andriukaitis ◽  
Algimantas Valinevicius ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Polyvinylidene Fluoride ◽  
Correlation Method ◽  
Transportation Systems ◽  
Experimental Result ◽  
Percentage Error ◽  
Prototype System ◽  
Vehicle Speed ◽  
Data Set

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.

Framework for Estimating the Impact of Camera-Based Intelligent Transportation Systems (ITS) Technology on Incident Duration

2018 ◽  
Vol 2672 (19) ◽  
pp. 25-33 ◽  
Author(s):  
Emily Moylan ◽  
Sai Chand ◽  
S. Travis Waller
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Set ◽  
Support Tool ◽  
Detection Delay ◽  
South Wales ◽  
Incident Duration ◽  
The Difference ◽  
The Impact

Safety is a major motivator of intelligent transportation systems (ITS) projects, and most efforts have addressed the potential to avoid incidents. Managing and reducing the duration of incidents is another key application for ITS despite challenges in distinguishing the true versus the reported duration of an incident. This paper presents a framework for modeling the impact of camera-based (closed-circuit television or CCTV) ITS technology on incident duration including an increase in the reported duration and a reduction in the true duration. The framework is validated against a data set of 121,793 accidents in New South Wales, Australia, covering 4.5 years. The results demonstrate that the use of CCTVs for incident duration contributes a 4.5 min reduction in average duration (as earlier detection can lead to more efficient clearance) and a 9% reduction in variance in the duration (as a uniform detection method supports standardized response procedures). These impacts are only visible when the 8.5 min median detection delay (the difference between the recorded duration and the true duration) is modeled and accounted for. These results offer a quantitative support tool for decision makers wishing to assess the value of incident-detection ITS projects.

scholarly journals Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

2019 ◽  
Vol 11 (4) ◽  
pp. 42-54 ◽  
Author(s):  
Ali Tourani ◽  
Asadollah Shahbahrami ◽  
Alireza Akoushideh ◽  
Saeed Khazaee ◽  
Ching. Y Suen
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Vehicle Speed ◽  
Speed Measurement

scholarly journals Double Magnetic Loop and Methods for Calculating Its Inductance

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ferran Mocholí Belenguer ◽  
Antonio Mocholí Salcedo ◽  
Victor Milián Sánchez ◽  
José Humberto Arroyo Núñez
Keyword(s):  
Magnetic Field ◽  
Research Group ◽  
Intelligent Transportation Systems ◽  
Theoretical Study ◽  
Magnetic Loop ◽  
Operating Mode ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Experimental Measurements ◽  
Traffic Sensors

Due to their simplicity and operating mode, magnetic loops are one of the most used traffic sensors in Intelligent Transportation Systems (ITS). However, at this moment, their potential is not being fully exploited, as neither the speed nor the length of the vehicles can be surely ascertained with the use of a single magnetic loop. In this way, the vast majority of them are only being used to count vehicles on urban and interurban roads. For this reason, in order to contribute to the development of new traffic sensors and make roads safer, this paper introduces a theoretical study to explain the design and peculiarities of the innovative double loops, how to calculate their magnetic field and three different methods to calculate their inductance. Finally, the different inductance values obtained by these three methods will be analyzed and compared with experimental measurements carried out by our research group in order to know which method is more accurate and if all of them are equally reliable.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

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