Abstract
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.
Highlights
Recent technological advances have revolutionized intelligent transport systems (ITSs) in terms of data collection, traffic management, and control
A novel speed estimation algorithm for long vehicles is proposed based on a pair of magnetic sensors and evaluated with reference piezo-electric sensors
The proposed approach is enabled by two nodes of the magnetic field sensor spaced by 0.3 m, which are deployed on an intercity two-lane road, where average traffic speed is 85 km/h
Summary
Recent technological advances have revolutionized intelligent transport systems (ITSs) in terms of data collection, traffic management, and control. The integrated magnetic field sensor is superior to other sensing technologies owing to its unique advantages, such as being insensitive to climate and weather conditions, being less susceptible than loops to stresses of traffic, its low cost, its small volume, and its comparability to wireless communication [4]. The use of this technology is mostly based on the anisotropic magneto-resistive (AMR)-type sensor. Cross-correlation is a base method for accurate speed estimation from a pair of magnetic signatures
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