Abstract
Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.
Highlights
Introduction published maps and institutional affilWith the ongoing rise of vehicle number in the streets, the road traffic sector requires intelligent systems to mitigate congestions by monitoring and controlling the traffic
The average mismatch between two aligned curves is 5%, and 95% of all cases criterion below criterion below 10%
Thesecond methods wereconsisted tested with different datasets
Summary
With the ongoing rise of vehicle number in the streets, the road traffic sector requires intelligent systems to mitigate congestions by monitoring and controlling the traffic. For streets with volatile traffic direction, are already a must-have thing in modern city life. The first step for this kind of system is to collect useful road traffic information: vehicle volume, speed, length, type and direction [1,2,3]. This research aims to explore practical traffic classification possibilities, based on data collected during real-life traffic conditions, using a single magnetic AMR sensor hub. The key parameter for vehicle classification is length, whose estimation is dependent on speed measurement. It was noticed that using the cross-correlation method to estimate speed produces a high error due to uneven magnetic signals.
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