Title :
Detecting expressway traffic incident by traffic flow and robust statistics
Author :
Yang, Zhengling ; Song, Yanwen ; Wang, Teng ; Li, Yan
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
Abstract :
In order to detect expressway traffic incidents veritably, the probabilistic identifying results are suggested to demonstrate the traffic jam/congestion detection by traffic flow. First, the noise series in a traffic flow is decomposed by wavelet denoising and by second kind Fourier analysis denoising respectively. Second, hypothesis testing by chi-squared distribution is employed to sort out the robust variance (scale) estimators. Last, assuming that the detected flow point with possible congestion follows a normal distribution with the mean of the decomposed signal and the variance estimated from the decomposed noise series previously, the confidence levels of congestions are generated in a probabilistic form. Numerical experiments show that the probabilistic identifying results by this three steps method are reliable and reasonable.
Keywords :
Fourier analysis; automated highways; estimation theory; image denoising; normal distribution; object detection; road traffic; wavelet transforms; chi-squared distribution; congestion confidence levels; expressway traffic incident detection; hypothesis testing; noise series; normal distribution; probabilistic form; robust statistics; robust variance estimators; second kind Fourier analysis denoising; signal decomposition mean; traffic flow; traffic jam-congestion detection; variance estimation; wavelet denoising; Noise reduction; Probabilistic logic; Robustness; Time series analysis; Wavelet analysis; White noise; congestion; expressway traffic flow; robust statistics; time series; traffic incident detection;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4577-1414-6
DOI :
10.1109/CECNet.2012.6201448