  While it is believed that denoising is not always necessary in many big data
applications, we show in this paper that denoising is helpful in urban traffic
analysis by applying the method of bounded total variation denoising to the
urban road traffic prediction and clustering problem. We propose two
easy-to-implement methods to estimate the noise strength parameter in the
denoising algorithm, and apply the denoising algorithm to GPS-based traffic
data from Beijing taxi system. For the traffic prediction problem, we combine
neural network and history matching method for roads randomly chosen from an
urban area of Beijing. Numerical experiments show that the predicting accuracy
is improved significantly by applying the proposed bounded total variation
denoising algorithm. We also test the algorithm on clustering problem, where a
recently developed clustering analysis method is applied to more than one
hundred urban road segments in Beijing based on their velocity profiles. Better
clustering result is obtained after denoising.
