Detecting Regularities of Traffic Signal Timing Using GPS Trajectories

Juan YU  Peizhong LU  Jianmin HAN  Jianfeng LU  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.4   pp.956-963
Publication Date: 2018/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016IIP0022
Type of Manuscript: Special Section PAPER (Special Section on Intelligent Information and Communication Technology and its Applications to Creative Activity Support)
Category: Technologies for Knowledge Support Platform
taxi GPS trajectory,  traffic signals,  cycle length estimation,  

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Traffic signal phase and timing (TSPaT) information is valuable for various applications, such as velocity advisory systems, navigation systems, collision warning systems, and so forth. In this paper, we focus on learning baseline timing cycle lengths for fixed-time traffic signals. The cycle length is the most important parameter among all timing parameters, such as green lengths. We formulate the cycle length learning problem as a period estimation problem using a sparse set of noisy observations, and propose the most frequent approximate greatest common divisor (MFAGCD) algorithms to solve the problem. The accuracy performance of our proposed algorithms is experimentally evaluated on both simulation data and the real taxi GPS trajectory data collected in Shanghai, China. Experimental results show that the MFAGCD algorithms have better sparsity and outliers tolerant capabilities than existing cycle length estimation algorithms.