Detecting Regularities of Traffic Signal Timing Using GPS Trajectories

Juan YU; Peizhong LU; Jianmin HAN; Jianfeng LU

doi:10.1587/transinf.2016IIP0022

Detecting Regularities of Traffic Signal Timing Using GPS Trajectories

Juan YU, Peizhong LU, Jianmin HAN, Jianfeng LU

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.956-963

Publication Date: 2018/04/01

Publicized: 2018/01/19

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

Authors

Juan YU
  Hangzhou Dianzi University,Fudan University
Peizhong LU
  Fudan University
Jianmin HAN
  Zhejiang Normal University
Jianfeng LU
  Zhejiang Normal University

Keyword

taxi GPS trajectory, traffic signals, cycle length estimation

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Juan YU, Peizhong LU, Jianmin HAN, Jianfeng LU, "Detecting Regularities of Traffic Signal Timing Using GPS Trajectories" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 4, pp. 956-963, April 2018, doi: 10.1587/transinf.2016IIP0022.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016IIP0022/_p

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@ARTICLE{e101-d_4_956,
author={Juan YU, Peizhong LU, Jianmin HAN, Jianfeng LU, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Regularities of Traffic Signal Timing Using GPS Trajectories},
year={2018},
volume={E101-D},
number={4},
pages={956-963},
abstract={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.},
keywords={},
doi={10.1587/transinf.2016IIP0022},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Detecting Regularities of Traffic Signal Timing Using GPS Trajectories
T2 - IEICE TRANSACTIONS on Information
SP - 956
EP - 963
AU - Juan YU
AU - Peizhong LU
AU - Jianmin HAN
AU - Jianfeng LU
PY - 2018
DO - 10.1587/transinf.2016IIP0022
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - 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.
ER -