Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

Shi An; Haiqiang Yang; Jian Wang; Na Cui; Jianxun Cui

doi:10.1016/j.ins.2016.06.033

Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

Shi An
, Haiqiang Yang
, Jian Wang^*
, Na Cui
, Jianxun Cui

^*Corresponding author for this work

School of Transportation Science and Engineering, Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

77 Scopus citations

Abstract

In this study, we developed a method for measuring urban Recurrent Congestion (RC) evolution patterns based on grid divisions and GPS-equipped vehicle mobility data. The method uses a three-step process: Detecting congestion in the grids, distinguishing RC from Non-Recurrent Congestion (NRC), and measuring the RC evolution pattern. A series of indicators were also established which reflect the RC evolution pattern. We conducted an experiment to evaluate the proposed method using GPS trajectory data collected from taxis in Harbin, China, and compared the results against real traffic information and field survey results. We hope that the findings discussed in this paper provide a better understanding of urban RC evolution patterns.

Original language	English
Pages (from-to)	515-526
Number of pages	12
Journal	Information Sciences
Volume	373
DOIs	https://doi.org/10.1016/j.ins.2016.06.033
State	Published - 10 Dec 2016
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Congestion evolution pattern
GPS trajectory data
Recurrent congestion
Taxi
Urban road network

Access to Document

10.1016/j.ins.2016.06.033

Cite this

@article{b146168d77ff407e94ffb11ed5b7d9a9,

title = "Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data",

abstract = "In this study, we developed a method for measuring urban Recurrent Congestion (RC) evolution patterns based on grid divisions and GPS-equipped vehicle mobility data. The method uses a three-step process: Detecting congestion in the grids, distinguishing RC from Non-Recurrent Congestion (NRC), and measuring the RC evolution pattern. A series of indicators were also established which reflect the RC evolution pattern. We conducted an experiment to evaluate the proposed method using GPS trajectory data collected from taxis in Harbin, China, and compared the results against real traffic information and field survey results. We hope that the findings discussed in this paper provide a better understanding of urban RC evolution patterns.",

keywords = "Congestion evolution pattern, GPS trajectory data, Recurrent congestion, Taxi, Urban road network",

author = "Shi An and Haiqiang Yang and Jian Wang and Na Cui and Jianxun Cui",

note = "Publisher Copyright: {\textcopyright} 2016",

year = "2016",

month = dec,

day = "10",

doi = "10.1016/j.ins.2016.06.033",

language = "英语",

volume = "373",

pages = "515--526",

journal = "Information Sciences",

issn = "0020-0255",

publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

AU - An, Shi

AU - Yang, Haiqiang

AU - Wang, Jian

AU - Cui, Na

AU - Cui, Jianxun

PY - 2016/12/10

Y1 - 2016/12/10

N2 - In this study, we developed a method for measuring urban Recurrent Congestion (RC) evolution patterns based on grid divisions and GPS-equipped vehicle mobility data. The method uses a three-step process: Detecting congestion in the grids, distinguishing RC from Non-Recurrent Congestion (NRC), and measuring the RC evolution pattern. A series of indicators were also established which reflect the RC evolution pattern. We conducted an experiment to evaluate the proposed method using GPS trajectory data collected from taxis in Harbin, China, and compared the results against real traffic information and field survey results. We hope that the findings discussed in this paper provide a better understanding of urban RC evolution patterns.

AB - In this study, we developed a method for measuring urban Recurrent Congestion (RC) evolution patterns based on grid divisions and GPS-equipped vehicle mobility data. The method uses a three-step process: Detecting congestion in the grids, distinguishing RC from Non-Recurrent Congestion (NRC), and measuring the RC evolution pattern. A series of indicators were also established which reflect the RC evolution pattern. We conducted an experiment to evaluate the proposed method using GPS trajectory data collected from taxis in Harbin, China, and compared the results against real traffic information and field survey results. We hope that the findings discussed in this paper provide a better understanding of urban RC evolution patterns.

KW - Congestion evolution pattern

KW - GPS trajectory data

KW - Recurrent congestion

KW - Taxi

KW - Urban road network

UR - https://www.scopus.com/pages/publications/84978873840

U2 - 10.1016/j.ins.2016.06.033

DO - 10.1016/j.ins.2016.06.033

M3 - 文章

AN - SCOPUS:84978873840

SN - 0020-0255

VL - 373

SP - 515

EP - 526

JO - Information Sciences

JF - Information Sciences

ER -

Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

Abstract

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this