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Discovering Traffic Anomaly Propagation in Urban Space Using Traffic Change Peaks

Version 2 2024-06-06, 05:44
Version 1 2020-03-16, 08:35
conference contribution
posted on 2024-06-06, 05:44 authored by GL Huang, Y Ji, S Liu, R Zarei
© Springer Nature Singapore Pte Ltd 2020. Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as STOTree, are not accurate for two reasons. First, they discover the propagation pattern based on the detected anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal neighborhoods rather than considering from a global perspective, and thus cannot find a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using traffic change peaks, which can visualize the change of traffic anomalies (e.g., congestion and evacuation area) and thus accurately captures traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time period can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0–255) of pixels. An adaptive filter is developed to generate traffic change graphs from grid traffic images in consecutive periods, and clustering traffic change peaks along the road is to discover the propagation of traffic anomalies. The effectiveness of the proposed method has been demonstrated using a real-world GPS trajectory dataset.

History

Volume

1179

Pagination

67-76

Location

Ningbo, China

Start date

2019-05-15

End date

2019-05-20

ISSN

1865-0929

eISSN

1865-0937

ISBN-13

9789811528095

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

He J, Yu PS, Shi Y, Li X, Xie Z, Huang G, Cao J, Xiao F

Title of proceedings

ICDS 2019 : Data science : 6th international conference, ICDS 2019, Ningbo, China, May 15-20, 2019, revised selected papers

Event

Data Service. Conference (2019 : Ningbo, China)

Publisher

Springer

Place of publication

Berlin, Germany

Series

Communications in Computer and Information Science

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