Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2017
Abstract
The knowledge of traffic health status is essential to the general public and urban traffic management. To identify congestion cascades, an important phenomenon of traffic health, we propose a Bus Trajectory based Congestion Identification (BTCI) framework that explores the anomalous traffic health status and structure properties of congestion cascades using bus trajectory data. BTCI consists of two main steps, congested segment extraction and congestion cascades identification. The former constructs path speed models from historical vehicle transitions and design a non-parametric Kernel Density Estimation (KDE) function to derive a measure of congestion score. The latter aggregates congested segments (i.e., those with high congestion scores) into traffic congestion cascades by unifying both attribute coherence and spatio-temporal closeness of congested segments within a cascade. Extensive evaluations on 11.8 million bus trajectory data show that (1) BTCI can effectively identify congestion cascades, (2) the proposed congestion score is effective in extracting congested segments, (3) the proposed unified approach significantly outperforms alternative approaches in terms of extended precision, and (4) the identified congestion cascades are realistic, matching well with the traffic news and highly correlated with vehicle speed bands.
Keywords
Roads, Trajectory, Silicon, Accidents, Data models, Aggregates, Databases
Discipline
Computer Sciences | Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
2017 IEEE International Conference on Big Data: Boston, December 11-14: Proceedings
First Page
1133
Last Page
1142
ISBN
9781538627143
Identifier
10.1109/BigData.2017.8258039
Publisher
IEEE
City or Country
Pistacaway, NJ
Citation
CHIANG, Meng-Fen; LIM, Ee Peng; LEE, Wang-Chien; and KWEE, Agus Trisnajaya.
BTCI: A new framework for identifying congestion cascades using bus trajectory data. (2017). 2017 IEEE International Conference on Big Data: Boston, December 11-14: Proceedings. 1133-1142.
Available at: https://ink.library.smu.edu.sg/sis_research/3971
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/BigData.2017.8258039