BTCI: A new framework for identifying congestion cascades using bus trajectory data
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...
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sg-smu-ink.sis_research-49732020-03-24T03:03:42Z BTCI: A new framework for identifying congestion cascades using bus trajectory data CHIANG, Meng-Fen LIM, Ee Peng LEE, Wang-Chien KWEE, Agus Trisnajaya 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. 2017-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3971 info:doi/10.1109/BigData.2017.8258039 https://ink.library.smu.edu.sg/context/sis_research/article/4973/viewcontent/8._BTCI_a_New_Framwork_for_Identifying_Congestion_Cascades_Using_Bus_Trajectory_Data__IEEE_BigData2017_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Roads Trajectory Silicon Accidents Data models Aggregates Databases Computer Sciences Databases and Information Systems Theory and Algorithms |
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Roads Trajectory Silicon Accidents Data models Aggregates Databases Computer Sciences Databases and Information Systems Theory and Algorithms CHIANG, Meng-Fen LIM, Ee Peng LEE, Wang-Chien KWEE, Agus Trisnajaya BTCI: A new framework for identifying congestion cascades using bus trajectory data |
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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. |
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author |
CHIANG, Meng-Fen LIM, Ee Peng LEE, Wang-Chien KWEE, Agus Trisnajaya |
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CHIANG, Meng-Fen LIM, Ee Peng LEE, Wang-Chien KWEE, Agus Trisnajaya |
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CHIANG, Meng-Fen |
title |
BTCI: A new framework for identifying congestion cascades using bus trajectory data |
title_short |
BTCI: A new framework for identifying congestion cascades using bus trajectory data |
title_full |
BTCI: A new framework for identifying congestion cascades using bus trajectory data |
title_fullStr |
BTCI: A new framework for identifying congestion cascades using bus trajectory data |
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BTCI: A new framework for identifying congestion cascades using bus trajectory data |
title_sort |
btci: a new framework for identifying congestion cascades using bus trajectory data |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/3971 https://ink.library.smu.edu.sg/context/sis_research/article/4973/viewcontent/8._BTCI_a_New_Framwork_for_Identifying_Congestion_Cascades_Using_Bus_Trajectory_Data__IEEE_BigData2017_.pdf |
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