BTCI: A new framework for identifying congestion cascades using bus trajectory data

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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Bibliographic Details
Main Authors: CHIANG, Meng-Fen, LIM, Ee Peng, LEE, Wang-Chien, KWEE, Agus Trisnajaya
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Roads
Trajectory
Silicon
Accidents
Data models
Aggregates
Databases
Computer Sciences
Databases and Information Systems
Theory and Algorithms
Online Access: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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Institution: Singapore Management University
Language: English
Description
Summary: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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