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conference paper not in proceedings

Methodology for Developing Real-time Motorway Traffic Risk Identification Models Using Individual Vehicle Data

Pham, Minh-Hai  
•
Bhaskar, Ashish  
•
Chung, Edward
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2011
Transportation Research Board Annual Meeting

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

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Type
conference paper not in proceedings
Author(s)
Pham, Minh-Hai  
•
Bhaskar, Ashish  
•
Chung, Edward
•
Dumont, André-Gilles  
Date Issued

2011

Subjects

Traffic safety

•

motorway

•

online motorway risk identification model

•

Accident prevention

•

data mining

•

random forest regression

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LAVOC  
NEARCTIS
Event nameEvent placeEvent date
Transportation Research Board Annual Meeting

Washington DC, USA

January 24-27, 2011

Available on Infoscience
October 21, 2010
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/55776
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