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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18445

Title: Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development using Loop Detector Data
Authors: PIRDAVANI, Ali
DE PAUW, Ellen
BRIJS, Tom
DANIELS, Stijn
Magis, Maarten
BELLEMANS, Tom
WETS, Geert
Issue Date: 2015
Citation: Traffic Injury Prevention, 16 (8), p. 786-791
Abstract: Objectives: There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g. collected by loop detectors). The main objective of this paper is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways. Methods: In this study, the potential prediction variables are confined to traffic related characteristics. Given that the dependent variable (i.e. traffic safety condition) is dichotomous (i.e. “no-crash” or “crash”), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction towards Antwerp. Results: The results of analysis show that several traffic flow characteristics such as traffic volume, average speed and standard deviation of speed at the upstream loop detector station, and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately while it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate. Conclusions: The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems.
Notes: Pirdavani, A (reprint author) Hasselt Univ, Sch Transportat Sci, Transportat Res Inst IMOB, Wetenschapspk 5, BE-3590 Diepenbeek, Belgium. ali.pirdavani@uhasselt.be
URI: http://hdl.handle.net/1942/18445
Link to publication: http://dx.doi.org/10.1080/15389588.2015.1017572
DOI: 10.1080/15389588.2015.1017572
ISI #: 000361532200007
ISSN: 1538-9588
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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