Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17926
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPIRDAVANI, Ali-
dc.contributor.authorDE PAUW, Ellen-
dc.contributor.authorDANIELS, Stijn-
dc.contributor.authorMagis, Maarten-
dc.contributor.authorBRIJS, Tom-
dc.contributor.authorBELLEMANS, Tom-
dc.contributor.authorWETS, Geert-
dc.date.accessioned2014-12-04T14:13:36Z-
dc.date.available2014-12-04T14:13:36Z-
dc.date.issued2015-
dc.identifier.citation94th Annual Meeting of the Transportation Research Board, Washington D.C., USA, 11-15/01/2015-
dc.identifier.urihttp://hdl.handle.net/1942/17926-
dc.description.abstractThere is a growing trend in development and application of real-time crash prediction models within dynamic safety management systems. These real-time crash prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g. collected by loop detectors). 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. The main objective of this paper is to develop a real-time prediction model that will potentially be utilized within traffic management systems. This model aims to predict the traffic safety condition of motorways. 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 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. The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems.-
dc.language.isoen-
dc.titleApplication of a Rule-Based Approach in Real-Time Crash Prediction Model Development for Motorways-
dc.typeConference Material-
local.bibliographicCitation.conferencedate11-15/01/2015-
local.bibliographicCitation.conferencename94th Annual Meeting of the Transportation Research Board-
local.bibliographicCitation.conferenceplaceWashington D.C., USA-
local.bibliographicCitation.jcatC2-
local.type.refereedRefereed-
local.type.specifiedPaper-
item.fulltextNo Fulltext-
item.contributorDANIELS, Stijn-
item.contributorBRIJS, Tom-
item.contributorWETS, Geert-
item.contributorBELLEMANS, Tom-
item.contributorDE PAUW, Ellen-
item.contributorPIRDAVANI, Ali-
item.contributorMagis, Maarten-
item.fullcitationPIRDAVANI, Ali; DE PAUW, Ellen; DANIELS, Stijn; Magis, Maarten; BRIJS, Tom; BELLEMANS, Tom & WETS, Geert (2015) Application of a Rule-Based Approach in Real-Time Crash Prediction Model Development for Motorways. In: 94th Annual Meeting of the Transportation Research Board, Washington D.C., USA, 11-15/01/2015.-
item.accessRightsClosed Access-
Appears in Collections:Research publications
Show simple item record

Page view(s)

18
checked on May 19, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.