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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2078

Title: Understanding spatial concentrations of road accidents using frequent item sets
Authors: GEURTS, Karolien
Thomas, I
WETS, Geert
Issue Date: 2005
Citation: ACCIDENT ANALYSIS AND PREVENTION, 37(4). p. 787-799
Abstract: This paper aims at understanding why road accidents tend to cluster in specific road segments. More particularly, it aims at analyzing which are the characteristics of the accidents occurring in "black" zones compared to those scattered all over the road. A technique of frequent item sets (data mining) is applied for automatically identifying accident circumstances that frequently occur together, for accidents located in and outside "black" zones. A Belgian periurban region is used as case study. Results show that accidents occurring in "black" zones are characterized by left-turns at signalized intersections, collisions with pedestrians, loss control of the vehicle (run-off-roadway) and rainy weather conditions. Accidents occurring outside "black" zones (scattered in space) are characterized by left turns on intersections with traffic signs, head-on collisions and drunken road user(s). Furthermore, parallel collisions and accidents on highways or roads with separated lanes, occurring at night or during the weekend are frequently occurring accident patterns for all accident locations. These exploratory results show the potentiality of the frequent item set method in addition to more classical statistical techniques, but also suggest that there is no unique countermeasure for reducing the number of accidents. (c) 2005 Elsevier Ltd. All rights reserved.
Notes: Limburgs Univ Ctr, Transportat Res Inst, B-3590 Diepenbeek, Belgium. Natl Fund Sci Res, B-1348 Louvain, Belgium. Univ Catholique Louvain, Dept Geog, B-1348 Louvain, Belgium.Wets, G, Limburgs Univ Ctr, Transportat Res Inst, Univ Campus,Gebouw D, B-3590 Diepenbeek, Belgium.geertwets@luc.ac.be
URI: http://hdl.handle.net/1942/2078
DOI: 10.1016/j.aap.2005.03.023
ISI #: 000230247700023
ISSN: 0001-4575
Category: A1
Type: Journal Contribution
Validation: ecoom, 2006
Appears in Collections: Research publications

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