Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique. (July 2016)

• Record Type: Journal Article
• Authors: Miler, Mario; Todić, Filip; Ševrović, Marko
• Is Part Of: Transportation research. Volume 68(2016)
• Page Start: 185
• Page End: 193
• Publication Date: 2016-07
• Subjects: Traffic accident -- OpenStreetMap -- Jaro–Winkler -- Inverse Distance Weighting -- Data validation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2016.04.003
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A generic model for evaluating traffic accident locations is proposed. The potential use of OpenStreetMap in traffic accident analysis is evaluated. Traffic accidents street names are analyzed using the Jaro–Winkler metrics. Traffic accident locations are analyzed using Inverse Distance Weighting. The proposed model shows significant improvements in estimating correct locations. Abstract: The objective of this research was to develop a model for validating traffic accident locations that would be applicable worldwide, regardless of linguistic or cultural differences. In order to achieve this, a Volunteered Geographic Information (VGI) dataset was used, the OpenStreetMap (OSM) project. To test the developed model, a total of 8550 accidents with fatal or non-fatal injuries that occurred in the City of Zagreb from 2010 to 2014 were evaluated. Traffic accident data was collected using the pen-and-paper method while the traffic accident locations were determined using Global Positioning System (GPS) receivers embedded within police vehicles. This form of data entry invariably introduces errors in both geometric and contextual attributes. To fully counteract these errors, the developed model consists of two key concepts: the Jaro–Winkler string matching technique and the Inverse Distance Weighting method. Over 66% of traffic accident locations were validated, which is an increase of 15% when compared to the classical approach. The model outlined in this paper shows a significant improvement in estimating the correct location of traffic accidents. This in turn results in a drastic decrease in resources needed to estimate the quality of accident locations.