Citation

Imprialou MIM, Quddus M, Pitfield DE. Transp. Res. Rec. 2015; 2514: 39-47.

Copyright

(Copyright © 2015, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.3141/2514-05

PMID

unavailable

Abstract

The spatial nature of traffic crashes makes crash locations one of the most important and informative attributes of crash databases. It is, however, very likely that crash locations recorded in terms of easting and northing coordinates, distances from junctions, addresses, road names, and types are inaccurately reported. Improving the quality of crash location mapping therefore has the potential to enhance the accuracy of many spatial crash analyses. Determination of correct crash locations usually requires a combination of crash and network attributes with suitable crash-mapping methods. Urban road networks are more sensitive to erroneous matches because of high road density and inherent complexity. A novel crash-mapping method is presented; it is suitable for urban and metropolitan areas and matches all the crashes that occurred in London from 2010 to 2012. The method is based on a hierarchical data structure of crashes (i.e., candidate road links are nested within vehicles and vehicles are nested within crashes) and employs a multilevel logistic regression model to estimate the probability distribution of mapping a crash onto a set of candidate road links. The road link with the highest probability is considered to be the correct segment for mapping the crash. This method is based on two primary variables: (a) distance between the crash location and a candidate segment and (b) difference between the vehicle direction just before the collision and the link direction. Despite the fact that road names were not considered because of the limited availability of this variable in the applied crash database, the developed method provides 97.1% (±1%) accurate matches (N = 1,000). The method was compared with two simpler, nonprobabilistic crash-mapping algorithms, and the results were used to demonstrate the effect of crash location data quality on a crash risk analysis.