Citation

Bartin B, Ozbay K, Xu C. Transp. Res. Rec. 2019; 2673(11): 264-275.

Copyright

(Copyright © 2019, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198119850789

PMID

unavailable

Abstract

This paper presents the use of a clustering method for automatically estimating horizontal curvature data and crash modification factors (CMFs) using Geographic Information System (GIS) roadway shapefiles. The clustering method identifies distinct sections on a roadway, either curved or tangent, based on the proximity of the approximated curvature values of data points from GIS roadway centerline shapefiles, and calculates horizontal curvature data and the corresponding CMFs. The results of the clustering method are compared with two other methods: (1) the mobile access vehicle method based on field GPS measurements and (2) the manual data extraction method based on satellite images. The comparison was conducted on a total of 24.7 mi of four NJ rural two-lane roads. The results showed that the CMFs estimated by the clustering method were within 12.2 and 15.5% of the ones produced by the mobile asset vehicle and the manual data extraction method, respectively. In addition, the sensitivity of the manually extracted horizontal curvature data was examined by conducting three additional independent trials. The average percent difference in the calculated CMFs between trials was 15.5%. This study therefore concludes that the clustering method can produce CMF estimates as accurate as the two other methods method much more efficiently in relation to time and money.

Language: en