Assessing the role of pavement macrotexture in preventing crashes on highways.
OBJECTIVE
The objective of this article is to assess the role of pavement macrotexture in preventing crashes on highways in the State of North Carolina.


METHODS
Laser profilometer data obtained from the North Carolina Department of Transportation (NCDOT) for highways comprising four corridors are processed to calculate pavement macrotexture at 100-m (approximately 330-ft) sections according to the American Society for Testing and Materials (ASTM) standards. Crash data collected over the same lengths of the corridors were integrated with the calculated pavement macrotexture for each section. Scatterplots were generated to assess the role of pavement macrotexture on crashes and logarithm of crashes. Regression analyses were conducted by considering predictor variables such as million vehicle miles of travel (as a function of traffic volume and length), the number of interchanges, the number of at-grade intersections, the number of grade-separated interchanges, and the number of bridges, culverts, and overhead signs along with pavement macrotexture to study the statistical significance of relationship between pavement macrotexture and crashes (both linear and log-linear) when compared to other predictor variables.


RESULTS
Scatterplots and regression analysis conducted indicate a more statistically significant relationship between pavement macrotexture and logarithm of crashes than between pavement macrotexture and crashes. The coefficient for pavement macrotexture, in general, is negative, indicating that the number of crashes or logarithm of crashes decreases as it increases. The relation between pavement macrotexture and logarithm of crashes is generally stronger than between most other predictor variables and crashes or logarithm of crashes.


CONCLUSIONS
Based on results obtained, it can be concluded that maintaining pavement macrotexture greater than or equal to 1.524 mm (0.06 in.) as a threshold limit would possibly reduce crashes and provide safe transportation to road users on highways.
Pulugurtha
Srinivas S
SS
Civil & Environmental Engineering, Center for Transportation Policy Studies, The University of North Carolina at Charlotte, Charlotte, NC 28223-0001, USA. sspulugurtha@uncc.edu
Kusam
Prasanna R
PR
Patel
Kuvleshay J
KJ
D000056:Accident Prevention / Q000379:methods*; D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D000465:Algorithms; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D009657:North Carolina; D012044:Regression Analysis; D012306:Risk