title: A novel approach for analyzing severe crash patterns on multilane highways.
abstract: This study presents a novel approach for analysis of patterns in severe crashes that occur on mid-block segments of multilane highways with partially limited access. A within stratum matched crash vs. non-crash classification approach is adopted towards that end. Under this approach crashes serve as units of analysis and it does not require aggregation of crash data over arterial segments of arbitrary lengths. Also, the proposed approach does not use information on non-severe crashes and hence is not affected by under-reporting of the minor crashes. Random samples of time, day of week, and location (i.e., milepost) combinations were collected for multilane arterials in the state of Florida and matched with severe crashes from the corresponding corridor to form matched strata consisting of severe crash and non-crash cases. For these cases, geometric design/roadside and traffic characteristics were derived based on the corresponding milepost locations. Four groups of crashes, severe rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes, on multilane arterials segments were compared separately to the non-crash cases. Severe lane-change related crashes may primarily be attributed to exposure while single-vehicle crashes and pedestrian crashes have no significant relationship with the ADT (Average Daily Traffic). For severe rear-end crashes speed limit, ADT, K-factor, time of day/day of week, median type, pavement condition, and presence of horizontal curvature were significant factors. The proposed approach uses general roadway characteristics as independent variables rather than event-specific information (i.e., crash characteristics such as driver/vehicle details); it has the potential to fit within a safety evaluation framework for arterial segments.
authors: Department of Civil & Environmental Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, United States. apande@calpoly.edu
authors: Anurag
authors: A
authors: Pande
authors: Mohamed
authors: M
authors: Abdel-Aty
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001288:Attention; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001336:Automobiles / Q000706:statistics & numerical data*; D016022:Case-Control Studies; D016208:Databases, Factual; D005431:Florida; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D015999:Multivariate Analysis; D010465:Perception; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D013223:Statistics as Topic