This study examines patterns in severe crashes that occur on mid-block segments of multilane arterials with partially limited access. A within stratum matched crash vs. non-crash classification approach is adopted towardsthat end. The approach does not require aggregation of crash data over arterial segments and therefore crashes serve as units of analysis. Also, the proposed approach doesn’t use information on non-severe crashes and hence is unaffected by under-reporting of the least severe crashes. Random samples of time, day of week, and location (i.e., milepost) combinations werecollected for arterials in the state of Florida and matched with severe crashes from the corresponding arterial to form matched strata consisting of crash and non-crash cases. For these cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. Preliminary analysis involving simple (one covariate) logistic regression models (based on within stratum matched sampling) was followed up by four multi-covariate models comparing severe crashes of different types with non-crash cases. The groups of crashes include severe rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes attributable to segments of multilane arterials. The results reveal that severe crashes of these types are affected by different factors. Severe lane-change related crashes may primarily be attributed to exposure while single-vehicle crashes and pedestrian crashes have no significant relationship with the average daily traffic (ADT). 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. Since the proposed approach uses general roadway characteristics and not 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.
Abstract