Modeling the probability of freeway rear-end crash occurrence

A microscopic model of freeway rear-end crash risk is developed based on a modified negative binomial regression and estimated using Washington State data. Compared with most existing models, this model has two major advantages: (1) It directly considers a driver's response time distribution; and (2) it applies a new dual-impact structure accounting for the probability of both a vehicle becoming an obstacle (P-o) and the following vehicle's reaction failure (P-f). The results show for example that truck percentage-mile-per-lane has a dual impact, it increases P-o and decreases P-f, yielding a net decrease in rear-end crash probabilities. Urban area, curvature, off-ramp and merge, shoulder width, and merge section are factors found to increase rear-end crash probabilities. Daily vehicle miles traveled (VMT) per lane has a dual impact; it decreases P-o and increases P-f, yielding a net increase, indicating for example that focusing VMT related safety improvement efforts on reducing drivers' failure to avoid crashes, such as crash-avoidance systems, is of key importance. Understanding such dual impacts is important for selecting and evaluating safety improvement plans for freeways.