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 (Po) and the following vehicle's reaction failure (Pf). The results show for example that truck percentage-mile-per-lane has a dual impact, it increases Po and decreases Pf, 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 Po and increases Pf, 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.

Kim, J.K., Y. Wang, and G.F. Ulfarsson. Modeling the Probability of Freeway Rear-End Crash Occurrence. In Journal of Transportation Engineering, Vol 133, No. 1, American Society for Civil Engineers, Reston, V.A., Jan 2007, pp. 11-19.