Comparison of corridor-level fatal and injury crash models with site-level models for network screening purposes on Florida urban and suburban divided arterials

Objective: Develop corridor-level network screening models to identify high-risk corridors where safety improvements could be implemented to reduce fatal and injury (FI) crashes. Methods: A novel corridor definition focused on context classification and lane count was developed and applied to urban and suburban four-lane divided arterial roadways in Florida. Negative binomial regression models were developed for multi- and single-vehicle crashes using 80% of the corridors (training set). Crash frequency predictions were obtained from the developed corridor models and similar site-level models from the Highway Safety Manual (HSM) models for the remaining 20% of the corridors (testing set). Results from all models were adjusted using the empirical Bayes (EB) method. Results: A total of 130 corridors were identified across seven counties. These corridors contained approximately 349 km (217 miles) of roadway and experienced 11,437 multi-vehicle and 746 single-vehicle crashes that resulted in fatalities or injuries from 2017 to 2021. After applying the HSM site-level models and the developed corridor-level models to the testing set (both with and without EB adjustments), the corridor-level models with EB adjustments were the most accurate for corridor crash prediction. Applying the corridor-level models with EB adjustments to the testing set gave a predicted value of 386.44 crashes/year, which was the closest to the observed crash frequency of 383.20 crashes/year. From the corridor-level models, a 3.48-km (2.16-mile) high-risk corridor in Miami-Dade County was identified and analyzed site-by-site using the HSM methodology to identify specific sites within the corridor where safety improvements could provide the most FI crash reductions. Conclusions: The corridor-level models were more accurate and statistically reliable than similar HSM models while being less data intensive. They also only required corridor-level data rather than data for each intersection and segment. By using readily available data, the methods in this paper can be easily replicated by agencies to develop their own network screening corridor-level models and expedite the identification of corridors in need of safety improvements to reduce FI crashes. Existing site-level network screening methods can be used to supplement the developed corridor-level methodology by identifying high-risk sites within identified high-risk corridors.