The growing concern about the possible safety-related impacts of Intelligent Transportation Systems (ITS) has focused attention on the need to develop new statistical approaches to predict accident severity. This paper presents a nested logit formulation as a means for determining accident severity given that an accident has occurred. Four levels of severity are considered: (1) property damage only, (2) possible injury, (3) evident injury, and (4) disabling injury or fatality. Using 5-year accident data from a 61 km section of rural interstate in Washington State (which has been selected as an ITS demonstration site), we estimate a nested logit model of accident severity. The estimation results provide valuable evidence on the effect that environmental conditions, highway design, accident type, driver characteristics and vehicle attributes have on accident severity. Our findings show that the nested logit formulation is a promising approach to evaluate the impact that ITS or other safety-related countermeasures may have on accident severities.
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Safety, Risk, Reliability and Quality
- Public Health, Environmental and Occupational Health