Abstract: Single-vehicle crash, as a common crash type of freeway crash, leads to great loss to society every year.  To reduce the negative impact brought by this type of crash, this study took freeway single-vehicle crash severity as dependent variable and constructed a econometric model to investigate the major influence factors. And some engineering and management countermeasures were put forward. The single-vehicle crash data of Guangdong Kaiyang freeway in 2013—2015 was collected. To describe the weather condition at the time when crash occurred more comprehensively, specifically and accurately, real-time weather data was matched to each crash according to the crash time and location. In terms of methodology, considering that the crash severity was ordered and there might exist heterogeneity in crash data, random parameter ordered logit model was established to fit the data. The results show that, the impact of vehicle type and humidity on crash severity has significant heterogeneity. The significant variables also include driver type, emergency medical services (EMS) response time, wind speed and crash time. Compared to non-professional driver, professional driver has a 6.22% higher probability for severe crash. Compared to other vehicles like coach, truck has a 0.59% lower probability for severe crash. Compared to daytime, the probability of severe crash at night decreases 0.31%. When the EMS response time increases 1min, the humidity increases 1%, and the wind speed decreases 1m/s, the probability of severe crash would increase 0.01%, 0.02% and 0.10% respectively. Finally, according to the results, some safety countermeasures were put forward.

Key words: freeway, single-vehicle crash, crash severity, ordered logit model, real-time weather data