Abstract
This study investigates factors contributing to crash severity on suburban highways and compares discrete-choice methods under a multisource data-fusion framework. Ordered logit, multinomial logit, and mixed logit models are applied to integrated crash and traffic-counter data from Khorasan Razavi province. The data cover geometric design, time, weather and environmental conditions, land use, traffic, vehicle characteristics, and driver characteristics. The mixed-logit model provides the best fit. Significant effects across the models demonstrate the value of combining multiple data sources when identifying factors that influence crash severity.
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