Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors |
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| Institution: | 1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong, 510641, PR China;2. Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 464-8603, Japan;1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Applied Statistics, School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;3. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208 USA;1. College of Engineering, Zhejiang Normal University, Zhejiang 321005, China;2. Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Zhejiang 321005, China;3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;4. Sichuan Vocational and Technical College of Communications, Chengdu, Sichuan 611130, China;1. Zachry Department of Civil and Environmental Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843-3136, United States;2. Center for Transportation Safety, Texas A&M Transportation Institute, 3135 TAMU, College Station, Texas, 77843-3135, United States;1. Alabama Transportation Institute, The University of Alabama Tuscaloosa, AL, United States;2. Department of Civil, Construction and Environmental Engineering, The University of Alabama Tuscaloosa, AL, United States;1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Center for Connected Multimodal Mobility, Clemson University, Clemson, SC 29634, USA;3. School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA |
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| Abstract: | Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, “adverse weather,” which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade. |
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| Keywords: | Freeway safety Crash injury severity Real-time weather data Multinomial logit model Spatial effect |
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