A hybrid finite mixture model for exploring heterogeneous ordering patterns of driver injury severity |
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| Affiliation: | 1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China;2. College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, PR China;1. Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue W., Waterloo, Ontario, Canada N2L 3G1;2. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Mailbox 125, No. 1040 Heping Road, Wuhan, Hubei 430063, China;3. Department of Biostatistics and Epidemiology, McGill University, 687 Pine Avenue West, Montreal, Canada;4. Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke St. W., Montreal, Quebec, Canada H3A 2K6;1. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, 150090, China;2. Center for Road Safety, Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA;1. Department of Preventive and Social Medicine, University of Otago, PO Box 56, Dunedin 9054, New Zealand;2. Department of Transport, Technical University of Denmark, Bygningstorvet 116B, 2800 Kgs. Lyngby, Denmark;1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China;2. Dept. of Civil and Coastal Engineering, University of Florida, 513-A Weil Hall, PO Box 116580, Gainesville, FL 32611, United States;1. Road and Traffic Key Laboratory, Ministry of Education, Shanghai 201804, China;2. College of Transportation Engineering, Tongji University,4800 Cao''an Road, Shanghai 201804, China;3. Department of Civil, Environmental and Construction Engineering, University of Central Florida Orlando, FL 32826-2450, United States |
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| Abstract: | Debates on the ordering patterns of crash injury severity are ongoing in the literature. Models without proper econometrical structures for accommodating the complex ordering patterns of injury severity could result in biased estimations and misinterpretations of factors. This study proposes a hybrid finite mixture (HFM) model aiming to capture heterogeneous ordering patterns of driver injury severity while enhancing modeling flexibility. It attempts to probabilistically partition samples into two groups in which one group represents an unordered/nominal data-generating process while the other represents an ordered data-generating process. Conceptually, the newly developed model offers flexible coefficient settings for mining additional information from crash data, and more importantly it allows the coexistence of multiple ordering patterns for the dependent variable. A thorough modeling performance comparison is conducted between the HFM model, and the multinomial logit (MNL), ordered logit (OL), finite mixture multinomial logit (FMMNL) and finite mixture ordered logit (FMOL) models. According to the empirical results, the HFM model presents a strong ability to extract information from the data, and more importantly to uncover heterogeneous ordering relationships between factors and driver injury severity. In addition, the estimated weight parameter associated with the MNL component in the HFM model is greater than the one associated with the OL component, which indicates a larger likelihood of the unordered pattern than the ordered pattern for driver injury severity. |
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| Keywords: | Highway safety Mixture model EM algorithm Injury severity Heterogeneous ordering pattern |
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