A hybrid finite mixture model for exploring heterogeneous ordering patterns of driver injury severity. (April 2016)

• Record Type: Journal Article
• Authors: Ma, Lu; Wang, Guan; Yan, Xuedong; Weng, Jinxian
• Is Part Of: Accident analysis and prevention. Volume 89(2016)
• Page Start: 62
• Page End: 73
• Publication Date: 2016-04
• Subjects: Highway safety -- Mixture model -- EM algorithm -- Injury severity -- Heterogeneous ordering pattern; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.01.004
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: We develop a hybrid finite mixture (HFM) model for crash injury severity. The ordering pattern of severity could be a mixture of ordered and unordered features. The unordered pattern of injury severity might dominate over the ordered pattern. The HFM model is able to capture the heterogeneous ordering patterns of the data. The impacts of some robust factors are invariant to the structure of models. 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.