期刊
ANALYTIC METHODS IN ACCIDENT RESEARCH
卷 33, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.amar.2021.100191
关键词
Random parameters; Crash severity; Marginal effects; Temporal instability; Predictive performance
资金
- National Natural Science Foundation of China [71901081]
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University [K202103]
- Natural Science Foundation of Shandong Province [ZR2020MG020]
- Center for Teaching Old Models New Tricks (TOMNET) - US Department of Transportation [69A3551747116]
- Center for Transportation, Environment, and Community Health (CTECH), University Transportation Centers - US Department of Transportation [69A3551747119]
Random parameters logit models have become popular for investigating crash-injury severities, with the model incorporating heterogeneity in means and variances showing superior predictive performance. Different methods for computing marginal effects and testing temporal instability were considered, with pairwise comparison proving to provide more detailed insights into temporal variability.
Random parameters logit models have become an increasingly popular method to investigate crash-injury severities in recent years. However, there remain potential elements of the approach that need clarification including out-of-sample prediction, the calculation of marginal effects, and temporal instability testing. In this study, four models are considered for comparison: a fixed parameters multinomial logit model; a random parameters logit model; a random parameters logit model with heterogeneity in means; and a random parameters logit model with heterogeneity in means and variances. A full simulation of random parameters is undertaken for out-of-sample injury-severity predictions, and the prediction accuracy of the estimated models was assessed. Results indicate, not surprisingly, that the random parameters logit model with heterogeneity in the means and variances outperformed other models in predictive performance. Following this, two alternative methods for computing marginal effects are considered: one using Monte Carlo simulation and the other using individual estimates of random parameters. The empirical results indicate that both methods produced defensible results since the full distributions of random parameters are considered. Finally, two testing alternatives for temporal instability are evaluated: a global test across all time periods being considered, and a pairwise time-period to time-period comparison. It is shown that the pairwise comparison can provide more detailed insights into possible temporal variability. (C) 2021 Elsevier Ltd. All rights reserved.
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