Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

Several studies have reported the superior performance of the Negative Binomial–Lindley (NB-L) compared to the commonly used Negative Binomial distribution. Consequently, different parameterisations of the NB-L distribution have been introduced to further improve crash data modelling. However, little is known on how these models perform for different data domains. This study is documenting a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and the negative binomial quasi-Lindley (NB-QL). The results show that the NB-WLindley distribution performed better for the majority of data domains. Also, its generalised linear model (NB-WLindley GLM) showed superior statistical performance relative to the NB GLM and NB-L GLM. The results of this study contribute to the advancement of current predictive models used in transportation safety and provide insights for safety analysts and researchers when these models should be used.

• Record URL:
  • https://doi.org/10.1080/23249935.2022.2062480
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23249935
• Supplemental Notes:
  • © 2022 Hong Kong Society for Transportation Studies Limited. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Khodadadi, Ali
  • Shirazi, Mohammadali
  • Geedipally, Srinivas
  • Lord, Dominique
• Publication Date: 2023-5

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 2062480
• Serial:
  • Transportmetrica A: Transport Science
  • Volume: 19
  • Issue Number: 3
  • Publisher: Taylor & Francis
  • ISSN: 2324-9935
  • EISSN: 2324-9943
  • Serial URL: http://www.tandfonline.com/loi/ttra21

Subject/Index Terms

• TRT Terms: Binomial distributions; Crash data; Crash risk forecasting; Highway safety; Predictive models
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01897198
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 23 2023 4:52PM