Highlights We documented the derivation of Finite Mixture Negative Binomial Lindley (FMNB-L) model. We demonstrated the application of the model using a simulation study. We used the FMNB-L model to estimate statistical models for Texas four-lane freeways. We used multiple goodness-of-fit metrics to compare the FMNB-L with other models. The results show a significantly better fit by the FMNB-L compared to other analyzed models.

    Abstract Crash data are often highly dispersed; it may also include a large amount of zero observations or have a long tail. The traditional Negative Binomial (NB) model cannot model these data properly. To overcome this issue, the Negative Binomial-Lindley (NB-L) model has been proposed as an alternative to the NB to analyze data with these characteristics. Research studies have shown that the NB-L model provides a superior performance compared to the NB when data include numerous zero observations or have a long tail. In addition, crash data are often collected from sites with different spatial or temporal characteristics. Therefore, it is not unusual to assume that crash data are drawn from multiple subpopulations. Finite mixture models are powerful tools that can be used to account for underlying subpopulations and capture the population heterogeneity. This research documents the derivations and characteristics of the Finite mixture NB-L model (FMNB-L) to analyze data generated from heterogeneous subpopulations with many zero observations and a long tail. We demonstrated the application of the model to identify subpopulations with a simulation study. We then used the FMNB-L model to estimate statistical models for Texas four-lane freeway crashes. These data have unique characteristics; it is highly dispersed, have many locations with very large number of crashes, as well as significant number of locations with zero crash. We used multiple goodness-of-fit metrics to compare the FMNB-L model with the NB, NB-L, and the finite mixture NB models. The FMNB-L identified two subpopulations in datasets. The results show a significantly better fit by the FMNB-L compared to other analyzed models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Finite mixture Negative Binomial-Lindley for modeling heterogeneous crash data with many zero observations




    Publication date :

    2022-06-25




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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

    Khodadadi, Ali / Shirazi, Mohammadali / Geedipally, Srinivas et al. | Taylor & Francis Verlag | 2023


    The negative binomial-Lindley generalized linear model: Characteristics and application using crash data

    Geedipally, Srinivas Reddy / Lord, Dominique / Dhavala, Soma Sekhar | Elsevier | 2011