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The negative Binomial-Lindley model with Time-Dependent Parameters: Accounting for temporal variations and excess
Richard Dzinyela1, Mohammadali Shirazi2, Subasish Das3
1Zachary Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 3136 TAMU, College Station, TX 77843-3136, United States.
Accident; Analysis and Prevention
|July 31, 2024
Summary
This study introduces a new time-dependent negative binomial-Lindley model to improve crash frequency analysis. The enhanced model better handles excess zero observations and temporal variations in crash data, leading to more accurate predictions.
Area of Science:
- Transportation Engineering
- Statistical Modeling
- Traffic Safety
Background:
- Crash data often exhibit excess zero observations, challenging traditional negative binomial (NB) models.
- Existing negative binomial-Lindley (NBL) and random parameters NBL models address excess zeros but may not fully capture temporal variations.
- Time-varying factors like traffic volume and weather necessitate models that account for temporal disaggregation and heterogeneity.
Purpose of the Study:
- To introduce a novel variant of the negative binomial-Lindley (NBL) model with time-dependent parameters.
- To address limitations in existing crash frequency models regarding excess zero observations and temporal variations.
- To enhance the accuracy of crash frequency analysis by incorporating time-dependent coefficients and Lindley parameters.
Main Methods:
- Developed a new NBL model with coefficients and Lindley parameters that vary over time.
- Conducted a simulation study to illustrate the model's derivations and characteristics.
- Applied the proposed model to empirical crash datasets from rural arterial roads in Texas, including time-dependent variables.
Main Results:
- The time-dependent NBL model demonstrated superior goodness-of-fit compared to NB, NBL, and time-dependent NB models.
- Wider shoulders and median presence were associated with decreased crash occurrences.
- Increased speed variation, wider road surfaces, and higher monthly average daily traffic (Monthly ADT) correlated with increased crash frequency.
Conclusions:
- The proposed NBL model with time-dependent parameters significantly improves crash frequency modeling by accounting for temporal variations and excess zeros.
- Understanding the impact of time-varying factors and road characteristics is crucial for effective traffic safety strategies.
- The study highlights the importance of disaggregated data and advanced statistical methods for accurate crash analysis.
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