Influence of road safety policies on the long-term trends in fatal Crashes: A Gaussian Copula-based time series count model with an autoregressive moving average process

Time series analysis plays a vital role in modeling historical crash trends and predicting the possible changes in future crash trends. In existing safety literature, earlier studies employed multiple approaches to model long-term crash risk profiles, such as integer-valued autoregressive Poisson regression model, integer-valued generalized autoregressive conditional heteroscedastic model, and generalized linear autoregressive and moving average models. However, these modeling frameworks often fail to fully capture several key properties of crash count data, especially negative serial correlation, and nonlinear dependence structures across temporal crash counts. To address these methodological gaps in existing safety literature, this study proposes to use a Gaussian Copula-based model for the long-term crash trend analysis. Specifically, this study proposes to use a Gaussian Copula-based Time Series Count Model with an Autoregressive Moving Average Process for the analysis of long-term trends in fatal crashes. The proposed approach can accommodate several data properties, which include (1) non-negative discrete property of count data, (2) positive and negative serial correlations among time series data, and (3) nonlinear dependence among time-series observations. The performance of the Gaussian Copula-based time series count model is compared with the generalized linear autoregressive and moving average model. The proposed modeling approaches are demonstrated by using yearly fatal crash count data for the years 1986 through 2022 from Queensland, Australia. The major safety interventions implemented in Queensland over those years are also highlighted to assess the possible and plausible impacts of these safety interventions in reducing fatal crash risks. Further, elasticity effects and overall percentage changes in fatal crashes across different time points are computed to demonstrate the implications of the proposed model. The policy analysis exercise shows that the implemented road safety interventions are likely to have diminishing marginal returns, underscoring the need for new and effective road safety policies to achieve the goal of zero fatalities within the set timeframe.