# Analyzing Important Elements for Improving the Safety of Motorways
## 2 Literature Review  
### Traffic Accident Prediction Model Using Traditional Statistical Models  
Various studies have introduced different models to elucidate the relationship between traffic accidents and key factors. These models encompass the Poisson regression model, Poisson-lognormal model, negative binomial regression model, and generalized estimating equations [13]. The primary objective of these traffic accident prediction models is to pinpoint factors significantly impacting the frequency of traffic accidents. Among these, the Poisson regression model stands out as a frequently utilized tool for estimating traffic accident frequencies. It is adept at handling discrete data and avoids returning negative values, rendering it suitable for modeling traffic accident frequencies, which are inherently non-negative [14]. The Poisson regression model operates under the assumption of equal mean and variance. However, in the context of traffic accidents, the variance often surpasses the mean, a phenomenon known as overdispersion [13]. Consequently, an overdispersion test becomes necessary to ascertain the presence of overdispersion. Failure to meet the Poisson regression model's assumption in the presence of overdispersion can introduce model bias. In such scenarios, resorting to the negative binomial regression model is recommended. [PERSON] et al. (2015) developed a traffic accident prediction model using a random effect negative binomial regression model to accommodate heterogeneity and mitigate biased parameter estimation. Their dataset encompassed 48,154 traffic accidents spanning three years on highways in Washington State, USA. By crafting both a negative binomial model and a random effect negative binomial model, they determined that the latter exhibited lower standard errors and thus delivered superior performance. Significant factors influencing traffic accidents in their study included AADT, speed limits, road surface conditions, horizontal alignment type, and visibility. [PERSON] (2016) devised a traffic accident severity prediction model employing a negative binomial regression model. The study incorporated six collected factors: driver negligence, age, gender, environmental factors, time of accident, and data related to road or vehicle defects, alongside the frequency and severity of traffic accidents. Descriptive analysis was conducted to identify factors deeply associated with traffic accident severity, forming the basis for the traffic accident prediction model. The findings revealed a decrease in traffic accident severity with increasing age, independent of the driver's gender. Moreover, improved environmental conditions, weekends versus weekdays, and evenings versus mornings were correlated with reduced traffic accident severity. The negative binomial regression model's analysis results regarding factors influencing traffic accidents aligned with those from descriptive analysis. [PERSON] et al. (2017) developed a model to analyze the relationships between variables associated with the frequency of traffic accidents, utilizing multivariate traffic accident data obtained from police reports in Tennessee, USA. Given the infrequent occurrence of traffic accidents, a substantial portion of the data comprised zero accident counts. To effectively address this issue, the study employed a multivariate random-parameter zero-inflated negative binomial (MRZINB) regression model. This model effectively estimated relationships between the frequency of traffic accidents and factors such as road pavement conditions, traffic factors, and road geometric characteristics. The study concluded that the MRZINB model adequately addressed data heterogeneity and zero-inflation tendencies. [PERSON] (2018) conducted a comparative analysis of Poisson, zero-inflated Poisson, and negative binomial regression models to elucidate the relationship between heavy vehicle traffic accidents and road geometric conditions, using data from the Highway Safety Information System.