Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Abstract

Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

Designing a Model of the Early Warning System on the Road Curvature to Prevent the Traffic Accidents

“…Assessment of travel speed shows that vehicle transverse stability which includes the slippage and overturns determines the curve radius value to be selected when designing road horizontal curves [31]. Zhang computed the radius mean and curvature degree for all categories used in his research and then he regressed them against the mean of accident rates in every category, his findings supported some previous studies which found and stated that the curve sharpness has a significant impact on the accident rates [30].…”

“…Assessment using linear regression models showed unsatisfactory results as the properties of linear regression are normally distributed [27,28], which can generate negative or non-discrete values of accident rates [21]. Another model, Poisson models were suggested [29] and used by Zhang et al [30], for their consideration of random and sporadic data, in addition to easy estimation of variables relationships, however results can be overestimated or underestimated.…”

Estimating the Volatility of Flights and Risk of Saturation of Airspaces in the European Core Area: A Methodological Proposal

“…In this case, volatility is caused by the uncertainty in the number of flights in each hour. For instance, some authors [42] have used the lineal Poison autoregressive (PAR) model as an alternative to the Poisson model to analyse the impacts of laws and climate on annual road traffic accidents. This alternative method may be applied in future work for comparative purposes.…”

An ensemble learning method for low visibility prediction on freeway using meteorological data

“…A representative solution is to assess safety risks and formulate corresponding control strategies. On one hand, some researchers [9][10][11][12][13] investigated the effects of low visibility together with traffic parameters on the collision risk and developed real-time crash risk prediction models under fog conditions based on the historical data. Visibility is either a model input variable or a constraint in these models.…”