Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data

Shaon, Mohammad Razaur Rahman; Qin, Xiao; Shirazi, Mohammadali; Lord, Dominique; Geedipally, Srinivas Reddy

doi:10.1016/j.amar.2018.04.002

Cited by 45 publications

(12 citation statements)

References 40 publications

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“…However, the Poisson model cannot handle overdispersed or underdispersed data and may result in biased estimation. In order to analyze the overdispersed data, many studies proposed different mixed Poisson models, such as the Poisson-gamma model (the negative binomial (NB) model) [7][8][9][10][11][12][13], Poisson-lognormal model [14][15][16], and Poissoninverse gamma model [17]. For the data with many zeros (i.e., excess zero-count data), the zero-inflated models were applied, including the zero-inflated Poisson model [18,19], zero-inflated negative binomial model [20][21][22], and their extension models (i.e., multiple random parameter zeroinflated negative binomial regression model [20] and zero expansion Poisson regression model with random parameter effect [23]).…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zou

et al. 2020

Journal of Advanced Transportation

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Zou

et al. 2020

Journal of Advanced Transportation

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“…In relation to the issue of excess zeros, it is worth mentioning that apart from the zero-altered approach scrutinized in this study, innovative models have been developed and applied to analyze the sort of count data in which the negative binomial-Lindley ( 67 – 69 ) has demonstrated potential and can be a direction for future research.…”

Section: Resultsmentioning

confidence: 99%

Application of a Hurdle Model with Random Effects to Explore the Relationship between Operational Characteristics and Safety Performance

Qiu

Logan

Oxley

et al. 2020

Transportation Research Record: Journal of the Transportation R

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This study used the bus incident data in Victoria, Australia to establish the relationship between operational characteristics and the safety performance of bus operators. A series of count models were investigated to account for methodological challenges, including excess zeros and panel data structure. The empirical results highlighted the different effects operational characteristics had on the risk and prevalence of bus incidents. Operators of smaller size, providing non-route services and operating in regional areas had a lower risk of having any reported incidents compared with larger route operators and operators in areas of higher accessibility. In cases where at least one incident had been reported, incident frequency was higher for operators with higher fleet total travel distance, older fleets and better roadworthy performance (this factor being counterintuitive). Findings from this study provide safety regulators with evidence-driven opportunities to enhance bus safety, including improving incident reporting practices, the establishment of a comprehensive database for heavy vehicle operators, and specific efforts targeted at older fleets.

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“…The RPNB model, which also addresses unobserved heterogeneity in regional safety over-dispersed data modeling, assumes that the parameters draw from some random distributions and vary randomly from case to case. To investigate unobserved heterogeneity across highway segments for crash count prediction, Shaon et al [38,39] and Chen et al [40] applied random parameters Poisson-based models in their safety studies. As a branch of random parameter Poisson-based modeling, RPNB has been widely used to account for data overdispersion and has been widely conducted by Venkataraman et al [6,41,42], Chen and Tarko [43], and Saeed et al [44].…”

Section: Related Workmentioning

confidence: 99%

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Wang

Shan

2021

IJGI

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Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis.

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Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data

Cited by 45 publications

References 40 publications

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

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

Application of a Hurdle Model with Random Effects to Explore the Relationship between Operational Characteristics and Safety Performance

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

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