Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections

2003 ◽  
Vol 35 (2) ◽  
pp. 253-259 ◽  
Author(s):  
Hoong Chor Chin ◽  
Mohammed Abdul Quddus
Keyword(s):  
Traffic Accident ◽  
Negative Binomial ◽  
Random Effect ◽  
Signalized Intersections ◽  
Negative Binomial Model ◽  
Binomial Model

scholarly journals Random Parameter Negative Binomial Model of Signalized Intersections

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee ◽  
Jinwoo Jeon
Keyword(s):  
Negative Binomial ◽  
Random Parameter ◽  
Signalized Intersections ◽  
Negative Binomial Model ◽  
Marginal Effect ◽  
Binomial Model ◽  
Random Parameters ◽  
Factors Affecting ◽  
Independent Variables ◽  
Insight Into

Factors affecting accident frequencies at 72 signalized intersections in the Gyeonggi-Do (province) over a four-year period (2007~2010) were explored using the random parameters negative binomial model. The empirical results from the comparison with fixed parameters binomial model show that the random parameters model outperforms its fixed parameters counterpart and provides a fuller understanding of the factors which determine accident frequencies at signalized intersections. In addition, elasticity and marginal effect were estimated to gain more insight into the effects of one-percent and one-unit changes in the dependent variable from changes in the independent variables.

scholarly journals Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia

2018 ◽  
Author(s):  
Fedy Ouni ◽  
Mounir Belloumi
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Surface Condition ◽  
Random Effect ◽  
Negative Binomial Model ◽  
Empirical Modeling ◽  
Binomial Model ◽  
Model Average

The purpose of the present study is to explore the linkage between Hazardous Road Locations-based crash counts and a variety of geometric characteristics, roadway characteristics, traffic flow characteristics and spatial features in the region of Sousse, Tunisia. For this purpose, collision data was collected from at 52 hazardous road sections including 1397 crash records for a 11-year monitoring period from January 1, 2004 to December 31, 2014 obtained from National Observatory for Information, Training, Documentation and Studies on Road Safety in Tunisia (NOITDRS). The matrix of Pearson correlation was used in order to avoid inclusion of both variables, which were highly correlated. Both the Random Effects Negative Binomial model and the Negative Binomial model were estimated. To evaluate the models, the Random Effect Negative Binomial model improves the goodness-of-fit compared to the Negative Binomial model. Average Daily Traffic volume, Curved alignment, Presence of public lighting, Visibility, Number of lane, Presence of vertical/horizontal sign, Presence of rural segment, Presence of drainage system, Roadway surface condition, Presence of paved shoulder and presence of major road were found as significant variables influencing accident occurrences. Overall, the current research contributes to the literature from empirical, modeling methodological standpoints since it was the first study conducted in Tunisia to use crash prediction models for hazardous road locations, and that portrays Tunisian reality. The research findings present advantageous insights on hazardous road locations in the region of Sousse, Tunisia and present useful planning tools for public authorities in Tunisia.

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