A Zero-Inflated Negative Binomial Crash Prediction Model for Freeway Bridge Sections

2021 ◽  
Author(s):  
Jianjun Song ◽  
Bingshi Huang ◽  
Yong Wang ◽  
Chao Wu ◽  
Xiaofang Zou ◽  
...  
Keyword(s):  
Prediction Model ◽  
Negative Binomial ◽  
Crash Prediction ◽  
Crash Prediction Model

scholarly journals Development of district-wise crash prediction model in Bangladesh

2020 ◽  
Vol 7 (1) ◽  
pp. 1762525 ◽  
Author(s):  
Soumik Nafis Sadeek ◽  
Shakil Mohammad Rifaat ◽  
Marinella Giunta
Keyword(s):  
Prediction Model ◽  
Crash Prediction ◽  
Crash Prediction Model

scholarly journals Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables

2016 ◽  
Author(s):  
Yanjie Zeng ◽  
Xiaofei Wang ◽  
Lineng Liu ◽  
Xinwei Li ◽  
Caifeng Jiang
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Model Parameters ◽  
Horizontal Curve ◽  
Frequency Model ◽  
Crash Prediction ◽  
Explanatory Variables ◽  
Crash Prediction Model ◽  
Report Data ◽  
Effective Use

Crash prediction of the sharp horizontal curve segment (SHCS) of a freeway is an important tool in analyzing safety of SHCSs and in building a crash prediction model (CPM). The design and crash report data of 88 SHCSs from different institutions were surveyed and three negative binomial (NB) regression models and three generalized negative binomial (GNB) regression models were built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The study demonstrates the effective use of the GNB model in analyzing the interactive influence of explanatory variables and in predicting freeway basic segments. Traffic volume, highway horizontal radius, and curve length have been formulated as explanatory variables. Subsequently, we performed statistical analysis to determine the model parameters and conducted sensitivity analysis. Among the six models, the result of model 6, which considered interactive influence, is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 SHCSs with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than models 1 and 3.

A crash-prediction model for multilane roads

2007 ◽  
Vol 39 (4) ◽  
pp. 657-670 ◽  
Author(s):  
Ciro Caliendo ◽  
Maurizio Guida ◽  
Alessandra Parisi
Keyword(s):  
Prediction Model ◽  
Crash Prediction ◽  
Crash Prediction Model

A GIS Based Crash Assignment Model for Signalized T Intersections

2014 ◽  
Vol 543-547 ◽  
pp. 4472-4475
Author(s):  
Bipin Karki ◽  
Xiao Bo Qu ◽  
Kriengsak Panuwatwanich ◽  
Sherif Mohamed ◽  
Partha Parajuli
Keyword(s):  
Information System ◽  
Prediction Model ◽  
Geographical Information System ◽  
Assignment Problem ◽  
Geographical Information ◽  
Crash Prediction ◽  
Assignment Model ◽  
Gis Database ◽  
Crash Prediction Model

The crash assignment problem has long been considered as one of the most important components in an approach-level crash prediction model for intersections. A few pioneering studies have been carried out to properly assign the crashes in or nearby intersections to various approaches. However, the implementation of these models is very time consuming as it can only be done one by one manually. In this paper, a geographical information system (GIS) database is developed to complete the crash assignment. This tool has been applied in Queensland, Australia in the development of crash prediction model for signalized T-intersections.

scholarly journals Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic

2003 ◽  
Vol 1840 (1) ◽  
pp. 67-77 ◽  
Author(s):  
Chris Lee ◽  
Bruce Hellinga ◽  
Frank Saccomanno
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Previous Model ◽  
Traffic Condition ◽  
Freeway Traffic ◽  
Crash Prediction ◽  
Crash Prediction Model ◽  
Time Basis

The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives. Therefore, the objectives of the present study were to ( a) suggest the rational methods by which the crash precursors included in the model can be determined on the basis of experimental results and ( b) test the performance of the modified crash prediction model. The study found that crash precursors can be determined in an objective manner, eliminating a characteristic of the previous model, in which the model results were dependent on analysts’ subjective categorization of crash precursors.

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