Development of crash frequency models for safety promotion of urban collector streets

ABSTRACT

The merits for development and application of crash frequency prediction models for safety promotion on any road type, with a focus on urban collector streets, are presented in this article. The city of Yazd, a medium-sized city in the middle of Iran, was selected as a case study and the data required for modelling crash frequencies along five collector streets comprising 31 street sections were collected. Six models including Poisson and negative binomial models and their deviations along with a hybrid artificial neural networks (ANN) model were developed to predict crash frequency along each street section. The overfitting problem was addressed using appropriate sensitivity analysis methods which were also used to identify the input variables with significant impact on the model performance. The results indicated that the developed hybrid ANN model provided the best performance in terms of accuracy and the number of input variables. The application of hybrid ANN model to evaluate the safety impacts of four different strategies, each resembled by one of the input variables of this model, indicated that these models can successfully be used for this purpose.

KEYWORDS:

• Urban collector streets
• urban accidents
• Artificial Neural Networks
• crash prediction models

Acknowledgment

The authors would like to express their appreciation to the staff and the head of Yazd Police Research Center for their help in providing accident statistics.

Disclosure statement

No potential conflict of interest was reported by the authors.