Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques

Hamid Behbahani, Amir Mohamadian Amiri, Reza Imaninasab and Meysam Alizamir

Journal of Forecasting, 2018, vol. 37, issue 7, 767-780

Abstract: Considerable effort has been made to determine which of the most common prediction modeling techniques performs best, based on crash‐related data. Accordingly, the present study aims to evaluate how crashes in the urban road network are affected by contributing factors. Therefore, in the present paper, a comparison has been done among four artificial neural network (ANN) techniques: extreme learning machine (ELM), probabilistic neural network (PNN), radial basis function (RBF), and multilayer perceptron (MLP). According to the measures used, including Nash–Sutcliffe (NS), mean absolute error (MAE), and root mean square error (RMSE), ELM was found to be the most successful approach in addressing the objectives defined in the present study. Moreover, not only is ELM the fastest algorithm due to its different structure, but it has also led to the most accurate prediction. In the end, the RReliefF algorithm was utilized to find the importance of variables used, including V/C, speed, vehicle kilometer traveled (VKT), roadway width, existence of median, and allowable/not‐allowable parking. It was proved that VKT is the most influential variable in accident occurrence, followed by two traffic flow characteristics: V/C and speed.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/for.2542

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:37:y:2018:i:7:p:767-780

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().