Citation

Keymanesh MR, Zabihian R. J. Transp. Res. (Tehran) 2023; 20(3): 171-190.

Copyright

(Copyright © 2023, Iran University of Science and technology, Transportation Research Institute)

DOI

10.22034/tri.2022.286782.2913

PMID

unavailable

Abstract

Increasing the level of safety in the road transport sector has always been one of the most important issues in the transportation system. In the national transport industry, the outsourcing industry always has the highest casualties. By identifying the safety indicators and the points of the extra-urban accident and increasing their level of safety, the percentage of losses and losses can be greatly reduced. Research for modeling human performance has led to the creation of two new research fields, neural networks and fuzzy systems. Neural networks are dynamic systems that, by simulating the functioning of the nervous system and the human brain by processing on experimental data, transfer knowledge or law beyond the data to the network structure, and relying on the ability to learn and ability parallel processing is capable of solving complex problems. Fuzzy systems, based on the approximate human decision-making process, are quantitatively and intuitively modeled quantitative, and thus they are trying to deal with uncertainties. The simplicity and comprehensiveness of this method are its advantages. Neuro-fuzzy systems combine two methods of parallel learning and processing of neural networks and approximate fuzzy inference. In this research, a neuro-fuzzy recurrence network is designed to predict the number of accidents. Then, the genetic algorithm is proposed for a new method in the training of this network and compared to them.

Language: en