Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques
| Autor: | Hamid Behbahani, Reza Imaninasab, Amir Mohamadian Amiri, Meysam Alizamir |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Mean squared error Artificial neural network Computer science Strategy and Management 05 social sciences 02 engineering and technology Management Science and Operations Research Traffic flow Computer Science Applications Probabilistic neural network Variable (computer science) Modeling and Simulation Multilayer perceptron 0502 economics and business Statistics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Radial basis function Statistics Probability and Uncertainty Extreme learning machine |
| Zdroj: | Journal of Forecasting. 37:767-780 |
| ISSN: | 0277-6693 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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