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Developing prediction models for slope variance from the international roughness index

Sbaih Razan, Imam Rana, Alhiary Mohammad, Al-Mistarehi Bara

Transport Problems

2022

T. 17, z. 2

93--106

Road roughness is considered a primary indicator of pavement condition and serviceability, and the performance of paved roads is linked to road roughness. The focu of this study is to develop a relationship between two important roughness indicators, namely the international roughness index (IRI) and slope variance (SV), based on actual road roughness data to achieve a suitable correlation between these two indices using artificial neural networks (ANNa) and gene expression programming (GEP) techniques. Different study areas were selected to develop the prediction model. The first study area is the Desert Highway in Jordan, while the three remaining study areas are located in the US. A total of 533 data sets were used in this study to develop a model to predict the IRI from the SV. The GeneXproTools 5 software package was used to build the GEP model, while MATLAB 2019 was employed to develop the ANN model. The results showed that the GEP and ANN models outperformed all other previous models. The GEP-Based model showed a better performance and more precise results than the ANN model according to the coefficient of determination (R2).

Evaluating the safety performance of roundabouts

Alshannaq Mo'ath, Imam Rana

Transport Problems

2020

T. 15, z. 1

141--152

The use of roundabouts is well recognized for sustaining an efficient and safe intersection. However, the safety results may vary based on the prevailing conditions. Therefore, this study assesses the safety performance of roundabouts in Jordan. This study developed a predictive model by collecting and analyzing all accident records of 12 major urban roundabouts in the country over 3 years. For developing the model, this study employed an accident frequency analysis. The model calculated the rate of accidents and incorporated the geometric and operational characteristics of roundabouts. This was followed by ranking the safety performance of the roundabouts. It was concluded that driver behavior of violating the traffic rules, lack of clear lane markings in the circulating area and inadequate signage at the roundabouts entries are the main causes of roundabout accidents. The research recommends including the developed predictive model in future traffic control and planning studies, for identifying hazardous locations, or for prioritizing roundabout improvements based on safety performance.