This study focuses on the development of entry capacity models for roundabouts by using Genetic Algorithm (GA), Multi-variate Adaptive Regression spline (MARS) and Random Forest Regression (RFR) technique under heterogeneous traffic conditions. Required data were collected from 27 selected roundabouts of India by using high-definition video (HD) camera. Influence area for gap acceptance (INAGA) method is employed to find out the critical gap and follow up time. It is found from sensitivity analysis that variable like Entry width (Ew) contributes the most while the follow-up time (Tf) variable has less contribution in the proposed model.
Comparison of Artificial Intelligence Based Roundabout Entry Capacity Models
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 18 , 2 ; 288-296
2020-05-01
9 pages
Article (Journal)
Electronic Resource
English
Genetic algorithm (GA) , Multi-variate adaptive regression spline (MARS) , Random Forest regression (RFR) , Influence area for gap acceptance (INAGA) Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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