Investigating Entry Capacity Models of Roundabouts under Heterogeneous Traffic Conditions

Abstract

The primary objectives of this study are to develop the two roundabout entry capacity model by utilizing regression based Multiple Non-linear Regression model (MNLR) and artificial intelligence based ANFIS (Adaptive Neuro-fuzzy Inference System) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of Artificial intelligence that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts spanning across 8 states of India. To assess the significance of these models and select the best model among them modified rank index (MRI) is applied in this study. The coefficient of determination (R2) and Nash–Sutcliffe model efficiency coefficient ‘E’ values are found to be (0.92, 0.91) & (0.98, 0.98) of MNLR & ANFIS model respectively. ANFIS model is found to be the best model in this study. But in a practical point of view, MNLR model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that critical gap is the prime variable and sharing 18.43 % for the development of roundabout entry capacity. As compared to Girabase formula (France), Brilon wu formula (Germany) & HCM 2010 models, the proposed MNLR model is quite reliable under low to medium range of traffic volumes.