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Title: Investigating Entry Capacity Models of Roundabouts under Heterogeneous Traffic Conditions
Authors: Patnaik, Ashish Kumar
Ranjan, Ankit Raj
Bhuyan, Prasanta Kumar
Keywords: Roundabout
Artificial intelligence
Critical gap
Sensitivity analysis
Issue Date: Jan-2018
Citation: Transportation Research Board (TRB) 97th Annual Meeting, Washington, D.C., USA, 7 – 11 January, 2018
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.
Description: Copyright of this document belongs to proceedings publisher.
Appears in Collections:Conference Papers

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