01663912

Component

https://doi.org/10.1177/0361198118777603

https://doi.org/10.1177/0361198118777603

The primary objectives of this study are to develop two roundabout entry capacity models using a regression-based multiple non-linear regression model (MNLR) and artificial intelligence (AI)-based ANFIS (adaptive neuro-fuzzy inference system) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of AI that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts in eight states of India. To assess the significance of these models and select the best model among them, modified rank index 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 and 0.98, 0.98 for the MNLR and ANFIS model, respectively. The ANFIS model is found to be the best model in this study. However, from a practical point of view, the MNLR model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that critical gap is the prime variable and shares 18.43% for the development of roundabout entry capacity. As compared with the Girabase formula (France), Brilon wu formula (Germany), and HCM 2010 models, the proposed MNLR model is quite reliable under low to medium ranges of traffic volumes.

18-04016

English

pp 35-43

2018

Transportation Research Record: Journal of the Transportation Research Board

Print

Figures (3) ; Maps; References (16) ; Tables (5)

Artificial intelligence; Fuzzy logic; Heterogeneity; Highway capacity; Multiple regression analysis; Neural networks; Roundabouts; Sensitivity analysis; Traffic models; Traffic volume; Vehicle mix

Heterogeneous traffic

India

Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Jan 8 2018 11:00AM

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