<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In India, urban vehicular traffic is highly heterogeneous; it appears to be extremely challenging to accurately anticipate roundabout capacity. The main objective of this paper is to develop two models which are predicting the capacity of roundabouts in the Indian scenario. One is Weighted Least Square Regression (WLSR), a linear regression is carried out using weights of each data variable while the other one is Self Adaptive Segregative Genetic Algorithm with Simulated Annealing (SASEGASA), which is a combination of Segregative Genetic Algorithm (SEGA) and self-adaptive selection pressure with the application of simulated annealing. The coefficient of determination (R<sup>2</sup>) and Nash-Sutcliffe model efficiency coefficient (E) for the models like SASEGASA and WLSR were obtained as (0.947, 0.949) and (0.906, 0.905) respectively. Ranking among the proposed models is carried out using a complex phenomenon known as Modified Ranking Index (MRI), where the highest rank generated from five estimates is chosen as the best model for this study. SASEGASA is ranked as the appropriate model to estimate the roundabout capacity as per MRI. As compared to previously used models, SASEGASA model is quite relevant for field application.</p>
</abstract>

In India, urban vehicular traffic is highly heterogeneous; it appears to be extremely challenging to accurately anticipate roundabout capacity. The main objective of this paper is to develop two models which are predicting the capacity of roundabouts in the Indian scenario. One is Weighted Least Square Regression (WLSR), a linear regression is carried out using weights of each data variable while the other one is Self Adaptive Segregative Genetic Algorithm with Simulated Annealing (SASEGASA), which is a combination of Segregative Genetic Algorithm (SEGA) and self-adaptive selection pressure with the application of simulated annealing. The coefficient of determination (R2) and Nash-Sutcliffe model efficiency coefficient (E) for the models like SASEGASA and WLSR were obtained as (0.947, 0.949) and (0.906, 0.905) respectively. Ranking among the proposed models is carried out using a complex phenomenon known as Modified Ranking Index (MRI), where the highest rank generated from five estimates is chosen as the best model for this study. SASEGASA is ranked as the appropriate model to estimate the roundabout capacity as per MRI. As compared to previously used models, SASEGASA model is quite relevant for field application.

Selecting a Suitable Model for Roundabout Entrance Capacity Estimation: A Case Study

Romanian Journal of Transport Infrastructure

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In India, urban vehicular traffic is highly heterogeneous; it appears to be extremely challenging to accurately anticipate roundabout capacity. The main...

Selecting a Suitable Model for Roundabout Entrance Capacity Estimation: A Case Study

Online veröffentlicht: 22. März 2024

Seitenbereich: 1 - 17

DOI: https://doi.org/10.2478/rjti-2023-0013

Schlüsselwörter
heterogeneous traffic, roundabout, capacity, artificial intelligence

© 2023 Aarohi Kumar Munshi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Selecting a Suitable Model for Roundabout Entrance Capacity Estimation: A Case Study

Online veröffentlicht: 22. März 2024

Seitenbereich: 1 - 17

DOI: https://doi.org/10.2478/rjti-2023-0013

Schlüsselwörterheterogeneous traffic, roundabout, capacity, artificial intelligence

© 2023 Aarohi Kumar Munshi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
heterogeneous traffic, roundabout, capacity, artificial intelligence