Modelling roundabout entry capacity for mixed traffic flow using ANN: a case study in India

• Autorzy: Munshi Aarohi Kumar , Patnaik Ashish Kumar

Identyfikatory

DOI

10.20858/sjsutst.2024.123.10

• Języki publikacji: EN

Abstrakty

EN

Roundabouts, as an unsignalized intersection, have an effective preventative measure designed to control straight-line crashes. Efficient traffic flow in cities depends upon appropriate capacity estimation of roundabouts. This study attempts to develop models for roundabout entry capacity by applying Artificial Neural Network (ANN) analysis for mixed traffic flow conditions. Data was gathered from 27 roundabouts spread across India. The influence area for gap acceptance (INAGA) concept was used as a graphical method to identify critical gap (Tc) of entry flow at roundabouts. This study indicated that the Bayesian Regularisation Neural Network (BRNN) based model has the best R2 and RMSE of 0.97 and 167.8. The connection weight approach and Garson algorithm evaluate the significance of each explanatory variable and identify follow-up time (Tf) as a critical parameter with values of 11.10 and 21.15%, respectively.

Słowa kluczowe

EN

INAGA; ANN; roundabouts entry capacity; Garson algorithm

PL

INAGA; przepustowość wlotów rond; ANN; algorytm Garsona

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2024
• Tom: z. 123
• Strony: 209--226
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Munshi Aarohi Kumar

• Department of Civil and Environmental Engineering, Research Scholar, Birla Institute of Technology Mesra, Ranchi 835215, Jharkhand, India

• https://orcid.org/0009-0003-5808-8287

autor

Patnaik Ashish Kumar

• Department of Civil and Environmental Engineering, Research Scholar, Birla Institute of Technology Mesra, Ranchi 835215, Jharkhand, India

• https://orcid.org/0000-0002-8674-9148

Bibliografia

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• 3. Ahmad Abdullah, Rajat Rastogi. 2016. “Regression model for entry capacity of a roundabout under mixed traffic condition – an Indian case study”. Transportation Letters 9(5): 243-257. DOI: https://doi.org/10.1080/19427867.2016.1203603.
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• 10. Patnaik Ashish Kumar, Yadu Krishna, Shweta Rao, Prashant Kumar Bhuyan. 2017. “Development of Roundabout Entry Capacity Model Using INAGA Method for Heterogeneous Traffic Flow Conditions”. Arab J Sci Eng 42(9): 4181-4199. DOI: https://doi.org/10.1007/s13369-017-2677-x.
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• 12. Ghanizadeh Ali Reza, Nasrin Heidarabadizadeh, Farhang Jalali. 2020. “Artificial neural network back-calculation of flexible pavements with sensitivity analysis using Garson’s and connection weights algorithms”. Innov. Infrastruct. Solut. 5(2): 63. DOI: https://doi.org/10.1007/s41062-020-00312-z.