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Statistics report
Dec. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 116
Paper Published : 1401
No. of Authors : 3560
  Journal Paper


Paper Title :
Gap Acceptance Analysis at an Unsignalised Intersection using Machine Learning Techniques

Author :Bhargavi Mukku, C.Naveen Kumar

Article Citation :Bhargavi Mukku ,C.Naveen Kumar , (2021 ) " Gap Acceptance Analysis at an Unsignalised Intersection using Machine Learning Techniques " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 11-16, Volume-9,Issue-12

Abstract : As the vehicular population of Hyderabad is increasing rapidly with compound annual growth rate (CAGR) of 13.84%. Deficiency in infrastructure to accommodate the present traffic is causing an increase in travel time. This is exaggerated by much delays especially at Unsignalised intersections. In this study, analysis of gap acceptance at an unsignalised intersection is evaluated and parameters like Approach speed, type of vehicle, Waiting time, Number of vehicles objecting subject vehicle were considered. To analyze the gap acceptance at an unsignalised intersection, Machine Learning Techniques like Artificial neural network (ANN) and Support vector regression (SVR) models were developed. ANN model is found to be the best fit model than SVR. Critical gap during peak hour using Harder’s model and Raff’s model were also calculated. To evaluate the performance of the calibrated models, measures like Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), Mean Squared Prediction Error (MSPE), Mean Absolute Percentage Error (MAPE), Schawarz’s Bayesian Criterior (SBC) were used. Keywords - Gap appectance, Unsignalised intersection, ANN, SVR, Critical gap

Type : Research paper

Published : Volume-9,Issue-12


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-18289   View Here

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