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Traffic speed limit modeling using support vector regression and firefly algorithm

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dc.contributor.author Maheshwari, S
dc.contributor.author Singh, D
dc.contributor.author Zaman, M
dc.contributor.author White, L
dc.contributor.editor Pasindu, HR
dc.date.accessioned 2022-09-13T06:25:18Z
dc.date.available 2022-09-13T06:25:18Z
dc.date.issued 2017-07
dc.identifier.citation ....********....... en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19059
dc.description.abstract Setting traffic speed limits using engineering approaches is highly desirable, however, spot studies required for such approaches are tedious, subjective and time consuming, in the present study, 85 percentile speeds were modeled using tw'o machine learning approaches a) Support Vector Regression, and b) Support Vector Regression (SVR) coupled with the Firefly Algorithm (FA). The objective of the study is to model traffic speed limits using artificial intelligence tools and quantify the efficiency of metaheuristic evolutionary algorithms for optimization. Input parameters, namely, physical characteristics of road, traffic and pavement condition were used for modeling. Physical characteristics of road included shoulder width, shoulder type and surface width. The traffic parameters consisted of average daily traffic and posted speed. Skid number and international roughness index were covered in pavement condition parameters. Two statistical models (Model 1 and Model 2) were developed for the prediction of 85th percentile speed. Model 1 consisted of physical characteristics of road, pavement condition parameters and traffic parameters including posted speed. Model 2 consisted of all the parameters of Model 1 except posted speed. Statistical performance evaluators like mean absolute relative error, mean square error, coefficient of determination and over-fitting ratio were used to compare the models. It was observed that the Model 1 outperformed Model 2, conveying the importance of posted speed for accurate prediction of operating speed. Application of firefly algorithm resulted in improved prediction accuracy with reduced computational time and manual work, highlighting the need to explore its application for civil engineering problems. en_US
dc.language.iso en en_US
dc.publisher Department of Civil Engineering en_US
dc.subject 85th percentile speed en_US
dc.subject Support Vector Regression en_US
dc.subject Firefly Algorithm en_US
dc.title Traffic speed limit modeling using support vector regression and firefly algorithm en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Civil Engineering en_US
dc.identifier.year 2017 en_US
dc.identifier.conference International conference on Advances in Highway Engineering & Transportation Systems en_US
dc.identifier.place Negombo en_US
dc.identifier.pgnos p. 69 en_US
dc.identifier.proceeding Proceedings of Advances in Highway Engineering & Transportation Systems 2017 en_US


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