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.
