Prediction of Average Annual Daily Traffic Using Machine Learning Methods
|International Journal of Computer Science and Engineering|
|© 2019 by SSRG - IJCSE Journal|
|Volume 6 Issue 11|
|Year of Publication : 2019|
|Authors : Srinivasan Suresh|
How to Cite?
Srinivasan Suresh, "Prediction of Average Annual Daily Traffic Using Machine Learning Methods," SSRG International Journal of Computer Science and Engineering , vol. 6, no. 11, pp. 51-54, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I11P111
The field of machine learning is growing enormously and been widely used. There are many techniques been developed in the machine learning arena. Using these techniques to predict the Average Annual Daily Traffic (AADT) would help to improve the accuracy in prediction and to plan routes in a better manner. The Linear regression, Ridge regression and Lasso regression methods are used in this analysis. The data is obtained from the Highway Performance Management System (HPMS) and from Virginia Roads, the official data provider for the State of Virginia. The raw data is cleansed, regrouped and prepared for the regression analysis. Route category, Through lanes, Facility type and Functional class are the key variables used in the analysis. The machine learning models built through these methods have almost 77% accuracy and these models can be reused to predict the AADT values for new routes or extension of routes.
Annual Average Daily Traffic (AADT), Machine Learning, Through Lanes, Functional System
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