Skip to main content

The Adaptive Lasso Procedure for Building a Traffic Forecasting Model


Abstract This paper will begin by initially discussing the potential uses and challenges of efficient and accurate traffic forecasting. The data we used includes traffic volume from seven locations on a busy Athens street in April and May of 2000. This data was used as part of a traffic forecasting competition. Our initial observation, was that due to the volatility and oscillating nature of daily traffic volume, simple linear regression models will not perform well in predicting the time-series data. For this we present the Harmonic Time Series model. Such model (assuming all predictors are significant) will include a sinusoidal term for each time index within a period of data. Our assumption is that traffic volumes have a period of one week (which... (more)
Created Date 2017-05
Contributor Mora, Juan (Author) / Kamarianakis, Ioannis (Thesis Director) / Yu, Wanchunzi (Committee Member) / W. P. Carey School of Business / School of Mathematical and Statistical Sciences / Barrett, The Honors College
Subject Regression / Time-series Models / Traffic Forecasting
Series Academic Year 2016-2017
Type Text
Extent 29 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


  Mora_J_Spring_2017.pdf
1.9 MB application/pdf
  • Download restricted to ASU - Sign In
Download Count: 2