Short Term Prediction of Traffic Parameters--Performance Comparison of Data Driven and Less Data Required Approaches

The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Therefore accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is an essential component of many traveler information systems. Different methods used for prediction of traffic parameters include historic averaging, regression analysis, Kalman filtering technique, time series analysis, machine learning techniques etc. However, in countries like India, where automated sensors are slowly getting implemented, development of such models are in a nascent stage. One of the related questions is the selection of suitable model to be used for the prediction problem – especially choice between data driven and less data demanding approaches. Since the automated data collection is in the beginning stage, many of the cities are struggling with database generation and processing and hence, a less data demanding approach will be attractive, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data driven technique namely Artificial Neural Networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data driven approach. Grey model, which is also reported as a good prediction tool, is selected as the less data demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction study. As an ATIS implementation for informing the road users about the future traffic conditions, short term prediction of parameters using ANN and Grey theory is demonstrated and a comparison of these techniques as a prediction tool is carried out in this paper.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AHB15 Intelligent Transportation Systems.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Badhrudeen, Mohamed
  • Raj, Jithin
  • Vanajakshi, Lelitha Devi
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 14p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Advanced traveler information systems; Automatic data collection systems; Neural networks; Traffic data; Traffic forecasting
• Uncontrolled Terms: Grey models
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01517576
• Record Type: Publication
• Report/Paper Numbers: 14-2864
• Files: TRIS, TRB, ATRI
• Created Date: Mar 10 2014 9:24AM