Short-Term Prediction of Travel Time Using Neural Networks on an Interurban Highway

This study uses a model based on travel time data measured in the field to investigate the predictability of travel time on an interurban highway. The study also seeks to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-km-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The results showed that the majority of the forecasts were close to the actual measured values, suggesting that the use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times. Directions for future research are discussed.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00494488
• Authors:
  • Innamaa, Satu
• Publication Date: 2005-11

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 649-669
• Serial:
  • Transportation
  • Volume: 32
  • Issue Number: 6
  • Publisher: Springer
  • ISSN: 0049-4488
  • EISSN: 1572-9435
  • Serial URL: http://link.springer.com/journal/11116

Subject/Index Terms

• TRT Terms: Advanced traveler information systems; Forecasting; Highways; Intercity travel; Mathematical prediction; Neural networks; Rural highways; Travel time
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01006893
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Nov 3 2005 8:14AM