Self-adapting prediction model for traffic flow status

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

The purpose of the study was to develop a method for making a self-adapting short-term prediction model for the flow status. The model was based on field-measured travel time data and self-organising maps. The test site was Ring Road I in the Helsinki metropolitan area. The forecasts were based on the outcomes of previous moments when the traffic situation was similar to the present. The forecast was set to the most common outcome in the cluster of these similar samples. The model was allowed to work online and its performance was studied. The proportion of correct forecasts was 93.8-96.3% over the entire trial period and 80.9-82.3% in congested conditions for the model in normal weather and road conditions. The average daily change in the proportion of correct forecasts was positive over the whole trial period: +0.3-0.4%. Two naïve comparison models were made. Both comparison models performed considerably poorer than the self-adapting model.
Original languageEnglish
Title of host publicationProceedings of 12th WCTR
Subtitle of host publicationWorld Conference on Transportation Research
Place of PublicationLisbon
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
EventWorld Conference on Transportation Research, WCTR - Lissabon, Portugal
Duration: 11 Jul 201015 Jul 2010

Conference

ConferenceWorld Conference on Transportation Research, WCTR
CountryPortugal
CityLissabon
Period11/07/1015/07/10

Fingerprint

prediction
road
traffic
travel time
metropolitan area
weather
forecast
comparison
trial

Keywords

  • prediction
  • traffic flow status
  • self-organising map

Cite this

Innamaa, S. (2010). Self-adapting prediction model for traffic flow status. In Proceedings of 12th WCTR: World Conference on Transportation Research [15] Lisbon.
Innamaa, Satu. / Self-adapting prediction model for traffic flow status. Proceedings of 12th WCTR: World Conference on Transportation Research. Lisbon, 2010.
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Innamaa, S 2010, Self-adapting prediction model for traffic flow status. in Proceedings of 12th WCTR: World Conference on Transportation Research., 15, Lisbon, World Conference on Transportation Research, WCTR, Lissabon, Portugal, 11/07/10.

Self-adapting prediction model for traffic flow status. / Innamaa, Satu.

Proceedings of 12th WCTR: World Conference on Transportation Research. Lisbon, 2010. 15.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AB - The purpose of the study was to develop a method for making a self-adapting short-term prediction model for the flow status. The model was based on field-measured travel time data and self-organising maps. The test site was Ring Road I in the Helsinki metropolitan area. The forecasts were based on the outcomes of previous moments when the traffic situation was similar to the present. The forecast was set to the most common outcome in the cluster of these similar samples. The model was allowed to work online and its performance was studied. The proportion of correct forecasts was 93.8-96.3% over the entire trial period and 80.9-82.3% in congested conditions for the model in normal weather and road conditions. The average daily change in the proportion of correct forecasts was positive over the whole trial period: +0.3-0.4%. Two naïve comparison models were made. Both comparison models performed considerably poorer than the self-adapting model.

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Innamaa S. Self-adapting prediction model for traffic flow status. In Proceedings of 12th WCTR: World Conference on Transportation Research. Lisbon. 2010. 15