Effect of monitoring system structure on short-term prediction of highway travel time

Satu Innamaa (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

This article discusses how the structure of the measurement system affects the short-term forecasts of travel time based on it. The effects of section length and location of different measurement stations were investigated. The study used empirical data. The research was carried out on a 28-km long interurban two-lane highway section. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the division of long road sections into shorter sub-links in the travel time measurement system was important. Furthermore, it would be crucial to obtain information about traffic flow rates entering the section in order to time the start of congestion correctly. In conclusion, the structure of the monitoring system should be based on the analysis of a typical location and the development of congestion along the section.
Original languageEnglish
Pages (from-to)125-140
Number of pages16
JournalTransportation Planning and Technology
Volume29
Issue number2
DOIs
Publication statusPublished - 2006
MoE publication typeA1 Journal article-refereed

Fingerprint

system structure
Travel time
congestion
monitoring system
travel time
travel
monitoring
road
Monitoring
Multilayer neural networks
prediction
Time measurement
Flow rate
Neural networks
neural network
traffic
effect
time
forecast
analysis

Keywords

  • travel time
  • prediction
  • monitoring system
  • highway

Cite this

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Effect of monitoring system structure on short-term prediction of highway travel time. / Innamaa, Satu (Corresponding Author).

In: Transportation Planning and Technology, Vol. 29, No. 2, 2006, p. 125-140.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Innamaa, Satu

PY - 2006

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AB - This article discusses how the structure of the measurement system affects the short-term forecasts of travel time based on it. The effects of section length and location of different measurement stations were investigated. The study used empirical data. The research was carried out on a 28-km long interurban two-lane highway section. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the division of long road sections into shorter sub-links in the travel time measurement system was important. Furthermore, it would be crucial to obtain information about traffic flow rates entering the section in order to time the start of congestion correctly. In conclusion, the structure of the monitoring system should be based on the analysis of a typical location and the development of congestion along the section.

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