Abstract
The main purpose of this study was to investigate the predictability of
travel time with a model based on travel time data measured in the field
on an interurban highway. Another purpose was 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-kilometre-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 main results showed that the majority of the forecasts
were close to the actual measured values. Consequently, use of the
prediction model would improve the quality of travel time information
based directly on the sum of the latest measured travel times.
| Original language | English |
|---|---|
| Pages (from-to) | 649 - 669 |
| Number of pages | 21 |
| Journal | Transportation |
| Volume | 32 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2005 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- neural networks
- prediction
- travel time
- two-lane highway
- road traffic
- traffic
- traffic congestion