• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for datateknologi og informatikk
  • View Item
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for datateknologi og informatikk
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Traffic flow forecasting with deep learning

Kanestrøm, Per Øyvind
Master thesis
Thumbnail
View/Open
14739_FULLTEXT.pdf (2.276Mb)
14739_COVER.pdf (1.556Mb)
URI
http://hdl.handle.net/11250/2445851
Date
2017
Metadata
Show full item record
Collections
  • Institutt for datateknologi og informatikk [3873]
Abstract
In recent years there has been a vast increase in available data with the ad-

vancement of smart cities. In the domain of Intelligent Transportation Systems

(ITS) this modernisation can positively effect transportation networks, thus cut-

ting down travel time, increase efficacy, and reduce environmental impact from

vehicles.

Norwegian Public Roads Administration (NPRA) is currently deploying a new

vehicle detector system named Datainn on all public roads in Norway. Datainn

sends metadata on all detected vehicles in real time. This includes information

about speed, gap between vehicles, weight, and classification of vehicle type.

Many machine learning approaches has been researched in literature on how

to forecast traffic flow information. One such approach is that of using Artificial

Neural Networks (ANNs). In this research ANN based methods have been explored.

This was done by first performing a state-of-the-art Structured Literature Review

(SLR) on ANN methods in literature.

From the review, Stacked Sparse Autoencoder (SSAE) model was compared

with recent advances of Long Short-Term Memory (LSTM) and Deep Neural

Network (DNN) on four different prediction horizons. The data foundation was

the new Datainn system using traffic data from a highway around Norway s

capitol, Oslo. Further, the model performance was assessed with extended feature

vectors including more metadata from Datainn.

The results found that the LSTM model always outperformed DNN and SSAE,

although in general the performance characteristics was somewhat similar. Ex-

tending the feature vector with more variables had a negative effect on DNN,

while resulting in better performance for Recurrent Neural Network (RNN) on

long-term (60 minute) forecasting horizons. For SSAE it had a slight positive

effect, but not enough get better results than RNN or DNN.
Publisher
NTNU

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit