Change search
ReferencesLink to record
Permanent link

Direct link
Speed prediction for triggering vehicle activated signs
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0001-6526-6537
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2016 (English)Report (Other academic)
Abstract [en]

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Place, publisher, year, edition, pages
2016. , 16 p.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2016:01
Keyword [en]
vehicle activated signs; trigger speed; adaptive neuro-fuzzy inference systems; classification and regression tree; Random forest; multiple linear regression; mean speed; traffic flow
National Category
Computer Engineering
Research subject
Complex Systems – Microdata Analysis
URN: urn:nbn:se:du-20614OAI: diva2:891071
Available from: 2016-01-05 Created: 2016-01-05 Last updated: 2016-01-12Bibliographically approved

Open Access in DiVA

fulltext(513 kB)87 downloads
File information
File name FULLTEXT01.pdfFile size 513 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Jomaa, DialaYella, SirilDougherty, Mark
By organisation
Computer Engineering
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 87 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 311 hits
ReferencesLink to record
Permanent link

Direct link