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Speed prediction for triggering vehicle activated signs
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0001-6526-6537
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
2016 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
2016. , s. 16
Serie
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2016:01
Nyckelord [en]
vehicle activated signs; trigger speed; adaptive neuro-fuzzy inference systems; classification and regression tree; Random forest; multiple linear regression; mean speed; traffic flow
Nationell ämneskategori
Datorteknik
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-20614OAI: oai:DiVA.org:du-20614DiVA, id: diva2:891071
Tillgänglig från: 2016-01-05 Skapad: 2016-01-05 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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