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Automatic trigger speed for vehicle activated signs using Adaptive Neuro fuzzy system and Random Forest
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.ORCID-id: 0000-0001-6526-6537
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
(engelsk)Inngår i: International Journal on Advances in Intelligent Systems, ISSN 1942-2679, E-ISSN 1942-2679Artikkel i tidsskrift (Fagfellevurdert) Submitted
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-21503OAI: oai:DiVA.org:du-21503DiVA, id: diva2:931716
Tilgjengelig fra: 2016-05-30 Laget: 2016-05-30 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Inngår i avhandling
1. A data driven approach for automating vehicle activated signs
Åpne denne publikasjonen i ny fane eller vindu >>A data driven approach for automating vehicle activated signs
2016 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated.

Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

sted, utgiver, år, opplag, sider
Borlänge: Dalarna University, 2016
Serie
Dalarna Doctoral Dissertations in Microdata Analysis ; 4
Emneord
Optimal trigger speed, vehicle activated sign, mean speed, standard deviation, calibration, driver behaviour, data driven approach, automatic model
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-21504 (URN)978-91-89020-96-2 (ISBN)
Disputas
2016-06-16, Clas Ohlson, Borlänge, 13:00 (engelsk)
Veileder
Tilgjengelig fra: 2016-05-30 Laget: 2016-05-30 Sist oppdatert: 2019-06-17bibliografisk kontrollert

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