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Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means
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.
2014 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

Ort, förlag, år, upplaga, sidor
2014.
Nyckelord [en]
Solar-powered vehicle activated signs; Self Organising Maps; K-means clustering; Trigger speed
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-14133OAI: oai:DiVA.org:du-14133DiVA, id: diva2:719468
Konferens
The Third International Conference on Intelligent Systems and Applications, INTELLI 2014, June 22 - 26, 2014 - Seville, Spain
Tillgänglig från: 2014-05-25 Skapad: 2014-05-25 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Ingår i avhandling
1. A data driven approach for automating vehicle activated signs
Öppna denna publikation i ny flik eller fönster >>A data driven approach for automating vehicle activated signs
2016 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2016
Serie
Dalarna Doctoral Dissertations in Microdata Analysis ; 4
Nyckelord
Optimal trigger speed, vehicle activated sign, mean speed, standard deviation, calibration, driver behaviour, data driven approach, automatic model
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-21504 (URN)978-91-89020-96-2 (ISBN)
Disputation
2016-06-16, Clas Ohlson, Borlänge, 13:00 (Engelska)
Handledare
Tillgänglig från: 2016-05-30 Skapad: 2016-05-30 Senast uppdaterad: 2019-06-17Bibliografiskt granskad

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http://www.iaria.org/conferences2014/INTELLI14.htmlhttp://www.iaria.org/conferences2014/ProgramINTELLI14.html

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