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Triggering Radar Speed Warning signs using Association Rules and Clustering Techniques
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.
2012 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Radar speed warning signs (RSWS) have been used in recent years across Sweden and elsewhere in the world. Such signs measure vehicle speed using radar and are designed to display a message when the driver exceeds a pre-set threshold speed, which is often relative to the speed limit on a particular road segment. RSWS are typically placed on locations which are perceived to be problematic by relevant authorities. Excessive speeding or road accidents are examples of such perceived problems.  Deploying RSWS in many relevant locations is often impractical due to the lack of necessary power supply needed for operation. Battery driven RSWS are an alternative but are less attractive because of limited running time and frequent maintenance (changing batteries etc). Therefore, solar powered RSWS are more desirable. However, these signs are also dependent on batteries that need to be charged. The duration of operation of solar powered RSWS largely depend on how often the sign is triggered. Constant activation of the sign drains the battery. It is desirable to trigger the sign only when necessary. Hence, the main goal of this research is to design a model that optimises the performance of RSWS depending on prevailing conditions i.e traffic flows during different times of the day and so on.  Vehicle speed data had been collected at a test site in Sweden all hours of the day. This paper attempts to use a hybrid system based on Apriori and K-means clustering algorithm. Apriori algorithm is simple and efficient to determine associations’ rules among attributes in particular to discover the most common combination that can occur within the data set.  K-means clustering is basically used to quantize the input variables into smaller clusters that can easily derive the trigger threshold value. The proposed hybrid system indicated that the system was able to trigger solar RWWS efficiently.

Ort, förlag, år, upplaga, sidor
Stockholm, 2012.
Nationell ämneskategori
Datorteknik
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-11800OAI: oai:DiVA.org:du-11800DiVA, id: diva2:602934
Konferens
Första nationella konferensen i transportforskning, Stockholm, 18-19 oktober 2012
Tillgänglig från: 2013-02-04 Skapad: 2013-02-04 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Jomaa, Diala

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Jomaa, DialaDougherty, MarkYella, Siril
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