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Predicting automatic trigger speed for vehicle-activated signs
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0001-6526-6537
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2018 (English)In: Journal of Intelligent Systems, ISSN 0334-1860, E-ISSN 2191-026XArticle in journal (Refereed) In press
Abstract [en]

Vehicle-activated signs (VAS) are speed-warning signs activated by radar when the driver speed exceeds a pre-set threshold, i.e. the trigger speed. The trigger speed is often set relative to the speed limit and is displayed for all types of vehicles. It is our opinion that having a static setting for the trigger speed may be inappropriate, given that traffic and road conditions are dynamic in nature. Further, different vehicle classes (mainly cars and trucks) behave differently, so a uniform trigger speed of such signs may be inappropriate to warn different types of vehicles. The current study aims to investigate an automatic VAS, i.e. one that could warn vehicle users with an appropriate trigger speed by taking into account vehicle types and road conditions. We therefore investigated different vehicle classes, their speeds, and the time of day to be able to conclude whether different trigger speeds of VAS are essential or not. The current study is entirely data driven; data are initially presented to a self-organising map (SOM) to be able to partition the data into different clusters, i.e. vehicle classes. Speed, time of day, and length of vehicle were supplied as inputs to the SOM. Further, the 85th percentile speed for the next hour is predicted using appropriate prediction models. Adaptive neuro-fuzzy inference systems and random forest (RF) were chosen for speed prediction; the mean speed, traffic flow, and standard deviation of vehicle speeds were supplied as inputs for the prediction models. The results achieved in this work show that RF is a reliable model in terms of accuracy and efficiency, and can be used in finding appropriate trigger speeds for an automatic VAS. 

Place, publisher, year, edition, pages
2018.
Keywords [en]
adaptive neuro-fuzzy inference systems, random forest, self-organising maps, trigger speed, vehicle-activated signs
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-29039DOI: 10.1515/jisys-2016-0329Scopus ID: 2-s2.0-85057306921OAI: oai:DiVA.org:du-29039DiVA, id: diva2:1269194
Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2018-12-10Bibliographically approved

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Jomaa, DialaYella, Siril

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CiteExportLink to record
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