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Enhancing the value of commercial vehicle telematics data through analytics and optimisation techniques

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Telematics data has been mainly used to track the historical and live locations of vehicles. Recently, with more data available and better telecommunication technologies, fleet managers are able to investigate the root cause of faults, problems or events such as breakdown and incident. With recent development in cloud computing power, advanced analytics and optimisation techniques, fleet telematics can move to the next level, where future events can be predicted and systems can schedule maintenance to avoid breakdowns. Risk of accidents can also be raised and advance warnings given. In this paper, the evolution and current state of analytics and optimisation techniques and models in the area of telematics have been reviewed. Later, the key components, challenges and opportunities of applications of analytics in fleet telematics have been discussed.
Słowa kluczowe
Rocznik
Strony
27--30
Opis fizyczny
Bibliogr. 9 poz.
Twórcy
  • Microlise Ltd, Eastwood, Nottingham, NG16 3AG, UK
autor
  • Microlise Ltd, Eastwood, Nottingham, NG16 3AG, UK
Bibliografia
  • [1] Omnitracs, LLC, http://www.omnitracs.com, (accesed: 25.06.2014)
  • [2] The Freight Transport Association (FTA), The Logistics Report 2014, (2014)
  • [3] Goel , A., Gruhn , V.: A Fleet Monitoring System for Advanced Tracking of Commercial Vehicles. In: SMC’06. IEEE International Conference on Systems, Man and Cybernetics 3 (2006)
  • [4] Geczy, P.: Big Data Characteristics, The Macrotheme Review, 3(6) (2014)
  • [5] Maoz , M., How IT Should Deepen Big Data Analysis to Support Customer-Centricity. Gartner G00248980 (2013)
  • [6] SAS, Eight Levels of Analytics. sascom magazine, 4th quarter (2008)
  • [7] Mesgarpour , M., Landa -Silva , D., Dickinson , I.: Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles. In: Mikulski J. (ed.) CCIS 395, Springer Heidelberg (2013)
  • [8] Rezvani, M., AbuAli, M., Lee, S., Lee, J., Ni, J.: A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM). In: SAE International (2011)
  • [9] FleetRisk Advisors, http://www.fleetriskadvisors.com, (accessed: 25.06.2014)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-255fd7c9-18e7-4795-b5b6-ac0f1f784811
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