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Traffic condition monitoring using weighted kernel density for intelligent transportation
School of Science and Technology, Open University of Hong Kong, Hong Kong.
Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong.
Department of Electronic Engineering, City University of Hong Kong, Hong Kong.
Department of Electronic Engineering, City University of Hong Kong, Hong Kong.
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2015 (English)In: Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, 2015, 624-627 p.Conference paper, Published paper (Refereed)
Abstract [en]

Smart transportation is an application of intelligent system on transportation domain, expected to bring the society environmental and economic advantages. By combining with IoT techniques, the concept is being enhanced and raised to a system level. Numerous data are able to collect and effective analysis technique is needed. Here in this paper, we proposed a framework of employing IoT technique to construct a free time navigation system. The system aims at providing a real-time quantification of traffic conditions and suggests optimal route based on the information retrieved. The system can be basically separated into two major components: (i) the traffic condition estimation module and the (ii) real-time routing algorithm. In the first component, traffic conditions of roads will be estimated based the information collected from sensors installed on vehicles. Based on these location and speed information, the traffic condition can be quantified using a weighted kernel density estimation (WKDE) function. This function is a function of time and provides a real time insight of the overall traffic condition. By combining this information and the topological structure of the road network, a more accurate time consumption on each road can be estimated and hence enable a better routing.

Place, publisher, year, edition, pages
2015. 624-627 p.
Keyword [en]
Estimation, Internet, Kernel, Navigation, Real-time systems, Vehicles
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-26715DOI: 10.1109/INDIN.2015.7281807ISI: 000380453900090Scopus ID: 2-s2.0-84949520771Local ID: STCISBN: 978-147996649-3 (print)OAI: oai:DiVA.org:miun-26715DiVA: diva2:885928
Conference
13th International Conference on Industrial Informatics, INDIN 2015; Robinson CollegeCambridge; United Kingdom; 22 July 2015 through 24 July 2015
Available from: 2015-12-21 Created: 2015-12-21 Last updated: 2016-12-23Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf