Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55558
Title: Traffic condition monitoring using weighted kernel density for intelligent transportation
Authors: Lee, CC
Lee, WC 
Cai, H
Chi, HR
Wu, CK
Haase, J
Gidlund, M
Keywords: Estimation
Internet
Kernel
Navigation
Real-time systems
Vehicles
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, 22-24 July 2015, 7281807, p. 624-627 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/55558
ISBN: 9781479966493
DOI: 10.1109/INDIN.2015.7281807
Appears in Collections:Conference Paper

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