Travel time is considered the most useful travel related information as it is the best indicator of the level of service on the road stretch and is completely understandable to all users. Various technologies for measuring traffic flow parameters provide the optimal background for the implementation of data fusion schemes to gain the maximum accuracy from the combination of the available data. The objective of the data fusion is to gain knowledge of predicted departure based travel time from the two outdated accurate measurements. In this paper a new and simple algorithm is proposed for short-term highway travel time prediction by fusing direct travel time measurements estimated by vehicle reidentification, indirect travel time estimated by the extrapolation of spot speed measurements and additional qualitative data in terms of the level of service. The proposed algorithm has been in operation on the A1 highway in Slovenia for more than two years and has shown robust behaviour in the real world environment. The algorithm is capable of providing short-term travel time prediction in real time with a 9 % better accuracy than the presently used travel time prediction algorithms.
Highway travel time estimation using multiple data sources
IET Intelligent Transport Systems ; 10 , 10 ; 649-657
2016-12-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
optimal background , short-term highway travel time prediction , level-of-service , road stretch , vehicle reidentification , data fusion scheme , departure based travel time , learning (artificial intelligence) , indirect travel time estimation , Slovenia , traffic flow parameter measurement , road traffic control , qualitative data , travel time forecasting algorithms , spot speed measurements , direct travel time measurement fusion , extrapolation , traffic events , multiple data sources , A1 highway , spot speed extrapolation , LOS , velocity measurement
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