Highway travel time estimation using multiple data sources

作者：Pirc Jure; Turk Goran; Zura Marijan

来源：IET Intelligent Transport Systems, 2016, 10(10): 649-657.

DOI：10.1049/iet-its.2015.0211

• road traffic control
• extrapolation
• velocity measurement
• learning (artificial intelligence)
• Slovenia
• A1 highway
• traffic events
• travel time forecasting algorithms
• LOS
• qualitative data
• spot speed measurements
• spot speed extrapolation
• indirect travel time estimation
• vehicle reidentification
• direct travel time measurement fusion
• short-term highway travel time prediction
• departure based travel time
• data fusion scheme
• optimal background
• traffic flow parameter measurement

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 The objective of the data fusion is to gain knowledge of predicted departure based travel time from the two outdated accurate measurements.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.

• 出版日期2016-12