A Data Correction Algorithm for Low-Frequency Floating Car Data
1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3639; https://doi.org/10.3390/s18113639
Received: 25 September 2018
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Revised: 19 October 2018
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Accepted: 24 October 2018
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Published: 26 October 2018
(This article belongs to the Section Remote Sensors)
The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.
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Keywords:
data correction; map matching; OpenStreetMap; floating car
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MDPI and ACS Style
Li, B.; Guo, Y.; Zhou, J.; Cai, Y. A Data Correction Algorithm for Low-Frequency Floating Car Data. Sensors 2018, 18, 3639. https://doi.org/10.3390/s18113639
AMA Style
Li B, Guo Y, Zhou J, Cai Y. A Data Correction Algorithm for Low-Frequency Floating Car Data. Sensors. 2018; 18(11):3639. https://doi.org/10.3390/s18113639
Chicago/Turabian StyleLi, Bijun, Yuan Guo, Jian Zhou, and Yi Cai. 2018. "A Data Correction Algorithm for Low-Frequency Floating Car Data" Sensors 18, no. 11: 3639. https://doi.org/10.3390/s18113639
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