A Data Correction Algorithm for Low-Frequency Floating Car Data

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ID: 110044
2018
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Abstract
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.
Reference Key
li2018sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bijun Li;Yuan Guo;Jian Zhou;Yi Cai;Li, Bijun;Guo, Yuan;Zhou, Jian;Cai, Yi;
Journal sensors
Year 2018
DOI
10.3390/s18113639
URL
https://www.mdpi.com/1424-8220/18/11/3639
Keywords
map matching data correction openstreetmap floating car

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