Abstract

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

Highlights

  • The up-to-date road network is important for intelligent transportation applications such as car navigation and route planning

  • With more and more public vehicles are being equipped with GPS devices, a large amount of GPS trajectory data are available

  • Compared with remote sensing images and high-quality GPS data collected by professional survey vehicles, GPS trajectory data collected by public vehicles are relatively cheap and can be acquired in real-time with comprehensive coverage

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Summary

Introduction

The up-to-date road network is important for intelligent transportation applications such as car navigation and route planning. Compared with remote sensing images and high-quality GPS data collected by professional survey vehicles, GPS trajectory data collected by public vehicles are relatively cheap and can be acquired in real-time with comprehensive coverage These advantages have attracted many scholars to use this new data resource to extract and update road network information, such as road centerlines, average travel time, speed limit, number of lanes, and turning rules [3,4,5,6,7,8,9,10,11,12]. The commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions This presents big challenges to existing road network generation and updating methods [13,14].

Related Work
Data Preprocessing
Detection of Additive Changes
Experimental Results
Method
Parameter Sensitivity
Experiment II
Full Text
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