Abstract
Keeping road network databases up-to-date is crucial to Geographical Information System (GIS) applications such as road networking. The vector road centerlines extracted from field surveys and satellite images are expensive and labor intensive with long updating processes. The GPS data crowd-sourced by public transportation users, provides an expanding source for enhancing road maps because of its rich spatial-temporal coverage and reasonable level of accuracy. The overall objective of this project is to implement an optimized methodology, which generates road centerline from GPS data obtained from taxis in Beijing without using any reference plans. Since the dataset used in this project has longer time intervals between trajectories compared to previous studies, the extracted road network on straight road segments are more accurate than the extracted road network on highway ramps in this project.
Highlights
1.1 Motivation Keeping road network databases up-to-date is crucial to GeographicalInformation System (GIS) applications such as road networking
The more traditional methods for collecting vector road centerlines from field surveys and satellite images are expensive and labor intensive with long updating processes
This chapter includes the results of implementing the automatic road network extraction algorithm and a visual comparison with an open street map data is provided. 5.1 Experimental Results The data size was reduced by 82.5% after applying the standard circular window-smoothing algorithm, due to the 2-Gigabyte memory limitation of PythonWin, three typical regions were selected as the final case study in this project
Summary
Information System (GIS) applications such as road networking. The more traditional methods for collecting vector road centerlines from field surveys and satellite images are expensive and labor intensive with long updating processes. Many municipalities all around the globe have installed GPS receivers in their taxis for tracking, managing and improving their services. This sort of data can be interpreted and processed to identify traffic patterns, road geometry and network connectivity. Many studies have investigated road network extraction from GPS points, one of which is automatic road network extraction introduced by Niu (2013). In this project, the motive is to apply Niu’s (2013) method on Beijing taxi GPS data and examine if his method can be adapted to this datasets and if any modifications need to be made. The main challenge in this project is to modify the Python scripts written by Niu (2013) to work with the Beijing taxi data
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