A Road Network Extraction Method Based on Spatial Relationship and Distribution of Track Points of Floating Vehicles

Abstract

The present invention relates to a road network extraction method based on the spatial relationship and distribution of trajectory points of floating vehicles, comprising the following steps: S1: data conversion and cleaning; S2: establishment of a two-layer spatial grid index G; S3: calculation of the spatial distribution F of trajectory points ; S4: calculate the core point candidate set H based on the spatial distribution of the trajectory points; S5: calculate the core point P, calculate the geometric centroid of the core point candidate set, in the core point candidate set H, the trajectory point closest to the centroid is marked as the core point ; S6: core point connection, based on the method of clustering the direction of the trajectory points, and considering the factors of spatial distance, the traffic correlation function C (A, B) between adjacent grids is obtained, and the traffic correlation function C in the same direction is obtained. (A, B) The largest core point is connected; S7: After connecting all the core points, the road network is extracted. It has the advantages of stable and reliable operation, high road network extraction efficiency and accurate precision, and can be applied to floating vehicle data under various conditions.

Description

technical field [0001] The invention relates to the field of application technology based on big data of floating car trajectory, in particular to a road network extraction method based on the spatial relationship and distribution of floating car trajectory points. Background technique [0002] Floating vehicle technology is one of the dynamic traffic acquisition technologies often used in intelligent transportation systems in recent years. Its outstanding advantage is that it can obtain accurate and real-time dynamic traffic information through a small number of floating vehicles equipped with satellite positioning-based vehicle equipment, which is low in cost and efficient. High, with the characteristics of strong real-time performance and large coverage. [0003] Traditional road network extraction methods are mainly divided into on-the-spot surveying and mapping methods and generation methods based on remote sensing images. The former has a higher accuracy rate, but it ...

AI Technical Summary

Problems solved by technology

Some of these algorithms have high requirements on the data collection frequency of floating vehicles (the time interval between adjacent trajectory points is less than 10 seconds), but they are suitable for urban road networks, and the application space is relatively narrow; That is to say, the platform requirements of the research are relatively high; the road network constructed by some algorithms is more accurate, but the coverage is not good, and the time efficiency is very low, which is not conducive to popularization and application

However, for low-frequency sampling (about half a minute between adjacent track points) floating car data, the existing road network extraction algorithms based on floating car trajectories cannot be well applied to the extraction of ordinary roads.

Images