An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories

Abstract

Extracting highly detailed and accurate road network information from crowd-sourced vehicle trajectory data, which has the advantages of being low cost and able to update fast, is a hot topic. With the rapid development of wireless transmission technology, spatial positioning technology, and the improvement of software and hardware computing ability, more and more researchers are focusing on the analysis of Global Positioning System (GPS) trajectories and the extraction of road information. Road intersections are an important component of roads, as they play a significant role in navigation and urban planning. Even though there have been many studies on this subject, it remains challenging to determine road intersections, especially for crowd-sourced vehicle trajectory data with lower accuracy, lower sampling frequency, and uneven distribution. Therefore, we provided a new intersection-first approach for road network generation based on low-frequency taxi trajectories. Firstly, road intersections from vector space and raster space were extracted respectively via using different methods; then, we presented an integrated identification strategy to fuse the intersection extraction results from different schemes to overcome the sparseness of vehicle trajectory sampling and its uneven distribution; finally, we adjusted road information, repaired fractured segments, and extracted the single/double direction information and the turning relationships of the road network based on the intersection results, to guarantee precise geometry and correct topology for the road networks. Compared with other methods, this method shows better results, both in terms of their visual inspections and quantitative comparisons. This approach can solve the problems mentioned above and ensure the integrity and accuracy of road intersections and road networks. Therefore, the proposed method provides a promising solution for enriching and updating navigable road networks and can be applied in intelligent transportation systems.the latest changes in the actual road. With the development of the Internet and the popularity of GPS technology, the methods of geographic information acquisition have undergone tremendous changes. More and more locations can be reached via GPS or hand-held devices. Additionally, data are collected and uploaded by the public. These phenomena stimulate the generation of crowd-sourced trajectory data, which are acquired by soliciting contributions from a large group of volunteers. These data are different from those obtained by traditional measurement and remote sensing methods and have the advantages of being low cost, in real-time, and on a large scale, which is more suitable for the acquisition and rapid update of large-scale rural [5] and urban [6] road networks. At the same time, these data contain a large amount of driving record information, which makes a great contribution to the extraction of road-related geometry and attribute information, such as sidewalk networks [7], implicit entities [8], road boundary infor...

Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

“…Delaunay triangulation network was constructed, and corresponding judgment criteria were proposed to identify links based on trajectory distribution and road structure features. We also fused the road extraction results based on the morphology method [ 1 ] to optimize true link identification. Targeted link fitting.…”

“…CFDP algorithm is used to detect road intersections thanks to its threshold settings and stability of results [ 1 ]. In order to find density peaks, this algorithm needs to calculate the local density and distance of cell points.…”

“…Furthermore, this paper only constructs links based on road intersections; there will also have some missing links in the edge region, as the yellow line shows in A district of Figure 5 b. Therefore, this paper integrates morphological methods [ 1 ] to optimize and supplement Type I links identified by Criterion 1, Criterion 2, and Criterion 3, which can help eliminate more false links by using other criteria and generate more precise road network. Preliminary identification results after optimization are shown in Figure 5 c. The optimization steps are as follows: Extracting missing road segments.…”

“…Road networks are of great significance to urban development and for traveling. How to obtain road information for reasonable planning and resource allocation has always been an economic issue for national economies and people’s livelihoods [ 1 ]. With the development of surveying, mapping, communications, computers, and other technologies, we can infer road networks based on various data sources, such as crowd-sourced vehicle trajectories [ 2 , 3 , 4 , 5 ], laser point clouds [ 6 , 7 ], remote sensing images [ 8 , 9 ], aerial images [ 10 , 11 , 12 ], OpenStreetMap [ 13 , 14 , 15 ], etc.…”

Fusing Taxi Trajectories and RS Images to Build Road Map via DCNN

“…The previous studies provided multiple methods for road intersection detection, which can be divided into two categories. One category is detecting road intersections directly by analyzing geometric characteristics and spatial relationship in data source [36], [37]. The other category detects roadways first, and then intersections are recognized as the location where roadways meet [34], [35].…”