The fast matching of road network data is very important for navigation map update, traffic planning and management, emergency response and so on . With the continuous development of Web technology and the popularisation of intelligent mobile devices, the road network matching and updating technology based on volunteered geographic information (VGI) data has become a hot research topic in intelligent transportation system (ITS)- and geographic information system (GIS)-related industries.
Depending on the matching purposes, the road network matching methods based on VGI data can be divided into two categories [2, 3]: (1) road network improvement method; and (2) road network reconstruction method.
The former ones adopted track data to improve the existing road network data, such as extracting the node or centreline of the appointed road. In previous works , initial map and track data was used to improve the centreline of the designated roads in the road network. Tao et al.  used a spiral road network–updating strategy, which used a small amount of track data to update the road network locally. Zong-yao et al.  used the road mesh data to achieve incremental update of road network, which used the track point context distance information and hidden Markov model (HMM) to capture the mismatched track points. Li et al.  studied an urban road–updating method for low-sampling-rate floating vehicle data, which adopted an improved A* shortest path algorithm to improve the matching accuracy. Generally speaking, this kind of method relies on manual recognition and it is difficult to achieve automatic and batch processing.
The latter category of works directly extracted the geometric features of the entire road network from the track data and made a comprehensive comparison between the whole and local parts of the road network using the spatial scale and semantic features. Tian et al.  used a multi-level spreading algorithm to compare VGI data with old vector data to achieve incremental recognition of road network changes. In another paper , the characteristics of VGI data were fully utilised, a road segment distance similarity-matching algorithm and Delaunay triangulation fusion algorithm were adopted to achieve road network matching. Zhi-guo et al.  proposed a vehicle-track map-matching method based on Delaunay triangulation fusion algorithm (DST-MM), which was superior to existing algorithms in terms of low-sampling-frequency global positioning system (GPS) data-matching accuracy. To update and match the road network changes, Wang et al.  studied a new multi-scale dynamic method based on a hierarchical stroke mesh to detect matches between OpenStreetMap (OSM) data and professional surveying and mapping data. Zhang et al.  proposed a novel turning point-based offline map-matching method, which had the best efficiency at different sampling intervals. All in all, this kind of method needs to process massive track data with high computational complexity, so the matching accuracy is easily affected by data quality, road structure and other factors.
Global navigation satellite system (GNSS) track data is one of the main sources of VGI data, which can obtain mass travel trajectory data quickly and provide rich data resources for academic research . However, GNSS track data has some problems, such as uncertain data quality, uneven spatial distribution and multi-source heterogeneity, which bring some difficulties and challenges to the road network-matching research using GNSS data.
This paper studies a road network-matching method based on GNSS track data, which can achieve road network data matching using the rough-to-fine strategy based on distance proximity and buffer constraints. The innovation of this paper can be summarised as follows. (1) The low-quality data in GNSS track data is removed by preprocessing and the deleted data is stored in vector form. (2) A distance proximity method is proposed for rough matching. (3) A rectangular or circular buffer is selected to achieve precise matching of road network data according to the buffer constraint criterion.
Figure 1 shows the structure diagram of this paper, which has two main processes: preprocessing and two-layer matching strategy.
On the one hand, a rasterisation method is applied to eliminate low-quality track data, and then the track data is converted into vector format by binarisation, refinement and boundary-tracking operations. On the other hand, to achieve the matching of the unchanged road arc, disappearing road arc and the global and local new road arcs, different buffer constraint schemes are selected depending on the proposed distance proximity.
The GNSS track data simplifies the data acquisition method, enriches the data source and reduces the acquisition cost. In practice, the sampling point is sampled at a certain distance interval or time. The GNSS track data can be denoted as follows:
GNSS track data has a distinct aggregation nature, i.e. most of them are located in the area on both sides of the road. However, due to the influencing factors, such as uncertainty of driving routes or speeds, sampling intervals or non-standard sampling methods, there are low-quality track data with inconsistent spatial distribution and data redundancy problems .
In order to reduce the complexity of the matching algorithm effectively, we adopt a rasterisation method to eliminate low-quality track data using the aggregation property of track data. The proposed rasterisation method is as follows. (1) Set the grid size in the original road network data, which is consistent with the vehicles’ size; (2) judge whether the track data falls into the grid; if it falls into the grid, the track data will be retained; otherwise, the track data will be removed.
For ease of identification, the original road network data is denoted by
Data format conversion is to convert the GNSS track data into vector data. The process is as follows: the raster data is binarised to get the binary image; a skeleton extraction method  is used to refine the road centreline; a boundary tracking method  is adopted to change the obtained road centreline data into line segments starting from nodes or closed lines and then store them in vector form.
The converted GNSS track data is denoted by
According to whether there is a relative change of road arc, the state of change of the road network can be divided into three situations: (1) unchanged road arc, i.e.
Due to different data collection methods and time, the spatial location of the same road section is quite different. Direct road network data matching will not only lead to large errors but also reduce the running speed of the algorithm [17, 18]. Hence, a rough matching strategy is carried out to estimate the changing state of
The rough matching strategy is a distance proximity criterion, which is the ratio of the Euclidean distance between the beginning and the end nodes for
Ideally, when the value of
The fine matching method based on rectangular buffer constraint is proposed to determine whether there are unchanged road arcs and global newly added road arcs.
As shown in Figure 2,
To determine whether
It can be concluded that if
The fine matching method based on circular buffer constraint is established to judge whether there are newly added local road arcs or missing road arcs. The proposed circular buffer constraint is shown in Figure 3.
The relationship between the two road arcs is measured by the ratio of
To sum up, the execution process of the proposed method is shown in Figure 4.
Step 1: Input
Step 2: Establish the minimum enclosing rectangle
Step 3: Judge whether
Step 5: Count the number of
Step 6: Check whether the input of
Step 7: When the road arc cannot be searched or the specified number of iterations is reached, exit the iteration and end of the algorithm.
In this paper, the experiment takes Visual Studio 2019 as the development environment and is written with ArcGIS10.3 and C++. The GNSS track data is first preprocessed, and then the distance proximity criterion is used to judge the changing state of the road arc so as to select different fine-matching strategies. In the process of matching, it is necessary to construct a rectangular buffer for the reference road network data, and the size of the buffer distance is critical to the matching result . The buffer distance is related to the average width of the road, the errors in data conversion and calculation; so, the average width of the actual road prevails in the experiment.
The experimental data is a public dataset , including 1:500 vector road network data and GNSS track data in Chicago. There are 543 track data and 83,542 sampling points in total. Figure 5 shows the proposed road network data matching process, Figure 5(A) is the original road network data, and Figure 5(B) is the GNSS track data.
As shown in Figure 5(C), after rasterisation of the GNSS track data, the low-quality track data is eliminated, and the grid size is basically the same as the vehicle size with 5 m × 5 m. The grid data is binarised, as shown in Figure 5(D), where the grey value of the track data that falls into the grid is set to zero; otherwise, it is set to one. The thinning result is shown in Figure 5(E). There exists deviation in the actual matching process; so, the value of the distance proximity that is close to 1.0 (1.05 ≥
In order to verify the effectiveness of the road network matching method proposed in this paper, a set of comparative tests are given, as shown in Figure 6. To display the matching results more intuitively, the original road network data is marked as blue and the vectorised GNSS track data is marked as red.
Figure 6 shows the matching result of road network data with more nodes and arcs. Figure 6(A) is the original road network, including 187 road nodes and 232 road arcs. Figure 6(B) is the vectorised GNSS track data, including 225 road nodes and 254 road arcs. As can be seen from the matching results in Figure 6(C) (blue represents the unchanged road area, red represents the newly added road area, and green represents the lost road area), although effective matching cannot be achieved for some small and curved road arcs for the relatively complex road data, the proposed matching algorithm has ideal matching effect in general.
The proposed matching method based on distance proximity and buffer constraints comprehensively considers the spatial geometric information of road arcs and nodes and takes into account the overall and local matching scheme, which lays a feasible foundation for road network data fusion. How to improve the applicability of the proposed method to road arcs with less track data needs to be further verified. In addition, the current matching methods are mainly aimed at the road network data of the same scale; there are relatively few mature technologies for data on different scales. We will pay more attention to matching of different-scale data in future work.
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