Abstract
Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.
Highlights
Street maps and transportation networks are the bases of building smart cities
This paper presents an algorithm for incrementally extracting road network graphics from vehicle trajectory data
The generated road network has the advantages of timely updating and high accuracy
Summary
Street maps and transportation networks are the bases of building smart cities. High accurate road network maps have great social and application values. (ii) Incremental track insertion uses the idea of map matching to gradually insert the trajectory into the initial map to construct a road network map [1,12,13,14] Representative algorithms in this class include the following. Bruntrup et al [13] proposed a spatial-clustering based algorithm that allows incrementally generating a road network, but the algorithm requires high quality (sampling rate and positional accuracy) tracking data. Ahmed et al [14] proposed an incremental algorithm for the road network construction that matching of trajectories and map is achieved by Fréchet distance. An algorithm for incrementally learning vehicle trajectory data and generating a road network is proposed.
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