摘要：AbstractMap-matching (MM) algorithms integrate positioning data with spatial road network data to identify the correct link on which a vehicle is travelling and determine the location of a vehicle on a link. There are four classes of MM algorithms, including geometric, topological, probabilistic and advanced. The topological map-matching (tMM) algorithms are relatively simple, easy and quick. Due to considering information of heading, proximity, link connectivity and turn-restriction weights, weight- based tMM algorithms are most robust and widely used tMM algorithms. As is known to all, a metropolis usually has intricate road network. And the urban road density has various performances in different parts of a metropolis’ urban area, which makes the weight scores used in tMM algorithm different. As a result, it can affect the accuracy of matched results. In this paper, the authors develop an enhanced weight-based tMM algorithm considering the urban road density. This algorithm was verified using actual taxi GPS data collected in the urban area of Harbin, China, about 600 positioning points and a 1:80,000 scale digital map of Harbin. The results show that this enhanced weight-based tMM algorithm outperforms the base algorithm and has potential to support many applications of Intelligent Transport System (ITS) service.
