Name
Abstract | ||
|---|---|---|
Integrating raw Global Position System (GPS) trajectories with a road network is often referred to as a map-matching problem. However, low-frequency trajectories (e.g., one GPS point for every 1–2 min) have raised many challenges to existing map-matching methods. In this paper, we propose a novel and global spatial–temporal map-matching method called spatial and temporal conditional random field (ST-CRF), which is based on insights relating to: 1) the spatial positioning accuracy of GPS points with the topological information of the underlying road network; 2) the spatial–temporal accessibility of a floating car; 3) the spatial distribution of the middle point between two consecutive GPS points; and 4) the consistency of the driving direction of a GPS trajectory. We construct a conditional random field model and identify the best matching path sequence from all candidate points. A series of experiments conducted for real environments using mass floating car data collected in Beijing and Shanghai shows that the ST-CRF method not only has better performance and robustness than other popular methods (e.g., point-line, ST-matching, and interactive voting-based map-matching methods) in low-frequency map matching but also solves the “label-bias” problem, which has long existed in the map matching of classical hidden Markov-based methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TITS.2016.2604484 | IEEE Trans. Intelligent Transportation Systems |
Keywords | Field | DocType |
Global Positioning System,Roads,Hidden Markov models,Trajectory,Automobiles,Information systems,Training | Conditional random field,Computer vision,Digital mapping,Simulation,Floating car data,Robustness (computer science),Global Positioning System,Artificial intelligence,Engineering,Hidden Markov model,Assisted GPS,Map matching | Journal |
Volume | Issue | ISSN |
18 | 5 | 1524-9050 |
Citations | PageRank | References |
11 | 0.63 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiliang Liu | 1 | 166 | 13.32 |
| Kang Liu | 2 | 1542 | 89.33 |
| Mingxiao Li | 3 | 17 | 3.48 |
| Feng Lu | 4 | 54 | 13.55 |