Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation
Bibliographic Data
| ID | 22033477 |
|---|---|
| Authors | Qingying Yu (0000-0002-5816-3190, Anhui Normal University), Fan Hu (0000-0002-7929-6953, Anhui Normal University), Chuanming Chen (0000-0002-0296-8192, Anhui Normal University, corresponding author), Liping Sun (0000-0003-3637-0305, Anhui Normal University), Xiaoyao Zheng (0000-0001-7554-4211, Anhui Normal University) |
| Year | 2022 |
| Volume | 11 |
| Issue | 7 |
| Pages | 355 |
| Publication date | 2022-06-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11070355 |
| OpenAlex | W4283371651 |
| Language | EN |
| Citations received | 1 |
| References cited | 29 |
Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms
Computer vision · Data mining · Geography · Global Positioning System · Map matching · Segmentation · Trajectory · Automated Road and Building Extraction · Computer Science · Human Mobility and Location-Based Analysis · Mathematics · Traffic Prediction and Management Techniques · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |