A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data

The growing use of probe vehicles generates a huge number of GNSS data. Limited by the satellite positioning technology, further improving the accuracy of map-matching is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is the most useful with the least amount of data. In addition, there are a large amount of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most map-matching studies only used the ego vehicle's data and ignored other vehicles' data. Based on it, this paper designs a new map-matching method to make full use of "Big data". We first sort all data into four groups according to their spatial and temporal distance from the present matching probe which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, a score for historical usage, and a score for traffic state using the spectral graph Markov neutral network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance map-matching accuracy. Furthermore, our method outperforms the others, especially when GNSS probing frequency is less than 0.01 Hz.Comment: 10 pages, 11 figures, 4 table

Cryopreservation of Orchid Genetic Resources by Desiccation: A Case Study of Bletilla formosana

Many native orchid populations declined yearly due to economic development and climate change. This resulted in some wild orchids being threatened. In order to maintain the orchid genetic resources, development of proper methods for the long‐term preservation is urgent. Low temperature or dry storage methods for the preservation of orchid genetic resources have been implemented but are not effective in maintaining high viability of certain orchids for long periods. Cryopreservation is one of the most acceptable methods for long‐term conservation of plant germplasm. Orchid seeds and pollens are ideal materials for long‐term preservation (seed banking) in liquid nitrogen (LN) as the seeds and pollens are minute, enabling the storage of many hundreds of thousands of seeds or pollens in a small vial, and as most species germinate readily, making the technique very economical. This article describes cryopreservation of orchid genetic resources by desiccation and a case study of Bletilla formosana. We hope to provide a more practical potential cryopreservation method for future research needs