An Enhanced Hidden Markov Map Matching Model for Floating Car Data.

The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running times, an inability to take full advantage of historical FCD information, and challenges in maintaining the topological adjacency and obeying traffic rules. To address these issues, an enhanced hidden Markov map matching (EHMM) model is proposed by adopting explicit topological expressions, using historical FCD information and introducing traffic rules.

[Review of dynamic global vegetation models (DGVMs)].

Dynamic global vegetation model (DGVM) is an important and efficient tool for study on the terrestrial carbon circle processes and vegetation dynamics. This paper reviewed the development history of DGVMs, introduced the basic structure of DGVMs, and the outlines of several world-widely used DGVMs, including CLM-DGVM, LPJ, IBIS and SEIB. The shortages of the description of dynamic vegetation mechanisms in the current DGVMs were proposed, including plant functional types (PFT) scheme, vegetation competition, disturbance, and phenology.