Latest revision as of 20:50, 3 February 2021

Abstract

With the development of big data and artificial intelligence, the technology of urban computing becomes more mature and widely used. In urban computing, using GPS-based trajectory data to discover urban dense areas, extract similar urban trajectories, predict urban traffic, and solve traffic congestion problems are all important issues. This paper presents a GPS-based trajectory pattern mining system called TPM. Firstly, the TPM can mine urban dense areas via clustering the spatial-temporal data, and automatically generate trajectories after the timing trajectory identification. Mainly, we propose a method for trajectory similarity matching, and similar trajectories can be extracted via the trajectory similarity matching in this system. The TPM can be applied to the trajectory system equipped with the GPS device, such as the vehicle trajectory, the bicycle trajectory, the electronic bracelet trajectory, etc., to provide services for traffic navigation and journey recommendation. Meantime, the system can provide support in the decision for urban resource allocation, urban functional region identification, traffic congestion and so on.

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1907.02678

• http://arxiv.org/pdf/1907.02678

• http://xplorestaging.ieee.org/ielx7/8952729/8962976/08963296.pdf?arnumber=8963296,

http://dx.doi.org/10.1109/besc48373.2019.8963296

• https://dblp.uni-trier.de/db/journals/corr/corr1907.html#abs-1907-02678,

https://arxiv-export-lb.library.cornell.edu/abs/1907.02678,

https://academic.microsoft.com/#/detail/3001431193