Abstract
Route Restoration Method for Sparse Taxi GPS trajectory based on Bayesian Network
Highlights
With the progress and development of information & communication technologies as well as the popularization of navigation-related smart devices, it is convenient to obtain the GPS data nowadays
This paper mainly aims at the situation of high data missing rate, based on the collection of urban taxi GPS data, comprehensively considers the factors such as time, space, driver characteristics, environmental characteristics, operation characteristics, etc., and reconstructs the route track combining with the actual road network situation
In order to test the validity of Bayesian network structure model, the sample data are divided into training set and test set according to the proportion of 3:1
Summary
With the progress and development of information & communication technologies as well as the popularization of navigation-related smart devices, it is convenient to obtain the GPS data nowadays. If the car is running in a multi-intersection zone, there are lots of route options and it is a hard work to restore the actual route chosen by this driver This issue stands out more especially in urban areas, where road network is composed of short links and vehicles can travel on many different road segments in few time intervals. This kind of problem can be named "restoring the real route based on the sparse GPS trajectory". For the situation of high GPS missing rate, we cannot align a sequence of observed user positions with digital road network straightly If this problem can be solved well, the cost of data collection and storage will be reduced
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