Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies

Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies

With the growing development of smart cities, public transit forecasting has begun to attract significant attention. In this paper, we propose an approach for forecasting passenger boarding choices and public transit passenger flow. Our prediction model is based on mining common user behaviors for s...

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Bibliographic Details
Main Authors: Ningyu Zhang, Huajun Chen, Xi Chen, Jiaoyan Chen
Format: Article
Language:English
Published: MDPI AG 2016-09-01
Series:ISPRS International Journal of Geo-Information
Subjects:
geosensor networks
smart city
crowdsensing
semantics
machine learning
Online Access:http://www.mdpi.com/2220-9964/5/10/180

Internet

http://www.mdpi.com/2220-9964/5/10/180

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