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

Computer Science and Technology Institute, Zhejiang University, 38 Zheda Road, Hangzhou 310058, China
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Academic Editors: Silvia Nittel and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(10), 180; https://doi.org/10.3390/ijgi5100180
Received: 30 July 2016 / Revised: 24 September 2016 / Accepted: 28 September 2016 / Published: 30 September 2016
(This article belongs to the Special Issue Geosensor Networks and Sensor Web)
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 semantic trajectories and enriching features using knowledge from geographic and weather data. All the experimental data comes from the Ridge Nantong Limited bus company and Alibaba platform which is also open to the public. We evaluate our approach using various data sources, including point of interest (POI), weather condition, and public bus information in Guangzhou to demonstrate its effectiveness. Experimental results show that our proposal performs better than baselines in the prediction of passenger boarding choices and public transit passenger flow. View Full-Text
Keywords: geosensor networks; smart city; crowdsensing; semantics; machine learning geosensor networks; smart city; crowdsensing; semantics; machine learning
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Zhang, N.; Chen, H.; Chen, X.; Chen, J. Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies. ISPRS Int. J. Geo-Inf. 2016, 5, 180.

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