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- Title
Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies.
- Authors
Ningyu Zhang; Huajun Chen; Xi Chen; Jiaoyan Chen
- Abstract
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.
- Subjects
PUBLIC transit; SEMANTIC networks (Information theory); FORECASTING
- Publication
ISPRS International Journal of Geo-Information, 2016, Vol 5, Issue 10, p180
- ISSN
2220-9964
- Publication type
Article
- DOI
10.3390/ijgi5100180