| Title |
Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies |
| ID_Doc |
43714 |
| Authors |
Zhang, NY; Chen, HJ; Chen, X; Chen, JY |
| Title |
Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies |
| Year |
2016 |
| Published |
Isprs International Journal Of Geo-Information, 5, 10 |
| DOI |
10.3390/ijgi5100180 |
| 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. |
| Author Keywords |
geosensor networks; smart city; crowdsensing; semantics; machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000387885900015 |
| WoS Category |
Computer Science, Information Systems; Geography, Physical; Remote Sensing |
| Research Area |
Computer Science; Physical Geography; Remote Sensing |
| PDF |
https://www.mdpi.com/2220-9964/5/10/180/pdf?version=1475228629
|