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.
Highlights
In recent years, geosensor networks and the sensor web have rapidly expanded in smart cities
We annotate the data with semantic information
We present two case studies: (1) Forecasting the boarding choices of passengers, predicting whether a passenger will or will not take the bus; (2) Forecasting public transit passenger flow, predicting how many passengers will take the bus
Summary
Geosensor networks and the sensor web have rapidly expanded in smart cities. Geosensors, such as card and bus GPS terminals, produce massive datastreams every day. Commuters usually have to deal with crowded buses or subways in order to get to work, which is inconvenient and unpleasant. It can be difficult for both private sector and government transit providers to arrange reasonable routes and predict the potential future flow of passengers. The ability to forecast public transit needs is beneficial
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