Predicting MRT Trips in Singapore by Creating a Mobility Behavior Model Based on GSM Data
| Autor: | Emin Aksehirli, Ying Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Service (systems architecture)
business.industry Computer science ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Telecommunications network Transport engineering Market research GSM 020204 information systems Public transport 0202 electrical engineering electronic engineering information engineering TRIPS architecture 020201 artificial intelligence & image processing GSM services Predictability business |
| Zdroj: | ICDM Workshops |
| DOI: | 10.1109/icdmw.2018.00098 |
| Popis: | Singapore has a significantly high coverage of both public transportation and communication network. Island-wide Mass Rapid Transport (MRT) system is the backbone of public transport, any improvement or disruption to its service can create a considerable impact to business productivity and people's lives. In this paper, we discuss our implementation of a system that uses telecommunication (Telco) data to predict trips in Singapore's MRT network in real-time. First, we study the predictive capabilities of available data sources and decide on the specific data to use for this work. We then investigate the predictability of MRT riders' mobility behavior based on what can be observed from our data source. Next, analyze the applicability of the features along with what is feasible to predict. Finally, the predictive capabilities of our supervised methods and their ensembles are evaluated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|