Name
Abstract | ||
|---|---|---|
Planning efficient public transport is a key issue in modem cities. When planning a route for a bus or a line for a tram or subway, it is necessary to consider people's demand for this service. In this work we present a method to use existing crowdsourced data (like Waze and OpenStreetMap) and cloud services (like Google Maps) to support a transportation network decision making process. The method is based on the Dempster-Shafer Theory to model transportation demand. It uses data from Waze to provide a congestion probability and data from OpenStreetMap to provide information about location of facilities such as shops, in order to predict where people may need to start or end their trips using public transportation vehicles. The paper also presents an example using this method with real data. The example shows an analysis of the current availability of public transportation stops in order to discover its weak points. |
Year | Venue | Keywords |
|---|---|---|
2019 | JOURNAL OF UNIVERSAL COMPUTER SCIENCE | Dempster-Shafer theory,transportation networks,smart cities,Origin-Destination planning problem |
Field | DocType | Volume |
Computer science,Transport engineering,Knowledge management,Public transport,Smart city | Journal | 25 |
Issue | ISSN | Citations |
8 | 0948-695X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Frez | 1 | 16 | 4.46 |
| Nelson Baloian | 2 | 271 | 46.73 |
| José A. Pino | 3 | 3 | 0.72 |
| Gustavo Zurita | 4 | 572 | 65.96 |
| Franco Basso | 5 | 4 | 2.10 |