Understanding daily mobility patterns in urban road networks using traffic flow analytics
| Autor: | Ibai Laña, Ignacio Olabarrieta, Javier Del Ser |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
050210 logistics & transportation
Computer science business.industry 05 social sciences Big data Context (language use) 02 engineering and technology Vehicle Information and Communication System Traffic flow computer.software_genre Data modeling Transport engineering Traffic engineering Analytics 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Cluster analysis business computer |
| Zdroj: | NOMS |
| DOI: | 10.1109/noms.2016.7502980 |
| Popis: | The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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