Scipediacontent: Scipediacontent moved page Draft Content 364475435 to Mena-Yedra et al 2017a

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Scipediacontent moved page Draft Content 364475435 to Mena-Yedra et al 2017a

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Created page with " == Abstract == Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility ch..."

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== Abstract ==

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyze the situation in a commercial real-time prediction system with its current problems and limitations. We analyze issues related to the use of spatiotemporal information to reconstruct the traffic state. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic state prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network, which we call as “structure learning”. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. (Part of special issue: 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary)

Peer Reviewed

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/2117/114367 http://hdl.handle.net/2117/114367] under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

* [https://doi.org/10.1016/j.trpro.2017.12.106 https://doi.org/10.1016/j.trpro.2017.12.106] under the license cc-by-nc-nd

* [https://api.elsevier.com/content/article/PII:S2352146517310037?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S2352146517310037?httpAccept=text/xml],
: [https://api.elsevier.com/content/article/PII:S2352146517310037?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S2352146517310037?httpAccept=text/plain],
: [http://dx.doi.org/10.1016/j.trpro.2017.12.106 http://dx.doi.org/10.1016/j.trpro.2017.12.106] under the license https://www.elsevier.com/tdm/userlicense/1.0/

* [https://www.sciencedirect.com/science/article/pii/S2352146517310037 https://www.sciencedirect.com/science/article/pii/S2352146517310037],
: [https://upcommons.upc.edu/handle/2117/114367 https://upcommons.upc.edu/handle/2117/114367],
: [https://upcommons.upc.edu/bitstream/2117/114367/4/1-s2.0-S2352146517310037-main.pdf https://upcommons.upc.edu/bitstream/2117/114367/4/1-s2.0-S2352146517310037-main.pdf],
: [https://core.ac.uk/display/157810368 https://core.ac.uk/display/157810368],
: [https://academic.microsoft.com/#/detail/2782004994 https://academic.microsoft.com/#/detail/2782004994]

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Scipediacontent: Scipediacontent moved page Draft Content 364475435 to Mena-Yedra et al 2017a

Scipediacontent: Created page with " == Abstract == Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility ch..."