Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons

التفاصيل البيبلوغرافية
العنوان:	Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons
المؤلفون:	Riccardo Martoglia, Gabriele Savoia
المصدر:	EAI Endorsed Transactions on Energy Web, Vol 9, Iss 39 (2022)
بيانات النشر:	European Alliance for Innovation (EAI), 2022.
سنة النشر:	2022
المجموعة:	LCC:Science LCC:Mathematics LCC:Electronic computers. Computer science
مصطلحات موضوعية:	Big Data Analytics, Time Series, Traffic Forecast, Time Aggregation, ARIMA, Apache Spark, Science, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
الوصف:	Due to its usefulness in various social contexts, from Intelligent Transportation Systems (ITSs) to the reduction of urban pollution, road traffic prediction represents an active research area in the scientific community, with strong potential impact on citizens’ well-being. Already considered a non-trivial problem, in many real applications an additional level of complexity is given by the large amount of data requiring Big Data domain technologies. In this paper, we present the first steps of a novel approach integrating both classic and machine learning models in the Spark-based big data architecture of the H2020 CLASS project, and we perform preliminary tests to see how usually little-considered variables (different data aggregation levels, time horizons and traffic density levels) influence the error of the different models.
نوع الوثيقة:	article
وصف الملف:	electronic resource
اللغة:	English
تدمد:	2032-944X
Relation:	https://publications.eai.eu/index.php/ew/article/view/1187; https://doaj.org/toc/2032-944X
DOI:	10.4108/ew.v9i39.1187
URL الوصول:	https://doaj.org/article/7ff3ecd8ee5648669e0f2677b8e31439
رقم الانضمام:	edsdoj.7ff3ecd8ee5648669e0f2677b8e31439
قاعدة البيانات:	Directory of Open Access Journals

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الوصف
تدمد:	2032944X
DOI:	10.4108/ew.v9i39.1187