A Scenario-Oriented Approach for Noise Detection on Traffic Flow Data

Autor:	Mahsa Francesco Alesiani, Luis Moreira-Matias, Mahsa Faizrahnemoon
Rok vydání:	2015
Předmět:	Engineering Bootstrapping business.industry Computation media_common.quotation_subject computer.software_genre Traffic flow Set (abstract data type) Data quality Quality (business) Data mining business computer Traffic generation model Reliability (statistics) media_common
Zdroj:	ITSC
DOI:	10.1109/itsc.2015.32
Popis:	Road transport solutions depend on the quality of the measurements of the underlying traffic state. This paper introduces quality indicators that aim at identify the presence of traffic measurement anomalies. The proposed method seeks inconsistency in the traffic measures by statistically evaluating the variability of measures. The computation of this indicator set is mainly based on bootstrapping. Each one of them was developed to address a distinct scenario. Experiments conducted using world traffic data shows promising results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::87d5660b6b785f1c1b29a76f1b939ca0 https://doi.org/10.1109/itsc.2015.32 Zobrazit plný text záznamu