On Evaluating Floating Car Data Quality for Knowledge Discovery

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Cerqueira, V (Autor) Outra; Moreira Matias, L (Autor) Outra; Khiari, J (Autor) Outra; van Lint, H (Autor) Outra

Revista

Título: IEEE Transactions on Intelligent Transportation Systems

Vol. 19

Páginas: 3749-3760

ISSN: 1524-9050

Editora: IEEE

Indexação

ISI Web of Knowledge - 11 Citações

Outras Informações

ID Authenticus: P-00P-WRP

DOI: 10.1109/tits.2018.2867834

Abstract (EN): Floating car data (FCD) denotes the type of data (location, speed, and destination) produced and broadcasted periodically by running vehicles. Increasingly, intelligent transportation systems take advantage of such data for prediction purposes as input to road and transit control and to discover useful mobility patterns with applications to transport service design and planning, to name just a few applications. However, there are considerable quality issues that affect the usefulness and efficacy of FCD in these many applications. In this paper, we propose a methodology to compute such quality indicators automatically for large FCD sets. It leverages on a set of statistical indicators (named Yuki-san) covering multiple dimensions of FCD such as spatio-temporal coverage, accuracy, and reliability. As such, the Yuki-san indicators provide a quick and intuitive means to assess the potential "value" and "veracity" characteristics of the data. Experimental results with two mobility-related data mining and supervised learning tasks on the basis of two real-world FCD sources show that the Yuki-san indicators are indeed consistent with how well the applications perform using the data. With a wider variety of FCD (e.g., from navigation systems and CAN buses) becoming available, further research and validation into the dimensions covered and the efficacy of the Yuki-San indicators is needed.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12