Automated Setting of Bus Schedule Coverage Using Unsupervised Machine Learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Khiari, J (Autor) Outra; Matias, LM (Autor) Outra; Cerqueira, V (Autor) Outra; Cats, O (Autor) Outra

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 552-564

20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016

19 April 2016 through 22 April 2016

Indexação

Scopus - 16 Citações

Outras Informações

ID Authenticus: P-00N-KHT

DOI: 10.1007/978-3-319-31753-3_44

Abstract (EN): The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale. © Springer International Publishing Switzerland 2016.

Idioma: Inglês

Tipo (Avaliação Docente): Científica