A data mining approach for trip time prediction in mass transit companies
In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze which departments need trip time prediction and when. Secondly, we review related work and third...
| Autor principal: | |
|---|---|
| Outros Autores: | , , |
| Formato: | book |
| Idioma: | eng |
| Publicado em: |
2005
|
| Assuntos: | |
| Texto completo: | https://hdl.handle.net/10216/6750 |
| País: | Portugal |
| Oai: | oai:repositorio-aberto.up.pt:10216/6750 |
| Resumo: | In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze which departments need trip time prediction and when. Secondly, we review related work and thirdly we present the analysis of trip time over a particular path. We proceed by presenting experimental results conducted on real data with the forecasting techniques we found most adequate, and conclude by discussing guidelines for future work. |
|---|