| Texto completo | |
| Autor(es): |
Ladeira, Lucas Z.
;
de Souza, Allan M.
;
Silva, Thiago H.
;
Pazzi, Richard W.
;
Villas, Leandro A.
;
IEEE Comp Soc
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2020 IEEE 13TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD 2020); v. N/A, p. 8-pg., 2020-01-01. |
| Resumo | |
The use of contextual data to suggest distinct types of routes helps to understand new aspects of a city that may change the perception of drivers about routes. The impact of these aspects may differ from driver to driver requiring a way to change the suggestion according to the driver's point of view. Therefore, this paper presents an approach that identifies distinct situations in multiple types of contextual data and proposes a personalized and context-aware vehicle rerouting service called PONCHE. It considers common characteristics found in every dataset of spatiotemporal data to overcome the necessity of processing specific aspects of distinct data types. Regarding personalized service, each driver's profile is reflected into contextual data type weights considered by the system, i.e., the intensity he/she wants to avoid a contextual region. With that, a driver's profile may ignore a determined contextual data type. Performance evaluation results show that PONCHE identifies the best routes according to the weights given by a driver. It also improves the quality of contextual information obtained according to traffic, crime, and vehicle crashes. This study takes into consideration contextual data from Austin and Chicago in the USA, enabling comparison with two distinct cities. (AU) | |
| Processo FAPESP: | 18/23011-1 - GoodWeb: uso de sensoriamento social para aprimorar a qualidade de vida em cidades e alavancar novos serviços |
| Beneficiário: | Thiago Henrique Silva |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |