dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorFed Inst Sao Paulo
dc.contributorUniversidade de São Paulo (USP)
dc.date.accessioned2021-06-25T11:54:54Z
dc.date.accessioned2022-12-19T22:50:54Z
dc.date.available2021-06-25T11:54:54Z
dc.date.available2022-12-19T22:50:54Z
dc.date.created2021-06-25T11:54:54Z
dc.date.issued2020-01-01
dc.identifier16th Annual International Conference On Distributed Computing In Sensor Systems (dcoss 2020). Los Alamitos: Ieee Computer Soc, p. 189-196, 2020.
dc.identifier2325-2936
dc.identifierhttp://hdl.handle.net/11449/209276
dc.identifier10.1109/DCOSS49796.2020.00040
dc.identifierWOS:000631687000026
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5389873
dc.description.abstractDue to technological advances, millions of data are continuously generated during the journeys made by people daily. This data can be essential to present decision making and thus contribute to minimizing the effects of traffic congestion on people's lives. It is vital to store that data in an organized manner so that it is processed and used efficiently. In this project, we present TRUDE, a tool for generating a dataset with data collected during users' urban mobility, and through notifications of interventions. The tool was developed in modules that collect data on two fronts: by a web application, where users manage the data entry related to the programmed interventions for the city of Catanduva/SP, and by a mobile app that collects the user's routes during their journeys, through their mobile devices. The results presented demonstrate that the mobile application tool consumes little memory, battery, and data of the devices during its execution. Also, due the dataset is possible showing drivers the interventions that are generated on their routes, presenting in advance another alternative to improve the flow of vehicles in the city.
dc.languageeng
dc.publisherIeee Computer Soc
dc.relation16th Annual International Conference On Distributed Computing In Sensor Systems (dcoss 2020)
dc.sourceWeb of Science
dc.subjectdataset
dc.subjecturban mobility
dc.subjectintervention
dc.subjectmobile application
dc.subjectweb application
dc.titleTRUDE - Tools for Urban Mobility Dataset Generation
dc.typeActas de congresos


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