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Project: PTDC/ECM-TRA/6803/2014 - POCI-01-0145-FEDER-016848

Project name: UrbySense - Análise e previsão de mobilidade urbana fora da rotina com base em pegadas digitais
Project code: PTDC/ECM-TRA/6803/2014 - POCI-01-0145-FEDER-016848
Main Objective: Reforçar a investigação, o desenvolvimento tecnológico e a inovação
Intervention Region: Norte, Centro
Proposing institution/Lead promoter/Coordinating entity: Universidade de Coimbra
Partner(s)/Co-promoter(s)/Participating institution(s): Faculdade de Engenharia da Universidade do Porto
Date of approval: 2018-04-08
Start date: 2016-06-01
Completion date: 2018-08-31
Eligible Cost of the Project
Total Eligible Cost: 176.000,00 EUR
Eligible Cost in the University of Porto: 41.780,00 EUR
Faculdade de Engenharia da Universidade do Porto: 41.780,00 EUR
Total Financial Support
União Europeia - FEDER: 149.600,00 EUR
Orçamento de Estado: 26.400,00 EUR
Financial Support to the University of Porto
Total of the University of Porto: 41.780,00 EUR
União Europeia | União Europeia - FEDER | Faculdade de Engenharia da Universidade do Porto: 35.513,00 EUR
Nacional/Regional | Orçamento de Estado | Faculdade de Engenharia da Universidade do Porto: 6.267,00 EUR
Objectives, activities and expected/achieved results
Objetivos e Atividades In this project we propose to study individual's mobility for mining non-routine (leisure, social, etc.) mobility patterns from multiple
data sources. The following patterns are of great interest: locations of significance, modes of transport, trajectory patterns and location-based activities for destination choice modelling. Data collected via ubiquitous devices and smart metering combined with data from social media platforms provides a range of new close-to-real-time information that can be combined with the data from more traditional sources (surveys, transport system records and static data) for urban efficient mobility plan an management. When considered in isolation, each of these data sources has gaps/missing observations, so the matching of multiple data sources can facilitate transport analysis, and enable operators to better tune public transportation within cities with the aim of travelling at lower costs, faster and producing a smaller carbon footprint.
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