This paper proposes an approach that identifies the flows of people from collected GPS data. This in turn enables us to compute significant parameters, such as people average speed, amount of travelling people, etc. A proper solution for data filtering and analysis has been implemented and tested against real data, which reduces as much as possible running time by lowering the number of needed comparisons. The ability to gain insights on people flows can have many outcomes in the area of smart transportation, e.g.The efficacy of transportation means can be assessed, then potential improvements can be suggested on public transportation means, infrastructures, etc.

Demo: Get spatio-temporal flows from GPS data

Tramontana, Emiliano;VERGA, GABRIELLA
2018-01-01

Abstract

This paper proposes an approach that identifies the flows of people from collected GPS data. This in turn enables us to compute significant parameters, such as people average speed, amount of travelling people, etc. A proper solution for data filtering and analysis has been implemented and tested against real data, which reduces as much as possible running time by lowering the number of needed comparisons. The ability to gain insights on people flows can have many outcomes in the area of smart transportation, e.g.The efficacy of transportation means can be assessed, then potential improvements can be suggested on public transportation means, infrastructures, etc.
2018
9781538647059
Big data; Data analysis; GPS data; Smart cities; Smart transportation; Artificial Intelligence; Computer Networks and Communications; Computer Science Applications1707 Computer Vision and Pattern Recognition; Safety, Risk, Reliability and Quality
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/335025
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