Electric Vehicles (EVs) currently provide a major opportunity to decarbonize urban areas and improve their quality of life, however, the mass transition towards electric mobility requires understanding and solving the potential issues that they might cause to users. In this work, we propose a process that, through a mix of mobility data analytics, efficient trip planning, and simulation heuristics, is able to analyze the current fuel-based mobility of a user and quantitatively describe the impact of switching to EVs on their mobility lifestyle. We apply our process to a large dataset of real trips, analyzing both the impact of EVs on the collectivity and on the individuals, providing a case study with insights at the level of single users.

From fossil fuel to electricity: studying the impact of EVs on the daily mobility life of users

Bonavita, Agnese
;
2023

Abstract

Electric Vehicles (EVs) currently provide a major opportunity to decarbonize urban areas and improve their quality of life, however, the mass transition towards electric mobility requires understanding and solving the potential issues that they might cause to users. In this work, we propose a process that, through a mix of mobility data analytics, efficient trip planning, and simulation heuristics, is able to analyze the current fuel-based mobility of a user and quantitatively describe the impact of switching to EVs on their mobility lifestyle. We apply our process to a large dataset of real trips, analyzing both the impact of EVs on the collectivity and on the individuals, providing a case study with insights at the level of single users.
2023
Settore INF/01 - Informatica
Electric vehicles, Individual Mobility Networks, Mobility Data Mining, Trip planning
   Big Data for Mobility Tracking Knowledge Extraction in Urban Areas
   Track and Know
   European Commission
   Horizon 2020 Framework Programme
   780754

   Energy-efficient AI-ready Data Spaces
   Green.Dat.AI
   European Commission
   Horizon Europe Framework Programme
   101070416
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/135064
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