Scalability of electric shared mobility for future smart cities
Parole chiave BIG DATA ANALYSIS, ELECTRIC AND HYBRID VEHICLES, ELECTRIC ENERGY CONSUMPTION PROFILES, MACHINE LEARNING, MOBILITY, SMART CITIES
Riferimenti DANILO GIORDANO, MARCO MELLIA, LUCA VASSIO
Gruppi di ricerca DATABASE AND DATA MINING GROUP - DBDM, SmartData@PoliTO
Descrizione Our research group is studying the design of green and shared mobility in future smart cities.
To envision the future, first we are studying current mobility habits. We collected real data of a free floating car sharing operator based on internal combustion engine cars that we recorded in many cities world wide. We developed a mobility simulator to study how to migrate the current car sharing system from combustion engine cars to electric cars.
The aim of this thesis is to study the smart city of the futures where mobility is based on electric and shared vehicles. Using the tools developed by our research group, the candidate will study how car sharing system may scale up to a city usage from different points of view.
This thesis lies within this project and has the goal to exploit the collected data to analyze:
- Impact of usage increase of sharing mobility
- Increase customer demand from little scale to large scale
- Evaluate the impact of demand increase on the fleet usage and size
- Evaluate the impact of the demand increase on the charging infrastructure and power grid
- Find trade-offs between quality of services and costs for the operators
- Find possible non linear correlation between demand increase and car sharing fleet size
Conoscenze richieste Good programming languages knowledge (high level languages like Python)
Knowledge on statistics (regressions, probability estimations, etc.)
Basic knowledge on Big Data platform usage
Note The thesis will be developed within SmartData@PoliTO (https://smartdata.polito.it), a research center that focus on data science and Big Data
Scadenza validita proposta 04/10/2020 PROPONI LA TUA CANDIDATURA