Paper Type
Complete Research Paper
Description
Electric mobility is considered to be an essential building block of a sustainable transportation paradigm. Particularly for the urban metropolis of the 21st century electric vehicles embody a promise of decreased air pollution and increased quality of living. However, city planners need to muster enormous investments into urban charging infrastructure in advance. We develop a decision support system to help city planners use these funds more efficiently and place charging infrastructure where it is truly needed. We construct a regression model to determine factors that influnce the utilization of charge points in Amsterdam, one of the most "electrified" cities in the world. We include data on more than 50,000 points of interest, for instance shopping malls and museums, as well as charging data from 273 charging points with 427 individual outlets in the city of Amsterdam. From these influnces we derive a decision support system that places charge points where they are expected to experience the highest utilization.
SMART CITY PLANNING - DEVELOPING AN URBAN CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES
Electric mobility is considered to be an essential building block of a sustainable transportation paradigm. Particularly for the urban metropolis of the 21st century electric vehicles embody a promise of decreased air pollution and increased quality of living. However, city planners need to muster enormous investments into urban charging infrastructure in advance. We develop a decision support system to help city planners use these funds more efficiently and place charging infrastructure where it is truly needed. We construct a regression model to determine factors that influnce the utilization of charge points in Amsterdam, one of the most "electrified" cities in the world. We include data on more than 50,000 points of interest, for instance shopping malls and museums, as well as charging data from 273 charging points with 427 individual outlets in the city of Amsterdam. From these influnces we derive a decision support system that places charge points where they are expected to experience the highest utilization.