User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks

This paper presents an Artificial Neural Network (ANN) model that simulates user’s choice of electric or internal combustion engine vehicles based on basic vehicle attributes (purchase price, range, operating cost, taxes due to emissions, time to refuel/recharge and vehicle price depreciation), with the objective of analyzing user behavior and creating a model that can be used to support policymaking. The ANN is trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric vehicle attributes. Model results show that the electric vehicle parameters (especially purchase cost, range and recharge times), as well as the purchase cost of internal combustion engine vehicles are the most influencing on consumer’s vehicle choice. A graphical interface is created for the model, to make it easier to understand the interactions between different attributes and their impacts on consumer choices and thus help policy decisions.

MARQUES DOS SANTOS Fabio;  TECCHIO Paolo;  ARDENTE Fulvio;  PEKAR Ferenc; 

2021-01-26

MDPI

JRC122798

2071-1050 (online),   

https://www.mdpi.com/2071-1050/13/2/585,    https://publications.jrc.ec.europa.eu/repository/handle/JRC122798,   

10.3390/su13020585 (online),