文章基本信息

标题：User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks
本地全文：下载
作者：Fabio Luis Marques dos Santos ; Paolo Tecchio ; Fulvio Ardente 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2021
卷号：13
期号：2
页码：585
DOI：10.3390/su13020585
语种：English
出版社：MDPI, Open Access Journal
摘要：This paper presents an artificial neural network (ANN) model that simulates user’s choice of electric or internal combustion engine automotive 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 was trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric automotive 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, have the most influence on consumers’ vehicle choices. A graphical interface was 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.