Open AccessComparing fuel consumption based on normalised driving behaviour: a case study on major cities in China
Author(s) -
Shi Bin,
Xu Li,
Jiang Hong,
Meng Wuqiang
Publication year - 2017
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2016.0065
Subject(s) - beijing , fuel efficiency , driving cycle , china , consumption (sociology) , artificial neural network , automotive engineering , index (typography) , driving factors , transport engineering , task (project management) , computer science , simulation , engineering , artificial intelligence , electric vehicle , geography , social science , power (physics) , physics , archaeology , quantum mechanics , sociology , world wide web , systems engineering
Fuel economy is closely related to driving style, traffic situation, and urban geomorphic environment. In this study, the authors propose to compare fuel consumption based on normalised driving behaviour. A personalised driver model is established for each driver by using the locally designed neural network and the real‐world vehicle test data. Driving behaviour is normalised by employing the personalised model to conduct the speed‐following task as defined by standard driving cycle test. Based on the normalised driving behaviour, an aggressiveness index is used to quantitatively evaluate the driving style, and a fuel index is proposed to estimate the fuel consumption. A case study is conducted on the fuel consumption comparison in four major cities in China, namely, Beijing, Shanghai, Chongqing, and Nanjing. Computational results verify the effectiveness of the proposed scheme.