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Acceptance of Electric Vehicle in Indonesia: Case Study in Bandung
Prasetio E.A.a, Fajarindra Belgiawan P.a, Anggarini L.T.a, Novizayanti D.a, Nurfatiasari S.a
a National Center for Sustainable Transportation Technology, School of Business and Management Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.This research aims to comprehend how public electric vehicle is preferred among other transportation modes namely shuttle bus, public bus, private motorcycle, and private car, specifically for long-range (approximately more than 20 km) daily commuting. Data collection process is conducted using questionnaire-based survey that is divided into three sections: Stated-Preferences (SP), Sociodemographics (SD) characteristics, and statement evaluations (SE). SP includes eight sets of selected labelled experiments with several attributes: Travel time, travel cost, waiting time, access and egress time, access and egress cost, frequency, congestion time, and parking cost. Information on age, gender, and income are compiled in the SD section. The experimental design is developed using NGENE with a D-efficient design. We manage to gather 333 respondents and each of them corresponds to the 8 scenarios presented. Thus, a total of 2664 observations are acquired for further analysis in the light of travel mode choice behavior. An open source Python package, Biogeme, is used for the choice modeling analysis. Biogeme is designed for the maximum likelihood estimation of parametric models in general, with a special emphasis on discrete choice models. In this study, multinomial logit (MNL) modeling techniques is used as it is common in transportation research. There are 39 parameters (K= 39) used in the study comprised of four alternative specific constant (ASC): ASC2 for shuttle (SH), ASC3 for public bus (PB), ASC4 for private motorcycle (PM), and ASC5 for private car (PC); eight coefficients (beta) for each PB, PEV, and SH; five coefficients for each PM and PC; and a generic coefficient of travel cost. The result indicates that Indonesian commuters are mainly sensitive to travel time and congestion time when choosing transportation mode. It seems that emission, vibration, and noise levels are more concerning to public transport commuters than private transport commuters. Furthermore, it seems that commuters do not consider emission as very important. However, public electric bus is more preferred to public bus with the same parameters.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Choice model,Data collection process,Discrete choice models,Modeling technique,Public electric vehicle,Transportation mode,Transportation research,Travel mode choices[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]choice model,Electric vehicles,SP survey,transportation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This paper was supported by USAID through Sustainable Higher Education Research Alliances (SHERA) Program – Centre for Collaborative (CCR) National Center for Sustainable Transportation Technology (NCSTT).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT48285.2019.8994010[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]