26 CHAPTER III Research Methodology In this chapter, the Author explain the research design, research setting, sample size, data collection, and scenario development. III.1 Introduction This study is designed to explore people’s interest in choosing autonomous vehicle as their daily commute. A descriptive statistics and exploratory data analysis is conducted in this study in attempt to find out the significant factors that influence people decision in choosing autonomous vehicles (AV). III.2 Questionnaire Design A key element of the survey for this study to prospective travel choice based on stated preference (SP) survey. As an alternative approach, some studies use stated preference (SP) choice experiments to estimate consumer behavioural sensitivities to new mobility technologies – sometimes using this information to develop simulations (Sweet, 2021). The decision maker in each choice situation is the individual, group, or institutions which has the responsibility to make the decision at hand. The decision maker will depend on the specific choice situation (Koppelman & Bhat, 2006) In the stage of the stated choice, respondents were confronted with eight choice tasks, which required the respondents to select one out of 6 mobility options they had specified. Individuals make a choice from a set of alternatives available to them. The set of available alternatives may be constrained by the environment (Koppelman & Bhat, 2006). In this study, there are 6 different alternatives that provided based on the government mode transport planned for IKN. The conventional vehicle (CV), electric vehicle (EV), autonomous vehicle (AV), bus rapid transit (BRT), automated rapid transit (ART), dan Mass Rapid Transit (MRT). The alternatives in a choice process are characterized by a set of attribute values. The attributes of the hypothetical alternatives were pivoted around the attribute’s levels of the preference to increase the realism of the choice task (Hess & Rose, 2009; Koppelman & Bhat, 2006). There are six attributes were provided specify the six alternatives: travel time, travel cost, parking cost, waiting time, access egress, and shared. These attributes were drawn from prior studies that found they were the most popular in describing mode choice. Civil servants from ministry office who will be moved to IKN are worked in Jakarta, therefore the attributes which are provided has been assessed in the context of Jakarta. First for travel time and travel cost 27 (Belgiawan et al., (2019), Joewono et al, (2023), Sunitiyoso et al, (2022); Ilahi, et al, (2021)). Second, some also consider waiting time (Ilahi et al, (2021); Belgiawan et al, (2019); Sunitiyoso et al, (2022)). Some also consider access egress (Ilahi et al, (2021), Joewono et al, (2023). As some prior studies of AV in Indonesia hard to find, therefore we add shared attribute in our SP experiment. Travel time was defined as the door-to-door travel time, most of the time are gathered from Google Maps application to estimate the travel time. Travel time for conventional vehicle, electric vehicle, autonomous vehicle, bus rapid transit, and automated rapid transit were based on traveling a distance from Bundaran Hotel Indonesia to Lebak Bulus Grab Station which around 14 KM. However, for Mass Rapid Transit it gathered from the electronic source which stated the time for travelled from Bundaran Hotel Indonesia to Lebak bulus Grab Station. The selected route can be travelled by the alternatives provided. The distance between Hotel Indonesia to MRT Station Lebak Bulus Grab is around 14 KM, which still also ideal distance for commuting around 20-30 minutes (Tim De Chant, 2022). Waiting time denoted the time people would spend not moving and waiting outside a vehicle. In this study, waiting time is not applied in autonomous vehicle as it can be in private modes and in some implementations, by scheduling module can specify a time at which the self- driving car should reach the pick-up location (Saini et al, 2016). Also, in Indonesia, one of fleet taxis has introduced advanced booking which can scheduled for the pick-up time (Riani, 2022). BRT and MRT waiting time are taken from the actual data, and meanwhile we added “zero” value in the attribute value as we assumed when people arrived in station their transportation have arrived at the stop. Travel costs are costs that must be incurred for one trip in this case. Travel cost for conventional vehicle, electric vehicle, and autonomous vehicle are gathered from the fuel consumption or battery consumption that divided by the distance which is 14 KM. For BRT and MRT are from actual data and hypothetical while automated rapid transit travel cost are hypothetical. Meanwhile, parking costs only apply to CV and EV because these two modes of transportation are privately owned, parking cost prices in this study came from the parking facilities in Jakarta. Next, namely for access egress, is the time needed by people to access the mode of transportation that will be used. Lastly, shared, in which, in one mode of transportation, the user itself or commuting with stranger people. Table III.1 below is an example of the table for the experiment's, attributes, alternatives, and values. The attributes of alternatives may be generic (that is, they apply to all alternatives equally). In a travel mode choice context, these variables include measures of service (travel time, frequency, reliability of service, etc) and travel cost (Koppelman & Bhat, 2006). The data 28 is fundamental for a questionnaire that has been made. Then the data is processed by NGENE software using a D-Efficient design to get the set of attributes and alternatives (Bliemer & Rose, 2009).