Constructing agent data with detailed information on their sociodemographics is substantially important for agent-based modelling. However, to collect data about the whole population is not efficient, since it requires an expensive and time-consuming survey, especially for a large population. The paper uses a novel approach that integrates Bayesian network (BN) and generalized raking (GR) multilevel iterative proportional fitting (IPF). Furthermore, the approach is applied to construct the population for Greater Jakarta, Indonesia, which consists of 30 million inhabitants. The results show that the BN approach can produce data that represent the probability distribution of sample data and that the IPF can match it against aggregate census data.

This study concerns the effect of electric motorcycle attributes and policy incentives in conjunction with travellers’ residential location, activity patterns, and intrinsic motivations on the adoption of electric motorcycle in the Yogyakarta Metropolitan Area, Indonesia, employing a mixed logit model. This study revealed that the inclusion of spatiotemporal variables (particularly where people reside) and people's level of readiness to use electric motorcycles, in addition to the typical choice variables, improve the model specification. Residential locations are observed to have a higher magnitude than stage of change in explaining electric motorcycle adoption. However, convincing people of electric motorcycle’s technological and environmental advantages and its reliability should focus on more than the incentives thereof and faster speeds. As a better policy alternative to free annual tax, free battery replacements are recommended as they can reduce maintenance costs, which have recently become a bigger concern than purchasing costs.