Building Information Modeling (BIM) is a technological tool used in the sustainable construction process, beginning with the planning phase and continuing through the construction and post-construction phases. BIM has excellent capabilities and is being adopted by construction service providers in the process of implementing sustainable construction. However, many barriers remain in the adoption process, particularly with regard to the intention to use it. The purpose of this study was to determine the intention to implement BIM based on the variables of benefits and barriers. In order to parse the phenomena that occur in research, this study employed a quantitative method. Survey data in the form of a questionnaire were collected from the Special Region of Yogyakarta using a purposive sampling method for this study. Questionnaires were distributed via Google Form to respondents from the construction sector (Civil & Architectural Engineering) and BIM application users (students, workers). The findings of this study have provided an overview of the challenges and benefits of implementing BIM in sustainable construction. The barriers presented are financial, technological, and policy barriers, depending on the company's classification, while the benefits presented are environmental benefits and innovation.

Spatio-temporal data modelling is one of the methods in data analysis that uses space (spatial) and time (temporal) approaches. This study used Spatio-temporal statistical modelling to observe the daily activity patterns of people. Spatio-temporal modelling selected for support activity-based transportation demand. This research identifies community mobility patterns that will provide trip production data for transportation demand prediction. Using Spatio-temporal statistical modelling benefit this study because statistical this model can make model components in a physical system appearing to be random. Even if they are not, the models are helpful as they have accurate and precise predictions. In this study, descriptive analysis was used. Incorporating statistical distributions into the model is a natural way to solve the problem. This research collects daily activity data from 400 respondents recorded every 15 minutes. From this data, a pattern of respondents’ daily activities was formed, which was analyzed using R. Software R also visualizes data on daily activities of the community in Spatio-temporal modelling. This research aims to depict the daily activity patterns to predict trip production. This research found three clusters of trip production patterns with specific groups member and specific patterns between workdays and holidays.

Travel expenses are a significant factor in transportation planning. In addition to the other aspect, travel time, the community considers expense as the necessary element in deciding which mode the communities should take. However, there is a gap between the actual transport expense and the commuter's perception. Thus, comprehensive knowledge is urgently needed particularly to be seen as a major variable in transportation planning that sided with underprivileged groups of transport poverty. The study focused on describing the correlation between income and commuting transportation expenses. The analysis was carried out using two methods. The first method is a descriptive analysis used to provide insight into the patterns and characteristics of the data obtained from interviews with 421 respondents. The second method is regression analysis (linear and nonlinear) to explain the relation pattern between the dependent (commuting transportation expenses) and independent (income) variables. The study's findings demonstrate that transportation expenses follow a negative polynomial regression pattern on income, further implying that the percentage of transportation expenses in low-income communities is significantly higher than those in high-income communities.