Transit-oriented development (TOD) is based around transit stations, with the emphasis on high population density and multifunctional areas in promoting sustainable mobility. This study aimed to develop a TOD model that could achieve an optimum land use allocation to maximize transit ridership. A critical literature review, an analysis of value engineering through function and benchmarking studies were conducted in order to develop a baseline plan for a TOD model, which was then optimized using linear programming. A total of four light rail transit (LRT) stations located in Jakarta were used as the case study to represent model implementation, ridership evaluation and optimal design. The optimization results showed that office workers constituted the highest number of transit passengers, followed by those working in hotels and commercial/retail and residential users. It was also found that optimizing the design of the TOD can increase the number of daily LRT passengers by up to 55%.

Over the years, dry port studies experience a growing interest from academics and researchers focusing on various contexts within a seaport studies and involving a range of methods for evaluation and adoption. Some scholars have attempted to evaluate research in dry port during the past decade, but research involving structured analysis and extensive available literature are still very limited. Bibliometric analysis was conducted to generate 470 articles, and qualitative analysis was performed to produce 268 papers focusing on dry port studies from 1999 to 2019. The result categorize dry port studies into six thematic areas including (1) port policy and governance, (2) port management & strategies, (3) competition & performance, (4) planning & development, (5) dry port operation, and (6) spatial analysis. The finding of this study reveals that scholars are interested in competition, port performance, and dry port planning and development. On the other hand, the themes related to spatial analysis and dry port operation are found to remain limited. Moreover, sustainability issue has emerged as one of the potential topics that might highly contribute to the direction of future dry port studies.

Urban transport investments have contributed to the exponential increase of value from land and properties around the built infrastructure. Although literature had shown evidence of value uplift from the residential property due to transit development, little is known about the impact on commercial property values. This paper aims to evaluate the impact of rail transit proximity to the commercial property market by taking into account the pre-operation of the LRT project in Jakarta, Indonesia. This study utilizes a big data approach to accelerate research data processing by employing data mining method, as well as geographical information system (GIS) and hedonic price modeling (HPM) to investigate the property prices to formulate empirical evidence for the research. The results show opposite evidence compared to previous studies which argued that accessibility may contribute to the property price when closer to the transit station. The findings indicate that proximity to rail transit has an insignificant impact on the commercial property price compared to other variables such as building size, number of rooms, location, and hospitals. A comparison of the findings between this research with other studies was discussed and recommendation for further research development was proposed.

The increased energy consumption in office building led to stakeholders’ renewed interests in optimizing office buildings’ energy performances, such as indoor comfort, energy efficiency, and life cycle costs. One of the most effective solutions to hedging against building operation optimization is incorporating energy efficiency measures and architectural programming early. This paper proposes a Smart Integrated Workspace Design (SIWD) framework that integrates architectural programming system using Internet of Things (IoT) technology into the Value Engineering (VE) process to assess design alternatives targeted explicitly at future smart office buildings. The use of the SIWD led to an improved development cycle linking architectural plans and engineering design. An empirical test using a large building owner, their designers, and contractors in Jakarta, Indonesia, resulted in an 18% improvement in building energy performance and quality when the design team used the SIWD framework. It is also indicated that leveraging advanced technology and incorporating familiar concepts and processes increased user willingness to adopt the SIWD in future projects. So, incorporated the IoT in architectural design and evaluated by VE made the readiness and friendly technology to all occupants to get the building efficiency and comfort unsteady.

Smart building is a building development approach utilizing digital and communication technology to improve occupants' comfort inside the building and help increase energy usage efficiency in building operations. Despite its benefits, the smart building concept is still slowly adopted, particularly in developing countries. The advancement of computational techniques such as machine learning (ML) has helped building owners simulate and optimize various building performances in the building design process more accurately. Therefore, this study aims to assist energy efficiency design strategies in a building by identifying the features of the smart building characteristics that can potentially foster building energy efficiency. Furthermore, an ML model based on the features identified is then developed to predict the level of energy use. K-Nearest Neighbor (k-NN) algorithm is employed to develop the model with the openly accessible smart building energy usage datasets from Chulalongkorn University Building Energy Management System (CU-BEMS) as the training and testing datasets. The validation result shows that the predictive model has an average relative error value of 17.76%. The energy efficiency levels obtained from applying identified features range from 34.5% to 45.3%, depending on the reviewed floor. This paper also proposed the dashboard interface design for ML-based smart building energy management.