Bangkok suffered from the world’s worst traffic congestion in the 1990s due to rapidly increasing car ownership, reflecting the economic growth and road-dependent transport policy beginning in the 1960s. Due to its monocentric but scattered urban structure, traffic congestion is severe, causing tremendous economic loss, deteriorating air quality, and badly affecting the quality of life. A historical review reveals that the urban and transport plan and development were not efficiently coordinated, resulting in unorganized suburbanization and progressively more severe traffic congestion. It is important to reveal the impact of the transportation project on the housing market in order to incorporate the policies for transportation and urban development. To define the impact, the OLS hedonic price model and the local multiscale geographically weighted regression (MGWR) model were estimated, along with the condominium sales data. The results revealed that the impact of rail transit on a rise in property value significantly varied across the study area. It was estimated that, for the area along the major rail transit corridor in the city center, a premium of a location 100-m closer to the station would be more than 200 USD per square meter. At the same time, the value would be less than 80 USD for the area along the rail corridor in the suburb. These findings provide policy insights for future urban and railway development, including the proper coordination of rail transit development and urban development with subcenters, transit-oriented development, and improved pedestrian flow around transit stations.

Understanding the quality of life related to transportation plays a crucial role in enhancing commuters’ quality of life, particularly in daily trips. This study explores the spatial effects of built environment on quality of life related to transportation (QoLT) through the combination of GIS application and deep learning based on a questionnaire survey by focusing on a case study in Sukhumvit district, Bangkok, Thailand. The Geographic Information System (GIS) was applied for spatial analysis and visualization among all variables through a grid cell (500 × 500 sq.m.). In regard to deep learning, the semantic segmentation process that the model used in this research was OCRNet, and the selected backbone was HRNet_W48. A quality-of-life-related transportation indicator (life satisfaction) was implemented through 500 face-to-face interviews and the data were collected by a questionnaire survey. Then, multinomial regression analysis was performed to demonstrate the significant in positive and negative aspects of independent variables (built environment) with QoLT variables at a 0.05 level of statistical significance. The results revealed the individuals’ satisfaction from a diverse group of people in distinct areas or environments who consequently perceived QoLT differently. Built environmental factors were gathered by application of GIS and deep learning, which provided a number of data sets to describe the clusters of physical scene characteristics related to QoLT. The perception of commuters could be translated to different clusters of the physical attributes through the indicated satisfaction level of QoLT. The findings are consistent with the physical characteristics of each typological site context, allowing for an understanding of differences in accessibility to transport systems, including safety and cost of transport. In conclusion, these findings highlight essential aspects of urban planning and transport systems that must consider discrepancies of physical characteristics in terms of social and economic needs from a holistic viewpoint. A better understanding of QoLT adds important value for transportation development to balance the social, economic, and environmental levels toward sustainable futures.

Many megacities in the world, especially Bangkok, are facing severe congestion in road traffic and public transport, particularly during peak hours. This situation (a) worsens the quality of life, (b) releases emissions causing air pollution and climate change, and (c) subsequently creates requests for massive investment in transport infrastructure, which easily exceed the budget’s limit. Instead of solving the problem by supply-side strategies, applying ICT-based solutions to reform people’s daily activities, particularly commuting and working behaviors, on the demand-side is an alternative solution. As a promising solution, Mobility as a Service (MaaS) introduces ICT to persuade people to use public modes of transport. However, modal shift is a partial solution to mitigate traffic congestion. With technological advancements in communication, people become more flexible in their activities in terms of place and time aspects. MaaS should not only induce a transport mode shift but also a workplace shift and working time shift by extending the planning service in daily activity-travel level, exploiting people’s flexibilities for an activity’s place and time to manage travel demand. This paper proposes the QOL-MaaS as the extended MaaS to support space–time shift of activity-travel and reveals its potential impacts on traffic congestion, quality of life, and CO₂ emission.

Rail transit systems around the world have been suffering from heavily reduced ridership due to reduced capacity for social distancing and passengers’ concern over the risk of COVID-19 infection. Various countermeasures were implemented to reduce the COVID-19 risk so that passengers felt safe to travel on rail. The objectives of this study were to evaluate COVID-19 countermeasures of Bangkok’s urban rail from passengers’ viewpoints and examine its influence on passenger’s confidence. The background of the COVID-19 pandemic in Thailand and the rail countermeasures implemented in Bangkok were summarized. The data were obtained from an interview survey of 1105 railway passengers conducted at the stations during the second wave of the pandemic. Factor analyses and structural equation modeling were conducted. The results revealed that social distancing was not satisfied by the passengers but adversely caused inconvenience and increased infection risk when the station or rail were congested. On the other hand, the passenger temperature check, face mask enforcement, and hand sanitization countermeasures were found to highly and positively contribute to passengers’ confidence. Contact tracing application was also found to raise awareness and confidence. The findings provided insights for rail authorities and related agencies to effectively implement the countermeasures that would be practically and financially sustainable.

Roadway area calculation is a novel problem in remote sensing and urban planning. This paper models this problem as a two-step problem, roadway extraction, and area calculation. Roadway extraction from satellite images is a problem that has been tackled many times before. This paper proposes a method using pixel resolution to calculate the area of the roads covered in satellite images. The proposed approach uses novel U-net and Resnet architectures called U-net++ and ResNeXt. The state-of-the-art model is combined with the proposed efficient post-processing approach to improve the overlap with ground truth labels. The performance of the proposed road extraction algorithm is evaluated on the Massachusetts dataset and it is shown that the proposed approach outperforms the existing solutions which use models from the U-net family.

As the COVID-19 pandemic spreads globally, and its vaccine or drug is yet to be developed, many countries worldwide now resort to social distancing policies as effective non-pharmaceutical interventions. However, its effectiveness varies from country to country, and the factors responsible for such variations are not yet known. In this study, preliminary analysis of the international comparison of behavior changes that accompany social distancing policies is conducted using Google Community Mobility Reports, wherein ratios of behavior changes with social distance policy in 131 countries around the world are published. When these changes are analyzed using the generalized linear mixed model, it shows that the status of the infection spread and policy implementation uniformly influence behavior restrictions across every country. Furthermore, it suggests that the differences in the key contents of the social distancing policies and the socioeconomic status of each country may influence the ratio of behavior changes.