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.

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.

The often-discussed issue of parental escorting of children to school and its potential disruption of traffic flow has been extensively examined in the literature. Still, the specific effects of traffic congestion remain understudied. To fill this gap, this study addresses the impacts of school trips on traffic congestion. An agent-based model is applied to simulate various scenarios and assess their impact. Our findings indicate that the traffic speed without school trips is higher speed during peak hours by around 8% and average travel time in the city is reduced significantly. We examine countermeasures: Firstly, flexible working hours can lead to a morning traffic speed improvement of approximately 1.9%. Secondly, staggered school schedules can result in a 6.9% increase in traffic speed at 7 a.m. Optimizing school loading spaces can also enhance road capacity, mitigating road lane blockages during drop-off and pick-up periods. This research sheds light on the impact of parent-escorting travel existence and offers potential solutions to alleviate traffic congestion.

Among the benefits of high-speed rails (HSRs) discussed from various aspects, indirect benefits may contribute to medium- and long-term economic impacts such as an increase in service supply and gross regional product (GRP). In order to estimate the economic impacts, we modeled I/O–spatial interaction by combining the inter–industrial transactions shown on the I/O table with the geospatial distance decay of economic mass through passenger transportation. In addition, the regional economic impacts, as a part of the indirect benefits, were evaluated by the model applied to the Mumbai–Ahmedabad High-Speed Rail (MAHSR) corridor in India, which is an emerging country with remarkable economic growth. The results showed the economic impacts on each zone and each industry along the MAHSR corridor as a relative distribution. The unique feature of this approach is that it is possible to evaluate the geographic distributions and interregional disparity of economic impacts by combining the industrial I/O relationships with the changes in passenger accessibility associated with a large-scale transportation project such as an HSR. Moreover, this method can be applied to various countries and regions where detailed I/O statistical data, such as interregional I/O tables, are difficult to obtain, as well as various transportation project evaluations taking into account interregional equity.

Among the various effects of high-speed rail (HSR), a direct benefit to users has been measured as an increase in the gross domestic product (GDP) by the conventional cost–benefit analysis (CBA), which was institutionalized in the U.K. In recent years, the importance of capturing indirect benefits to non-users has also been broadly discussed. The indirect benefits of HSR can mainly be classified into two perspectives: regional economy and personal quality of life (QOL). In this study, we modeled the mutual enhancement between those effects and analyzed the indirect benefits of the Mumbai–Ahmedabad high-speed rail (MAHSR), which is currently under construction as the first HSR in India, an emerging country with a rapidly growing economy. The indirect benefit to the regional economy along the MAHSR corridor was estimated by industry and by zone. Additionally, the indirect benefit on personal QOL by individuals’ attributes and by zone through the mutual enhancement with the regional economy was also estimated. The personal QOL tends to show greater effects in the middle cities than those in the two metropolitan areas of Mumbai and Ahmedabad. This method can evaluate interregional disparity by identifying the differences in benefits by person, according to age, income, etc., which cannot be evaluated based on a mass measure, e.g., GDP, in the conventional CBA.