Search Results (4)

This study evaluated the effects of COVID-19 on attitudes toward residential associated with travel behavior on decisions regarding future relocation. Chi-square automatic interaction detection was used to generate tree and classification segments to investigate the various segmentations of travelers and residents around mass transit stations. The decision tree revealed that the most influential variables were the number of transport card ownerships, walking distance to the nearest mass station, number of households, type of resident, property ownership, travel cost, and trip frequency. During the COVID-19 pandemic, people have concentrated on reducing travel time, reducing the number of transfers, and decreasing unnecessary trips. Consequently, people who live near mass transit stations less than 400 and 400–1000 m away prefer to live in residential and rural areas in the future. Structural Equation Modeling was used to confirm the relationship between attitudes in normal and pandemic situations. According to the findings, attitudes toward residential accessibility of travel modes were a significant determinant of attitudes toward residential location areas. This research demonstrates travelers’ and residents’ uncertain decision-making regarding relocation, allowing policymakers and transport authorities to better understand their behavior to improve transportation services. Full article

The determinants of residential location choice have not been investigated in many developing countries. This paper examines this topic, including the influence of urban travels on house location decision-making in the Middle East and North Africa (MENA). Based on 8284 face-to-face interviews in Istanbul, Tehran, and Cairo, the dummy variable of residential location choice, including two categories of mobility reasons and other factors, was modeled by binary probit regression modeling. By means of receiver-operating characteristic analysis, the cutoff value of commuting distance and the time passed from the last relocation was estimated. Finally, the significant difference between the value of these two variables for people with different house location reasons were tested by Mann–Whitney U-test. The results show that the eight variables of shopping-entertainment mode choice in faraway places, frequency of public transit trips, neighborhood attractiveness perception, age, number of driving licenses in household, commuting distance, number of accessed facilities, and the (walkable) accessibility of facilities influence the residential self-selections. People who chose their current home based on mobility commute a daily mean distance of 8596 m and relocated less than 15.5 years ago, while those who chose their home based on other reasons, such as socioeconomics or personal reasons, commute longer and moved to a new house more than 15.5 years ago. This shows how the attitudes of people about residential location have changed in the MENA region, but there are still contextual differences to high-income countries. Full article

The present study aimed to investigate the psychosocial impacts of large-scale solar (LSS) power projects. There were 225 participants (n = 109 women, n = 3 did not indicate gender) participated in our study by completing a series of questionnaires. We found that participants who lived farther from the LSS power project location and those who viewed the project as being impactful were optimistic about the benefits LSS power projects could bring. Our participants also demonstrated support for renewable energy development in Malaysia. These findings may provide important implications for the implementation and execution of LSS power projects and policies. Full article

This study aimed to evaluate the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study included the Internal Migration Statistics microdata provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR using machine learning techniques, such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements are relatively farther. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information for the urban management of metropolitan residential districts and the construction of reasonable housing policies. Full article