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This study explores the modality and trip chaining patterns of individuals in Dhaka, Bangladesh. We use household-level trip data for a day collected from randomly selected respondents of the Dhaka Metropolitan Development Plan area. We found that walking and rickshaw are the dominant modes of travel. The majority of individuals are unimodal and mostly depend on either walking or on rickshaws. Individuals generally walk for the first and last-mile connections to public transit. Cars are used more for non-home-based business trips. Personalized modes such as cars, cycles, and motorcycles are present at a higher proportion in the super complex trip chain than other types of chains.

This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.

Bangladesh has emphasized active transportation in its transportation policies and has encouraged its population, especially the youth and students, towards bicycling. However, there is a scarcity of studies that have examined the factors important to the choice of active transportation that can be referenced to support the initiative. To address this research gap, in this study, we explore the influence of sociodemographics and latent perceptions of a built environment on the choice to walk and bicycle among students and nonstudents in Rajshahi, Bangladesh. In Rajshahi, we conducted a household survey between July and August, 2017. We used a modeling framework that integrated choice and latent variable (ICLV) models to effectively incorporate the latent perception variables in the choice model, addressing measurement error and endogeneity bias. Our models show that students are influenced by perceptions of safety from crime, while nonstudents are influenced by their perceptions of the walkability of a built environment when choosing a bicycle for commuting trips. For recreational bicycle trips, students are more concerned about the perceptions of road safety, whereas nonstudents are concerned about safety from crime. We find that road safety perception significantly and positively influences walking behavior among nonstudents. Structural equation models of the latent perception variables show that females are more likely to provide lower perceptions of neighborhood walkability, road safety, and safety from crime. Regarding active transportation decisions, overall, we find there is a difference between student and nonstudent groups and also within these groups. The findings of this study can assist in developing a sustainable active transportation system by addressing the needs of different segments of the population. In this study, we also provide recommendations regarding promoting active transportation in Rajshahi.

In developing countries, rapid and unregulated population growth, along with economic and industrial growth and development have accelerated the rate of changes in land use and land cover (LULC) during the early twenty-first century. One of the most effective ways to assess and manage the land transformation is through quantitative assessment of LULC changes. To find the best classifier for further uses of earth observation data, it is necessary to compare the accuracy of several LULC modelling algorithms. In this paper, four machine learning (ML) algorithms/classifier namely support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF) and decision tree (DT) have been examined using Landsat 8 Operational Land Imager (OLI) sensor data. All the algorithms performed almost in a similar accuracy level, where SVM scores 84.00% of Cohen kappa and 90.74% of overall accuracy. These figures are 82.99% and 90.05% respectively for KNN. For RF, Cohen kappa and overall accuracy recorded 78.24% and 87.62% respectively. DT gained 74.46% for Cohen kappa and 85.51% for overall accuracy. Clearly SVM outperformed other algorithms in low spatial-resolution satellite data. The outcome of the study would help in different stages of LULC modelling. Moreover, it would help the planners and policy makers in formulation and preparation of land-based policy and planning to ensure sustainable living environment.

Major cities of Bangladesh have been experiencing rapid urbanization and industrialization. These are incurring positive externalities to national economy at the expense of environmental degradation and deterioration of living environment. The ambivalent sequel of industrialization has made it necessary to study urban areas and monitor spatiotemporal changes to facilitate decision-making process regarding land use planning, resource distribution, priority setting for planning interventions. Thus, this study aims to classify land use land cover(LULC) of Narayanganj Sadar Upazila and detect spatiotemporal changes within the period of 2015 to 2020using MLC algorithm based supervised classification method. To serve this purpose, sentinel-2 satellite imagery are used. The results derived from the study elicit an increase (24.14 acre) in industrial land in 2020compared to 2015. Approximately 1,538-acres land transformed into built-up area in 2020. Decrease in vegetation (15.85%) and water body (7.65%) are also evident from the findings. The results of the study will be conducive to have an initial glimpse of the LULC changing trend in the study area due to rapid urbanization and thus, will be instrumental for performing further research.