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Cities worldwide are quickly developing due to population increase, economic development, and infrastructural improvements in the 21st century. MRT may transform urban mobility and the landscape by providing reliable, efficient, and sustainable transportation. This study seeks to identify the impact of newly established metro stations on the urban landscape and property values. The study focuses on Uttara South and North, two sizable metro stations in the capital city of Dhaka, Bangladesh. Shannon’s Entropy was applied to measure land-use dispersion and detect spontaneous development. Mann-Kendall test analysis was conducted to identify factors contributing to expansion. The Hedonic Pricing Model (HPM) was also employed to assess the impact of proximity to metro stations on residential property values. Additionally, a SWOT analysis was performed. Data was gathered through a questionnaire survey, Key Informant Interviews, and the Landsat 8 satellite. Results show moderate dispersion in entropy values, suggesting growth is structured. The HPM identified eight attributes contributing to the increase in property prices. With p-values below 0.05, each of the eight attributes showed statistical significance. Uttara North Metro Station’s entropy value rose from 1.20 in 2020 to 1.23 in 2024. Likewise, the entropy value of Uttara South Metro Station increased from 1.12 in 2020 to 1.18 in 2024. The Kendall tau value (1) shows upward trends, indicating that increasing population and housing demand are among the significant factors contributing to expansion. This research advocates affordable housing and inclusionary zoning rules to share Transit-Oriented Development (TOD) benefits evenly across socioeconomic levels to foster socio-economic diversity.

Transport mode choice is a crucial determinant of urban mobility patterns, influencing infrastructure planning, environmental sustainability, and social equity. Although it is important, there is still an unfulfilled gap in the use of socio-demographic, behavioral and perception-based factors to accurately predict choice of transport mode especially in the developing countries. The proposed study shall fill this gap by applying a deep learning model to make predictions of transport mode choice using a multidimensional survey data. The primary objective of this research was to investigate the potential of a Gated Recurrent Unit (GRU) neural network for classifying transport mode choice using a variety of socio-demographic, economic, and travel behavior features. The dataset includes 348 respondents, capturing responses related to seven transport modes: Bike, Bus, CNG, Car, Cycle, Easy bike, and Rickshaw. Key attributes in the dataset include gender, age, occupation, income, travel objectives, and safety perceptions related to different transport modes. The preprocessing of the data included the following: one-hot encoding of features of character type, normalization of numeric data, and label encoding of target variable. The GRU-based model was trained and tested based on stratified 80:20 train-test split and tested with reference to confusion matrices, ROC, and precision-recall. The accuracy of the model has a value of 79%, ensuring very good results on modes such as CNG (precision = 1.00, recall = 1.00) and Easy bike (precision = 0.73, recall = 0.88). This class-wise analysis raised the issue of less frequent mode prediction such as Cycle and Car modes which were having a lower recall. The AUC scores had micro- and macro-averages of 0.943 and 0.918 respectively. These findings reveal the ability of deep learning models, especially GRUs to be used reliably to predict transport mode preferences using a diversified group of socio-demographic and behavioral variables. This predictive modeling would provide urban designers and policymakers with the information needed to design more specifically focused interventions to promote sustainable transport networks, increase modal share, and reduce congestion in urban areas.

Tropical cyclones rank among the most destructive natural hazards globally, posing significant threats to coastal ecosystems and communities. Mangrove forests, renowned for their ecological importance and coastal protection services, are vulnerable to these disturbances, suffering structural damage, habitat loss, and disruption of vital ecosystem functions. Conventional field-based assessment methods often fall short in capturing the rapid and widespread impacts of cyclones, particularly in remote or cloud-obscured regions. This review aims to provide a comprehensive synthesis of remote sensing applications for monitoring cyclone-induced impacts on mangrove and coastal ecosystems worldwide. Through a systematic literature review of 74 peer-reviewed articles from 1990 to 2025, the study evaluates the utility of optical sensors, radar systems, and multi-sensor platforms in assessing inundation, vegetation damage, and ecosystem service loss. Key methodological advances such as time-series analysis, machine learning, and UAV-based validation are highlighted, alongside critical gaps including limited geographic coverage, weak validation practices, and minimal socio-economic integration. Notably, 75.4% of reviewed studies are concentrated in Asia, with Bangladesh and India alone accounting for 44.6% of the total literature, underscoring a pronounced geographic bias. The findings underscore the need for robust, near-real-time monitoring frameworks that combine satellite technologies with ground data and community engagement. Ultimately, the review advocates for an integrated, multi-sensor, and participatory approach to cyclone resilience, offering valuable insights for future research, disaster response planning, and sustainable mangrove management.

Bangladesh and other developing nations face road traffic accidents (RTAs) as a major public safety issue. Accidents have grown due to growing motor vehicle use, obsolete infrastructure, and lax traffic law enforcement. The Khulna–Rajshahi Highway experiences frequent and severe road crashes, resulting in high fatalities, injuries, and economic losses. The study aims to identify crash hotspots, assess land-use influences on road safety across varying road environments, and propose infrastructure and policy measures to enhance safety along the Khulna–Rajshahi corridor, with particular emphasis on VRU risks due to their disproportionate burden in Bangladesh's mixed-traffic environment. The research relied on a combination of Geographic Information System (GIS) tools, police-reported accident data from 2023 to 2024 integrated with VRU survey data (n = 208), and high-resolution satellite imagery. Advanced spatial techniques such as Kernel Density Estimation (KDE), Getis-Ord Gi∗ hotspot analysis, and Geographically Weighted Regression (GWR) were used to uncover meaningful patterns, yielding a model fit of R2 = 0.27. The results indicate a notable clustering of accidents (Moran's I = 0.065, p < 0.05), with urban stretches along the corridor reporting the highest concentrations, reaching up to 30–35 accidents per square kilometer. Severity mapping revealed critical danger zones in places like Puthia (Rajshahi), Boraigram Upazila (Natore), among others. Due to poor infrastructure and traffic management, motorcyclists (77% of VRUs) and pedestrians were especially susceptible in mixed-use zones near lively markets and junctions, where approximately 45% of vulnerable road user (VRU) incidents occurred. This study suggests immediate, focused safety measures to decrease accidents. Building pedestrian overpasses and placing traffic lights in high-risk areas are key. A replicable GIS-based, corridor-level evidence framework that uniquely integrates VRU behavioral data with spatial statistics allows planners and transport authorities to prioritize location-specific interventions to reduce road traffic accidents in developing-country highway contexts.

Bangladesh and other developing nations face road traffic accidents (RTAs) as a major public safety issue. Accidents have grown due to growing motor vehicle use, obsolete infrastructure, and lax traffic law enforcement. The Khulna–Rajshahi Highway experiences frequent and severe road crashes, resulting in high fatalities, injuries, and economic losses. The study aims to identify crash hotspots, assess land-use influences on road safety across varying road environments, and propose infrastructure and policy measures to enhance safety along the Khulna–Rajshahi corridor, with particular emphasis on VRU risks due to their disproportionate burden in Bangladesh's mixed-traffic environment. The research relied on a combination of Geographic Information System (GIS) tools, police-reported accident data from 2023 to 2024 integrated with VRU survey data (n = 208), and high-resolution satellite imagery. Advanced spatial techniques such as Kernel Density Estimation (KDE), Getis-Ord Gi∗ hotspot analysis, and Geographically Weighted Regression (GWR) were used to uncover meaningful patterns, yielding a model fit of R2 = 0.27. The results indicate a notable clustering of accidents (Moran's I = 0.065, p < 0.05), with urban stretches along the corridor reporting the highest concentrations, reaching up to 30–35 accidents per square kilometer. Severity mapping revealed critical danger zones in places like Puthia (Rajshahi), Boraigram Upazila (Natore), among others. Due to poor infrastructure and traffic management, motorcyclists (77% of VRUs) and pedestrians were especially susceptible in mixed-use zones near lively markets and junctions, where approximately 45% of vulnerable road user (VRU) incidents occurred. This study suggests immediate, focused safety measures to decrease accidents. Building pedestrian overpasses and placing traffic lights in high-risk areas are key. A replicable GIS-based, corridor-level evidence framework that uniquely integrates VRU behavioral data with spatial statistics allows planners and transport authorities to prioritize location-specific interventions to reduce road traffic accidents in developing-country highway contexts.

Open spaces function as the lungs of a city, providing environmental and social benefits. In a densely populated city like Dhaka, community-based open spaces play a crucial role in enhancing urban livability. The sustainability of such spaces largely depends on effective management. However, these spaces face multiple barriers in this regard. Therefore, this study aimed to explore the transformation of a community-based open space named Road No. 17 Park, understand the connection with other organizations and identify challenges faced. Employing Participatory Rural Appraisal (PRA) tools and Key Informant Interviews (KIIs), the research identifies that the committee’s strong relationships with the Ward Councillor and the local parliamentary member facilitated management activities, while unity among members remains its core strength. However, inadequate financial resources pose significant challenges. Hence, the study recommended future guidelines on the management of community-based open spaces, highlighting effective Public-Private Partnership and funding from NGOs and the local government.