Transit-oriented development (TOD) is a planning strategy that combines land use and transportation planning to promote economic, environmental, and social sustainability. While developed cities have embraced TOD, developing cities need to adopt it faster. This has resulted in a need for robust TOD measurement frameworks for developing countries. Furthermore, existing frameworks often use subjective weightage for different TOD indicators, which can lead to human biases. To address these issues, the authors aimed to develop a more robust and objective framework for measuring TOD in developing cities, particularly Dhaka, Bangladesh. The authors used density, diversity, destination accessibility, and design criteria to select eight indicators for measuring TOD. However, a buffer radius of 800 meters was taken for each of the 17 stations to calculate TOD. An objective-weighted spatial multi-criteria analysis (OSMCA) was used to evaluate the framework. The model’s robustness was assessed by analyzing the sensitivity of eight TOD scenarios and identifying hotspot clusters using statistical methods. Additionally, the authors ranked the stations based on the highest TOD score and compared TOD with developed and developing cities to gain planning insights. They proposed three different TOD planning methodologies for nodes that emphasize the importance of design, destination access, and density for (re)development, zoning, and affordable housing policies in Dhaka’s regions. Finally, the study discussed limitations and future research priorities.

Smoking is responsible for ninety percent of all premature deaths worldwide. Its prevalence is increasing in developing countries such as Bangladesh. Road traffic accidents (RTAs) have risen dramatically in recent years, with tobacco use accounting for 4–5 million fatalities each year. This trend will likely continue as more bus and truck drivers smoke in Bangladesh. Therefore, our study attempts to identify predictors that may be directly related to the frequency of RTAs and smoking. The study included 424 bus and truck drivers and ten key informant interviews (KIIs). Then, a linear regression (LR) analysis model was used to determine how various smoking-related predictors contribute to the frequency of accidents. Furthermore, a binary logistic regression (BLR) model was used to examine the likelihood of a driver being involved in an accident related to various smoking-related predictors. This study demonstrates a strong association between the incidence of accidents and the number of times a person smokes, smokes while driving, and uses smokeless tobacco (SLT) daily. The result has been taken from the second BLR model, which fits with the data more than the LR model. According to that model, a driver is more likely to be in an accident if the number of days per year that he smokes cigarettes increases and if he smokes while driving. Additionally, it stresses the need for more research to make a more accurate forecast.