Browsing by Author "Ahmed, Md. Tawkir"

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Dataset on Factors Influencing Pedestrian Crosswalk Usage Behavior in High-density Urban Areas of a Developing Country
(Elsevier, 2024-09-04) Sakiba, Nazmus; Paul, Tonmoy; Ahmed, Md. Tawkir; Al Momin, Khondhaker; Barua, Saurav
The dataset consists of survey data on pedestrian crosswalk usage behavior in high-density urban areas of a developing country, specifically collected from Dhaka, the capital city of Bangladesh. Data were gathered through a questionnaire survey conducted at twelve key locations, covering eight attributes related to crosswalk behavior and the demographic details of respondents. The survey yielded 682 valid responses, focusing on factors such as the suitability of crosswalk locations, guard rails, and lighting. The dataset is structured to support analyses using supervised machine learning techniques, facilitating reproducibility, secondary analysis, and policy development for pedestrian safety improvements. Furthermore, the dataset can be reused for cross-validation of future studies, comparison with pedestrian behavior in similar urban settings, and the development of predictive models to enhance pedestrian infrastructure in other developing regions.
Investigating factors influencing pedestrian crosswalk usage behavior in Dhaka city using supervised machine learning techniques
(2024-03-24) Sakib, Nazmus; Paul, Tonmoy; Ahmed, Md. Tawkir; Al Momin, Khondhaker; Barua, Saurav
Pedestrians are the most vulnerable road users and are over-represented in casualty statistics, particularly in low- and middle-income countries like Bangladesh. To ensure the safety of pedestrians, it is necessary to identify the factors underlying pedestrian behavior while crossing. Hence, this study aims to predict the pedestrian decision regarding crosswalks using supervised machine learning techniques namely, Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boost (XGBoost). A questionnaire survey was conducted in twelve important locations of Dhaka, Bangladesh using 8 attributes related to crosswalk behavior. Analysis suggests RF model is the most effective in terms of prediction performances, specifically having a 96.00% F1 score and 95.83% MCC value. It has been found that unsuitability of crosswalk location, absence of guard rails on median, and inadequate lightning at night near crosswalks are the most important features for preferring to use crosswalks. The findings of the study will help policymakers and transport planners to plan accordingly in order to develop safe crosswalks.