Niaz Mahmud Zafri
Biographic Data
| ID | 6477017 |
|---|---|
| NAME | Niaz Mahmud Zafri |
| GIVEN NAMES | Niaz Mahmud |
| FAMILY NAME | Zafri |
| SIGNATURE | ZAFRI N M |
| AFFILIATIONS | Bangladesh University of Engineering and Technology |
| ORCID | 0000-0002-0120-1862 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Advancing Pedestrian Models: A Comparative Review and Vision for the Future
Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach
In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance…
A multi-criteria decision-making approach for quantification of accessibility to market facilities in rural areas: An application in Bangladesh
Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach
In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance…
A multi-criteria decision-making approach for quantification of accessibility to market facilities in rural areas: An application in Bangladesh
Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach
In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance…
Advancing Pedestrian Models: A Comparative Review and Vision for the Future
Computer Science (2 works) · Economics (2 works) · Urban and Freight Transport Logistics (2 works) · Urban Transport and Accessibility (2 works) · Architecture and Computational Design (1 works) · Artificial Intelligence (1 works) · Business (1 works) · Context (archaeology (1 works) · Data mining (1 works) · Decision support system (1 works)