Document Type : Original Article
GIS Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
Naval Academy Research Institute, Lanvéoc- Poulmic, France
Burgundy Franche-Comté University, France
In the realm of urban transportation management, identifying critical locations within transportation networks is paramount for efficient urban planning, crisis management, and infrastructure development. This study investigates the effectiveness of the Network Kernel Density Estimation (Net-KDE) method in identifying these critical locations. The Net-KDE method is chosen for its inherent capacity to consider spatial patterns and path length between sample and estimation points, making it well-suited for capturing the complexities of urban transportation networks. The approach is experimented with a series of control maps applied to Tehran city. Overall, Net-KDE provides valuable and agreeable outputs that show its potential. The findings underscore the crucial role of scenario customization in enhancing the method's accuracy. Remarkably, featuring adaptive bandwidth and strategic sampling emerges as the most effective in identifying critical locations. This research has a broader impact on crisis responders, urban developers, and city planners in addition to improving our knowledge of urban network dynamics.