@conference {Badaoui202112,
	title = {Fuzzy Dynamic Centrality for Urban Traffic Resilience},
	booktitle = {2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021},
	year = {2021},
	note = {cited By 1},
	pages = {12-16},
	abstract = {When traveling to and from home, work or vacation, we have all experienced traffic delays. As a result, road travelers often add time to their trip in advance to anticipate traffic jams. Meanwhile, unplanned traffic jams can be fundamentally problematic for travelers. Which are caused by a variety of reasons, such as road closures, events, or severe weather. In this study, we shed the light on the importance of travel time in the detection of traffic congestion. We propose a fuzzy process to measure the uncertainty in the computation of travel time in order to develop fuzzy dynamic centrality for the detection of traffic congestion. A simulation is conducted on a small area from Mohammedia city using microscopic traffic simulator SUMO{\textquoteright}. The calculation of the uncertainty of the estimated travel time over the study period is based on the generation of Gaussian fuzzy numbers. Our proposed study is an essential component of smart traffic monitoring and provides genuine assistance for urban network resilience. {\textcopyright} 2021 IEEE.},
	keywords = {Fuzzy dynamic centrality, Fuzzy rules, Fuzzy travel time, Gaussian fuzzy numbers, Home work, Intelligent systems, Intelligent transportation systems, Intelligent vehicle highway systems, Motor transportation, Resilient networks, Roads and streets, Street traffic control, Traffic congestion, Traffic jams, Travel time, Travel-time, Uncertainty, Uncertainty analysis, Urban traffic, Vehicle actuated signals},
	doi = {10.1109/ICDABI53623.2021.9655939},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124647354\&doi=10.1109\%2fICDABI53623.2021.9655939\&partnerID=40\&md5=cff82ecaec7b839ed7c2803a46e25b18},
	author = {Badaoui, F.-E. and Boulmakoul, A. and Thami, R.O.H.}
}
