@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.}
}
@conference {Naja2020,
	title = {Alert Message Dissemination using Graph-based Markov Decision Process Model in VANETs},
	booktitle = {IEEE International Conference on Communications},
	volume = {2020-June},
	year = {2020},
	note = {cited By 0},
	abstract = {Vehicular Ad-hoc Networks (VANETs) have many promising applications such as improving vehicle driver{\textquoteright}s safety, and so, decreasing car deaths. In VANETs, the main communication way is broadcast. Such Broadcast needs to be done efficiently especially in congested areas to avoid the broadcast storm problem. A crashed vehicle disseminates a message about its incident to all nodes in the network. The vehicle nodes of the network have to disseminate in their turn the alert message carefully to maximize both reachability and saved rebroadcast (by reducing congestion) while minimizing the delay. This concern is demonstrated and modeled by decision hypothesis. In this paper, at first, we are keen on the Markov decision process (MDP), which is utilized to model and tackle such successive decision issues. We aim to optimize a utility for each vehicle relying upon an irregular domain and choices made by it. As the MDP formalism achieves its cutoff points when it is important to consider the cooperation between the several vehicles. We will begin utilizing the Graph-based MDP where the state and activity spaces are factorizable by factors. We then demonstrate that the transition functions and rewards are deteriorated into nearby functions, and the reliance relations between the vehicles are spoken to by a graph. To Figure the optimal strategy, we use Mean Field Approximation (MFA) for tackling GMDP issue. {\textcopyright} 2020 IEEE.},
	keywords = {Accidents, Broadcast storm problem, Graphic methods, Markov decision process models, Markov Decision Processes, Markov processes, Mean field approximation, Message dissemination, Mobile telecommunication systems, Optimal strategies, Traffic congestion, Transition functions, Vehicles, Vehicular ad hoc networks, Vehicular Adhoc Networks (VANETs)},
	doi = {10.1109/ICC40277.2020.9148997},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089436123\&doi=10.1109\%2fICC40277.2020.9148997\&partnerID=40\&md5=12d16adc7ab7a4b60d7ff52c15709c2c},
	author = {Naja, A. and Oualhaj, O.A. and Boulmalf, M. and Essaaidi, M. and Kobbane, A.}
}
