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Fuzzy Spatiotemporal Centrality for Urban Resilience

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Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation (INFUS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 307))

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Abstract

Traffic congestion is a growing concern in the world's most populated metropolitan areas. Over the past decades, several approaches have been developed to better understand and control urban traffic management, such as artificial intelligence, video streaming, fuzzy logic, and complex networks. In this study, we apply a combination of fuzzy logic and complex network analysis to better understand the dynamic structure of urban traffic networks. When studying the dynamic processes of transportation networks, the exploration of the critical entities plays a crucial role in exploring and analyzing the resilience and management of urban traffic systems. Therefore, we propose measures of flow and travel time variability as well as dynamic road saturation factors to develop fuzzy measures of dynamic centrality. Also, the fuzzy temporal spectral centrality is also considered for this purpose. Our proposal is a fundamental building component for intelligent traffic monitoring and provides real support for the resilience of urban networks.

This work was partially funded by the Digital Development Agency (ADD) and the National Center for Scientific and Technical Research (CNRST) in partnership with the Ministry of Industry, Commerce and Green and Digital Economy (MICEVN) and the Ministry of National Education, Professional Training, Higher Education and Scientific Research (MENFPESRC) # AL KHAWARIZMI Program #Intelligent & Resilient Urban Network Defender: A distributed real-time reactive intelligent transportation system for urban traffic congestion symbolic control and monitoring.

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Author information

Authors and Affiliations

  1. Computer Science Department, FSTM, Hassan II University of Casablanca, Casablanca, Morocco

    Azedine Boulmakoul & Fatima-ezzahra Badaoui

  2. LISA Lab. ENSA Berrechid, Hassan 1st University, Settat, Morocco

    Lamia Karim

  3. LIG/MRIM, CNRS, University Grenoble Alpes, Grenoble, France

    Azedine Boulmakoul & Ahmed Lbath

  4. ADMIR Lab. ENSIAS, Mohamed V University, Rabat, Morocco

    Rachid Oulad Haj Thami

Authors
  1. Azedine Boulmakoul
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  2. Fatima-ezzahra Badaoui
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  3. Lamia Karim
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  4. Ahmed Lbath
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  5. Rachid Oulad Haj Thami
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  3. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Basar Oztaysi

  5. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  6. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

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Boulmakoul, A., Badaoui, Fe., Karim, L., Lbath, A., Oulad Haj Thami, R. (2022). Fuzzy Spatiotemporal Centrality for Urban Resilience. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 307. Springer, Cham. https://doi.org/10.1007/978-3-030-85626-7_92

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