Smart Mobility, Evaluation of Urban Congestion through the Frugal Multi-Level Governance

Smart Mobility, Evaluation of Urban Congestion through the Frugal Multi-Level Governance

© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Adnane Founoun, Mahdi El Alaoui El Hanafi, Abdelkrim Haqiq, Aawatif Hayar

How to Cite?

Adnane Founoun, Mahdi El Alaoui El Hanafi, Abdelkrim Haqiq, Aawatif Hayar, "Smart Mobility, Evaluation of Urban Congestion through the Frugal Multi-Level Governance," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 327-333, 2022. Crossref,

Machine learning will be a technology that will greatly aid in the deployment of future smart cities in all these phases. Some complex problems can be difficult to understand or completely incomprehensible and can be difficult to solve using traditional machine learning approaches due to the lack of data on the problem. Therefore, there is a need for interactive machine learning (iML), an approach that uses humans as complements to machines. Smart mobility is one of the most popular areas of smart cities. This article is intended to distribute frugal diagnostics, which aims to investigate urban congestion based on input from users and people affected by machine learning technology. In addition, the presented assessments and perceived levels of congestion enable decision-makers to intelligently plan the transition to smart mobility, taking into account stakeholder views. This paper seeks to formulate a user-centric management process, as well as a frugal diagnostic system that enables agile and inexpensive decision-making.

Natural Language Toolkit, Interactive Machine Learning, Smart Mobility, Open Data, Multi-Level-Governance, decision-making process.

[1] A. Founoun, A. Hayar, Evaluation of the Concept of the Smart City Through Local Regulation and the Importance of the Local Initiative, 2018 IEEE International Smart Cities Conference, ISC2 2018. (2019) 1–6. doi:10.1109/ISC2.2018.8656933.
[2] H. Rezzouqi, I. Gryech, N. Sbihi, M. Ghogho, H. Benbrahim, Analyzing the Accuracy of the Historical Average for Urban Traffic Forecasting using Google Maps, Springer International Publishing. (2018). doi: 10.1007/978-3-030-01054-6_79.
[3] A. Hayes, G. Betis, Frugal Social Sustainable Collaborative Smart City Casablanca Paving the Way Towards Building New Concept for Future Smart Cities by and for All. (2018) 1–4. doi: 10.1109/senset.2017.8305444.
[4] A. Founoun, A. Hayar, Smart City Concept’s Energy Awareness Assessment Through Sustainable Development Standards, 3rd Renewable Energies, Power Systems and Green Inclusive Economy, REPS and GIE 2018. (2018) 1–6. doi:10.1109/REPSGIE.2018.8488808.
[5] T. Nam, T.A. Pardo, Smart City as Urban Innovation: Focusing on Management, Policy, and Context, ACM International Conference Proceeding Series. (2011) 185–194. doi:10.1145/2072069.2072100.
[6] P. Jones, P. Anciaes, C. Buckingham, C. Cavoli, T. Cohen, L. Cristea, R. Gerike, Project Summary and Recommendations for Cities Urban Mobility : Preparing for the Future, Learning from the Past. (2018).
[7] E. van der Zee, D. Bertocchi, D. Vanneste, Distribution of Tourists within Urban Heritage Destinations: A Hot Spot/Cold Spot Analysis of Tripadvisor Data as Support for Destination Management, Current Issues in Tourism. 23(2) (2020) 175–196. doi:10.1080/13683500.2018.1491955.
[8] A. Founoun, A. Hayar, A. Haqiq, The Textual Data Analysis Approach to Assist the Diagnosis of Smart Cities Initiatives, 5th IEEE International Smart Cities Conference, ISC2 2019. (2019) 150–153. doi:10.1109/ISC246665.2019.9071663.
[9] A. Bhayani, Word of Mouth in Consumers Purchase Decisions: The Moderating Role of Product Type, 21st IAMB Conference, International Academy of Management and Business, Canada. (2016) 1–13.
[10] Y. Mohd Adnan, H. Hamzah, M. Md Dali, M. Nasir Daud, Anuar Alias, An Initiatives-Based Framework for Assessing Smart City, Planning Malaysia. (5) (2016) 13–22. doi:10.21837/pm journal.v14.i5.189.
[11] J. Steenbruggen, E. Tranos, P. Nijkamp, Data From Mobile Phone Operators: A Tool for Smarter Cities?, Telecommunications Policy. 39(3–4) (2015) 335–346. Doi:10.1016/J.Telpol.2014.04.001.
[12] A.A. Batabyal, P. Nijkamp, Creative Capital, Information and Communication Technologies, and Economic Growth in Smart Cities, Economics of Innovation and New Technology. 28(2) (2019) 142–155. doi:10.1080/10438599.2018.1433587.
[13] S. Balbi, M. Misuraca, G. Scepi, Combining Different Evaluation Systems on Social Media for Measuring User Satisfaction, Information Processing and Management. 54(4) (2018) 674–685. doi:10.1016/j.ipm.2018.04.009.
[14] V. Fernandez-Anez, J.M. Fernández-Güell, R. Giffinger, Smart City Implementation and Discourses: An Integrated Conceptual Model. The Case of Vienna, Cities. 78(11) (2018) 4–16. doi:10.1016/j.cities.2017.12.004.
[15] V. Moustakas, A. Mattis, A. Vakali, L.G. Anthopoulos, CityDNA Dynamics: A Model for Smart City Maturity and Performance Benchmarking, The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020. (2020) 829–833. doi:10.1145/3366424.3386584.
[16] J. de D. Ortúzar, Sustainable Urban Mobility: What Can Be Done to Achieve it? Journal of the Indian Institute of Science. 99(4) (2019) 683–693. doi:10.1007/s41745-019-00130-y.
[17] V. Moustakas, A. Vakali, L.G. Anthopoulos, A Systematic Review for Smart City Data Analytics. 51(5) (2018).
[18] Founoun A, Hayar A, Essefar K, Haqiq A, Agile Governance Supported by the Frugal Smart City. In: Nagar A.K, Jat D.S, Marín-Raventós G, Mishra D.K, (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, Springer, Singapore. 334 (2022).
[19] Lunenburg, F. C, The Decision-Making Process. In National Forum of Educational Administration & Supervision Journal. 27(4) (2010).
[20] M. Bouchaqour, L. Ouadif, and L. Bahi, Assessing and Analysing the Potential of Urban Subsoil : A Case Study of Rabat, Morocco, 70(1) (2022) 192–198. doi: 10.14445/22315381/IJETT-V70I1P222.