International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

A FUZZY-BASED APPROACH FOR TRAFFIC JAM DETECTION

  • Abd El-Tawaba, Ayman Hussein (Information System Department, Faculty of Information Technology - Misr University for Science and Technology) ;
  • Abd El Fattah, Tarek (Information System Department, Faculty of Information Technology - Misr University for Science and Technology) ;
  • Mahmood, Mahmood A. (Department of Information Systems, Faculty of Computer and Information Sciences, Jouf University)
  • 투고 : 2021.12.05
  • 발행 : 2021.12.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Though many have studied choosing one of the alternative ways to reach a destination, the factors such as average road speed, distance, and number of traffic signals, traffic congestion, safety, and services still presents an indisputable challenge. This paper proposes two approaches: Appropriate membership function and ambiguous rule-based approach. It aims to tackle the route choice problem faced by almost all drivers in any city. It indirectly helps in tackling the problem of traffic congestion. The proposed approach considers the preference of each driver which is determined in a flexible way like a human and stored in the driver profile. These preferences relate to the criteria for evaluating each candidate route, considering the average speed, distance, safety, and services available. An illustrative case study demonstrates the added value of the proposed approach compared to some other approaches.

키워드

참고문헌

  1. Chapter 8 - inuence of pricing on mode choice decision integrated with la- tent variable: The case of jakarta greater area, in: K. G. Goulias, A. W. Davis (Eds.), Mapping the Travel Behavior Genome, Elsevier, 2020, pp. 125-143. doi:https://doi.org/10.1016/B978-0-12-817340-4.00008-5.
  2. S. Trubia, A. Severino, S. Curto, F. Arena, G. Pau, Smart roads: An overview of what future mobility will look like, Infrastructures 5 (12) (2020) 107. https://doi.org/10.3390/infrastructures5120107
  3. P. Lemonakis, N. Eliou, T. Karakasidis, Investigation of speed and trajectory of mo-torcycle riders at curved road sections of two-lane rural roads under diverse lighting conditions, Journal of Safety Research (2021).
  4. M. M. Fouad, M. A. Mahmood, H. Mahmoud, A. Mohamed, A. E. Hassanien, Intelligent road surface quality evaluation using rough mereology, in: 2014 14th International Con-ference on Hybrid Intelligent Systems, 2014, pp. 18-22. doi:10.1109/HIS.2014.7086163.
  5. J. Alam, D. M. Pandey, Design and analysis of a two stage tra_c light system using fuzzy logic, Journal of Information Technology 5 (11 2015). doi:10.4172/2165-7866.1000162.
  6. N. H. Anwarul Azim, Fuzzy tra_c control system, School of Computing Queen's Univer- sity (6 2019). doi:https://www.scribd.com/document/379628329/Fuzzy-Project-Final.
  7. J. P. T. Deepak Garg, Algorithm for detection of hot spots of tra_c through analysis of gps data, Department of Computer Science & Engineering,Thapar University, Patiala (Punjab), India 37 (2010) 2952-2961.
  8. S. Hegazy, Fuzzy logic tra_c signal controller enhancement based on aggressive driver behavior classi_cation, [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain (2016). doi:https://fount.aucegypt.edu/etds/555.
  9. E. Nelson, Tra_c jam and its social impacts, Institute of Development Studies in Par-tial,Mzumbe University (2013). doi:https://fount.aucegypt.edu/etds/555.
  10. I. Lu, C. Lewis, S. J. Lin, The forecast of motor vehicle, energy demand and co2 emission from taiwan's road transportation sector, Energy Policy 37 (8) (2009) 2952-2961. doi:https://doi.org/10.1016/j.enpol.2009.03.039. URL https://www.sciencedirect.com/science/article/pii/S0301421509001943
  11. A. MAY, G. MARSDEN, Urban transport and mobility: Transport and innovation unleashing the potential, International Transport Forum. (2010). URL https://www.itfoecd.org/sites/default/files/docs/10highlights.pdf (Accessed 20 July 2021)
  12. V. Mirzakhanov, Value of fuzzy logic for data mining and machine learning: A case study, Expert Systems with Applications 162 (2020) 113781. doi:10.1016/j.eswa.2020.113781.
  13. A. Chakrabartty, S. Gupta, Tra_c congestion in the metropolitan city of kolkata, Journal of Infrastructure Development 6 (2014) 43-59. doi:10.1177/0974930614543046.
  14. H. Mpogole, S. Msangi, Tra_c congestion in dar es salaam: Implications for workers' productivity, Journal of Sustainable Development 9 (2016) 103-110.doi:10.5539/jsd.v9n6p103.
  15. S. Datta, J. Mukherjee, Real-time tra_c control system using fuzzy logic based edge detector for images, International Journal of Computer Applications 119 (2015) 26-32.doi:10.5120/21137-4129.