Volume 3 , Issue 2 , PP: 91-101, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Z. Abualkishik 1 * , Rasha Almajed 2 , William Thompson 3
Doi: https://doi.org/10.54216/IJWAC.030204
In order to alleviate traffic congestion, traffic control systems are an important tool. These systems strive to boost the efficiency of road systems to optimize traffic flow on individual road segments. The benefits of real-time traffic control systems might be increased by integrating new telecommunication and autonomous car technology. There is six real-time traffic advancing sustainable development examined in this study: variable message signs and ramp meters, traffic diversion, and the integration of driverless vehicles into other traffic management systems, with four key criteria: economics, community and social, ecologic and traffic security, as well as 13 sub-criteria using MCDM. To do this, we offer unique additions to the WASPAS technique.
Autonomous Vehicles , Traffic control , WASPAS , MCDM , Traffic , security
[1] T. Yigitcanlar, M. Kamruzzaman, M. Foth, J. Sabatini-Marques, E. da Costa, and G. Ioppolo, “Can cities become smart without being sustainable? A systematic review of the literature,” Sustainable cities and society, vol. 45, pp. 348–365, 2019.
[2] P. Chun and M. D. Fontaine, “Evaluation of the impact of the I-66 active traffic management system,” Virginia Transportation Research Council, 2016.
[3] Z. H. Khattak, H. Park, S. Hong, R. A. Boateng, and B. L. Smith, “Investigating cybersecurity issues in active traffic management systems,” Transportation research record, vol. 2672, no. 19, pp. 79–90, 2018.
[4] B. Kuhn, K. Balke, N. Wood, and J. Colyar, “Active Traffic Management (ATM) Implementation and Operations Guide,” United States. Federal Highway Administration, 2017.
[5] N. B. Hounsell, B. P. Shrestha, J. Piao, and M. McDonald, “Review of urban traffic management and the impacts of new vehicle technologies,” IET intelligent transport systems, vol. 3, no. 4, pp. 419–428, 2009.
[6] M. Gohar, M. Muzammal, and A. U. Rahman, “SMART TSS: Defining transportation system behavior using big data analytics in smart cities,” Sustainable cities and society, vol. 41, pp. 114–119, 2018.
[7] M. M. Rathore, A. Paul, W.-H. Hong, H. Seo, I. Awan, and S. Saeed, “Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data,” Sustainable cities and society, vol. 40, pp. 600–610, 2018.
[8] Z. H. Khattak and M. D. Fontaine, “A Bayesian modeling framework for crash severity effects of active traffic management systems,” Accident Analysis & Prevention, vol. 145, p. 105544, 2020.
[9] C. Antoniou, H. N. Koutsopoulos, and M. Ben-Akiva, Kalman filter applications for traffic management. INTECH Open Access Publisher, 2010.
[10] T. Luettel, M. Himmelsbach, and H.-J. Wuensche, “Autonomous ground vehicles—Concepts and a path to the future,” Proceedings of the IEEE, vol. 100, no. Special Centennial Issue, pp. 1831–1839, 2012.
[11] S. A. E. O.-R. A. V. S. Committee, “Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,” SAE International: Warrendale, PA, USA, 2018.
[12] A. Chehri and H. T. Mouftah, “Autonomous vehicles in the sustainable cities, the beginning of a green adventure,” Sustainable Cities and Society, vol. 51, p. 101751, 2019.
[13] M. Makridis, K. Mattas, B. Ciuffo, M. A. Raposo, T. Toledo, and C. Thiel, “Connected and automated vehicles on a freeway scenario. Effect on traffic congestion and network capacity,” 7th Transport Research Arena, p. 13, 2018.
[14] A. Talebpour and H. S. Mahmassani, “Influence of connected and autonomous vehicles on traffic flow stability and throughput,” Transportation Research Part C: Emerging Technologies, vol. 71, pp. 143–163, 2016.
[15] A. Kuraksin, A. Shemyakin, and A. Parshkov, “Integrated assessment of traffic management efficiency in real time based on DTA model,” Transportation Research Procedia, vol. 50, pp. 337–345, 2020.
[16] P. Wang, H. Zhao, L. Wang, Z. He, and D. Wu, “A dynamic speed guidance model based on cooperative vehicle infrastructure system,” in 2016 Chinese Control and Decision Conference (CCDC), 2016, pp. 3974–3979.
[17] H. Chen and G. Abu-Lebdeh, “Assessment of Capacity and Flow Improvements of Combined Dynamic Signal Control and Dynamic Speed Limits in Signalized Networks,” 2006.
[18] S. Chen, J. Sun, and J. Yao, “Development and simulation application of a dynamic speed dynamic signal strategy for arterial traffic management,” in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011, pp. 1349–1354.
[19] D. S. Chen, X. X. Yu, K. Q. Hu, X. Sun, and Y. Y. Xia, “Safety-oriented speed guidance of urban expressway under model predictive control,” International journal of simulation modelling, vol. 13, no. 2, pp. 219–229, 2014.
[20] M. Jha, D. Cuneo, and M. Ben-Akiva, “Evaluation of freeway lane control for incident management,” Journal of transportation engineering, vol. 125, no. 6, pp. 495–501, 1999.
[21] K. A. Harder and J. R. Bloomfield, “Investigating the Effectiveness of Intelligent Lane Control Signals on Driver Behavior,” 2012.
[22] L. Schaefer, J. Upchurch, and S. A. Ashur, “An evaluation of freeway lane control signing using computer simulation,” Mathematical and computer modelling, vol. 27, no. 9–11, pp. 177–187, 1998.
[23] K. Chatterjee and M. Mcdonald, “Effectiveness of using variable message signs to disseminate dynamic traffic information: Evidence from field trails in European cities,” Transport Reviews, vol. 24, no. 5, pp. 559–585, 2004.
[24] W. Zhao, Z. Ma, K. Yang, H. Huang, F. Monsuur, and J. Lee, “Impacts of variable message signs on en-route route choice behavior,” Transportation Research Part A: Policy and Practice, vol. 139, pp. 335–349, 2020.
[25] E. Van den Hoogen and S. Smulders, “Control by variable speed signs: results of the Dutch experiment,” 1994.
[26] P. Kachroo and K. Özbay, Feedback ramp metering in intelligent transportation systems. Springer Science & Business Media, 2003.
[27] A. G. Hobeika, R. Sivanandan, S. Subramaniam, K. Ozbay, and Y. Zhang, “Real-time traffic diversion model: Conceptual approach,” Journal of transportation engineering, vol. 119, no. 4, pp. 515–534, 1993.
[28] A. A. AlZubi, A. Alarifi, M. Al-Maitah, and O. Alheyasat, “Multi-sensor information fusion for Internet of Things assisted automated guided vehicles in smart city,” Sustainable Cities and Society, vol. 64, p. 102539, 2021.
[29] J. Tang, A. McNabola, and B. Misstear, “The potential impacts of different traffic management strategies on air pollution and public health for a more sustainable city: A modelling case study from Dublin, Ireland,” Sustainable Cities and Society, vol. 60, p. 102229, 2020.
[30] S. Sohrabi, H. Khreis, and D. Lord, “Impacts of autonomous vehicles on public health: A conceptual model and policy recommendations,” Sustainable Cities and Society, vol. 63, p. 102457, 2020.
[31] M. Barth and K. Boriboonsomsin, “Traffic congestion and greenhouse gases,” Access Magazine, vol. 1, no. 35, pp. 2–9, 2009.
[32] M. F. Ballesteros, K. Webb, and R. J. McClure, “A review of CDC’s Web-based Injury Statistics Query and Reporting System (WISQARSTM): Planning for the future of injury surveillance,” Journal of safety research, vol. 61, pp. 211–215, 2017.
[33] I. Gökaşar, “Şerit Kontrol Sistemleri: D 100 Karayolu, İstanbul Örneği,” İMO Teknik Dergi, vol. 134, pp. 7635–7657, 2016.
