Математична модель ациклічного нечіткого керування системою світлофорів з адаптивним безумовним пріоритетом трамваю
DOI:
https://doi.org/10.30837/0135-1710.2022.178.023Анотація
Розглянуто питання врахування динаміки трамваю під час його наближення до перехрестя для надання безумовного світлофорного пріоритету. Розроблено математичну модель нечіткого керування системою світлофорів перехрестя. Запропоновані правила для формування значень важливостей світлофорних сигналів для трамваю та для ухвалення рішень щодо адаптації часових параметрів світлофорного плану на основі сумісности сигналів та їхніх важливостей. Математична модель реалізована та апробована за допомогою засобу моделювання міської мобільности SUMO для помірного та насиченого транспортних попитів на штучному перехресті.
Посилання
American Public Transportation Association. Economic Impact of Public Transportation Investment 2020 UP-DATE. APTA: American Public Transportation Association, 2020.
Litman T. Evaluating Public Transit Benefits and Costs. Victoria Transport Policy Institute Victoria, BC, Canada, 2021.
Vuchic V.R. Urban transit systems and tech-nology. John Wiley & Sons, 2007.
Bruun E., Allen D., Givoni M. Choosing the right public transport solution based on performance of components: 4 / Transport. 2018. Vol.
№ 4. P. 1017-1029.
M. Lytvynenko, O. Shkil, I. Filippenko, L. Rebezyuk. Model and Means of Timed Automata-based Real-time Adaptive Transit Signal Control. IEEE East-West Design & Test Symposium (EWDTS). 2020. Р. 1-4.
Transportation Research Board, National Academies of Sciences, Engineering, and Medicine. Transit Signal Priority: Current State of the Practice. Washington, DC: The National Academies Press, 2020.
Smith H.R., Hemily B., Ivanovic M. Transit Signal Priority (TSP): A Planning and Implementation Handbook. Washington, DC, USA: ITS America. 2005. 212 p.
American Association of State Highway and Transportation Officials (AASHTO), Institute of Transportation Engineers (ITE), National Electrical Manufacturers Association (NEMA). NTCIP 1211 Object Definitions for Signal Control and Prioritization (SCP). 2014.
Bhouri N.,Mayorano F.,Lotito P.,Haj Salem H., Lebacque J. Public Transport Priority for Multimodal Urban Traffic Control / Cybernetics and Information Technologies. 2015. Vol. 15. № 5. P. 17-36.
Jiang P.P., Poschinger A., Qi T.Y. MOTION - A Developing Urban Adaptive Traffic Signal Control System . Advanced Materials Research. 2013. Vol. 779. P. 788-791.
Oliveira-Neto F.M., Loureiro C.F.G., Han L.D. Active and passive bus priority strat-egies in mixed traffic arterials controlled by SCOOT adaptive signal system: Assessment of performance in Fortaleza, Brazil. Transportation research record. 2009. Vol. 2128. № 1.
Farges J.-L., Henry J.-J. PT priority and PRODYN. Artech House, 1994. P. 3086-3093.
Mirchandani, P., Knyazyan, A., Head, L., Wu, W. An Approach Towards the Integration of Bus Priority, Traffic Adaptive Signal Control, and Bus Infor-mation/Scheduling Systems. Lecture Notes in Economics and Mathematical Systems. 2001. Vol. 505. P. 319-334.
Niittymaki J., Maenpaa M. The Role of Fuzzy Logic Public Transport Priority in Traffic Signal Control / Traffic Engineering+ Control. 2001. Vol. 42. № 1. P. 22-26.
Gartner N.H., Tarnoff P.J., Andrews C.M. Evaluation of optimized policies for adaptive control strategy. Transportation research record. 199. № 1324. P. 105-114.
Kaparias, I., K. Zavitsas, M.G.H. Bell, M. Tomassini State-of-the-art of urban traffic management policies and technologies. ISIS. 2010. Vol. 13. P. 08.
A Definition of Soft Computing - adapted from L.A. Zadeh [Electronic resource]. URL: http://www.softcomputing.de/def.html.
Greguric, M., Vujic, M., Alexopoulos, C. and Miletic, M. Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data: 11. Applied Sciences. 2020. Vol. 10. № 11. P. 4011.
Jin J., Ma X. A group-based traffic signal control with adap-tive learning ability. Engineering applications of artificial intelligence. 2017. Vol. 65. P. 282-293.
Odeh, S.M., Mora, A.M., Moreno, M.N., Merelo, J.J. A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System . Advances in Fuzzy Systems. Hindawi. 2015. Vol. 2015. P. e378156.
Ghanim M.S., Abu-Lebdeh G. Real-Time Dynamic Transit Signal Priority Optimization for Coordinated Traffic Networks Using Genetic Algorithms and Artificial Neural Networks / Journal of Intelligent Transportation Systems. Taylor & Francis, 2015. Vol. 19, № 4. P. 327-338.
Kuang X., Xu L. Real-Time Traffic Signal Intelligent Control with Transit Priority / Journal of Software. 2012. Vol. 7. № 8. P. 1738-1743.
Niittymaki J. Fuzzy Logic Application to Public Transport Priorities at Signalized Intersections. Mathematics in Transport Planning and Control. Emerald Group Publishing Limited. 1998. P. 47-57.
Do L., Herman I., Hurak Z. Onboard Model-based Prediction of Tram Braking Distance. IFAC-PapersOnLine. Elsevier. 2020. Vol. 53. № 2. P. 15047-15052.
CKD Tatra T3 imhd.sk Bratislava [Electronic resource]. URL: https://imhd.sk/ba/popis-typu-vozidla/20/%C4%8CKD-Tatra-T3.
Takaoka Y., Kawamura A. Disturbance observer based adhesion control for Shinkansen. Nagoya, Japan. IEEE. 2000. P. 169-174.
Hay W.W. Propulsive Resistance / Railroad engineering. 2nd ed. Wiley. 1982. P. 69-89.
Heydecker B.G. A decomposition approach for signal opti-misation in road networks. Transportation Research Part B: Methodological. Pergamon. 1996. Vol. 30. № 2. P. 99-114.
Niittymaki J., Pursula M. Signal control using fuzzy logic / Fuzzy sets and systems. North-Holland. 2000. Vol. 116. № 1. P. 11-22.
Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y.P., Hilbrich, R., Lucken, L., Rummel, J., Wagner, P. and Wiesner, E. Microscopic Traffic Simulation using SUMO. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). 2018. P. 2575-2582.