Identyfikatory
Warianty tytułu
Podejście do projektowania infrastruktury transportowej z wykorzystaniem dwuetapowej metody bazującej na zmodyfikowanym algorytmie mrówkowym
Języki publikacji
Abstrakty
The main purpose of the paper is to develop a tool to effectively support the transport infrastructure planning process. The novel method is based on modified ant colony optimization and is used for evaluating new infrastructure sections in the public transport. The section could contain a single route as well as a combination of two or more routes. The authors propose a two-step evaluation. Step one uses a modified ant colony optimization to evaluate a single underground route. As a result, for each route, the effort that artificial ants have to cover the routes is obtained. Such effort is calculated considering the distance, total length, number of inhabitants, and areas with access to the rail network. In step two, the proposed routes are combined to create variants. The evaluation of combining routes use additional parameters like crossing time or travel time in alternative means. The case study located in one of the biggest agglomerations in Poland shows the method’s utility in the evaluation of options for a railway tunnel. The analyses show that all criteria have an influence on the results and that the new, two steps method gives an intriguing effect. The presented methodology contains novel elements and their implementation to specific transport infrastructure elements. The results are contrasted with an algorithm based on multi-criteria analysis, which showed the greater complexity of the proposed approach.
Głównym celem pracy jest opracowanie narzędzia, które umożliwi efektywne wsparcie w procesie planowania infrastruktury transportowej głównie pod kątem transportu zbiorowego. Nowatorska metoda opiera się na zmodyfikowanym algorytmie mrówkowym i jest wykorzystywana do oceny proponowanych nowych odcinków infrastruktury. Ocenie mogą podlegać warianty składające się z jednej lub więcej tras, co stanowi duży atut proponowanego narzędzia. Autorzy proponują dwuetapową ocenę. W kroku pierwszym przy użyciu zmodyfikowanego algorytmu mrówkowego oceniane są pojedyncze trasy. W rezultacie dla każdej trasy uzyskuje się wysiłek, jaki wirtualne mrówki muszą włożyć na pokonanie jej. Wysiłek ten jest obliczany bazując na odległości, potencjału pasażerskiego, atrakcyjności obszarów oraz dostępności do sieci kolejowej. W kroku drugim proponowane trasy są łączone w warianty. Bazując na wartościach otrzymanych w kroku pierwszym, dla każdego wariantu wyliczana jest zmodyfikowana wartość wysiłku. W tym celu wykorzystuje się dodatkowe kryteria oceny związane z czasem przejazdu oraz kosztem realizacji. Studium przypadku zlokalizowane jest we Wrocławiu i dotyczy problemu utworzenia nowego odcinka średnicowego. Otrzymane wyniki pokazują użyteczność metody w ocenie wariantów tunelu kolejowego. Ważną kwestią jest również to, że wszystkie kryteria mają wpływ na ocenę końcową. Przedstawiona metodyka zawiera nowatorskie elementy i ich implementację do konkretnych elementów infrastruktury transportowej. Otrzymane wyniki zestawiono z algorytmem opartym na analizie wielokryterialnej. Wykazało to większą złożoność proponowanego narzędzia, a tym samym lepsze przeanalizowanie problemu.
Czasopismo
Rocznik
Tom
Strony
67--82
Opis fizyczny
Bibliogr. 35 poz., il., tab.
Twórcy
autor
- Wroclaw University of Science and Technology, Faculty of Civil Engineering, Wroclaw, Poland
autor
- Wroclaw University of Science and Technology, Faculty of Civil Engineering, Wroclaw, Poland
Bibliografia
- [1] E. Cascetta, Transportation systems engineering: theory and methods. Springer Science & Business Media, 2001, doi: 10.1007/978-1-4757-6873-2.
- [2] H. Zhang, G. Lu, Y. Lei, G. Zhang, and I. Niyitanga, “A hybrid framework for synchronized passenger and train traffic simulation in an urban rail transit network”, International Journal of Rail Transportation, vol. 11, no. 6, pp. 912-941, 2023, doi: 10.1080/23248378.2022.2109522.
- [3] R. Bozzo, M. Canepa, C. Carnevali, R. Genova, and G. Priano, “Method for analysis and comparison in planning urban surface transport systems”, Sustainable City, vol. 155, pp. 931-942, 2012, doi: 10.2495/SC120782.
- [4] H. Orth, A. Nash, and U. Weidmann, “Level-Based Approach to Public Transport Network Planning”, Transportation Research Record, vol. 2537, no. 1, pp. 1-12, 2015, doi: 10.3141/2537-01.
- [5] G. Koroń and R. Żochowska, “Problems of Quality of Public Transportation Systems in Smart Cities-Smoothness and Disruptions in Urban Traffic”, in Modelling of the Interaction of the Different Vehicles and Various Transport Modes. Springer, 2020, pp. 383-414, doi: 10.1007/978-3-030-11512-8_9.
- [6] X. He, Z. Cao, S. Zhang, S. Liang, Y. Zhang, T. Ji, and Q. Shi, “Coordination Investigation of the Economic, Social and Environmental Benefits of Urban Public Transport Infrastructure in 13 Cities, Jiangsu Province, China”, International Journal of Environmental Research and Public Health, vol. 17, no. 18, art. no. 6809, 2020, doi: 10.3390/ijerph17186809.
- [7] M. Kruszyna, “Investment challenges pertaining to the achievement of the goals of the Mobility Policy based on the analysis of the results of traffic surveys in Wroclaw”, Archives of Civil Engineering, vol. 67, no. 3, pp. 505-523, 2021, doi: 10.24425/ACE.2021.138068.
- [8] M. Kruszyna and J. Makuch, “Mobility Nodes as an Extension of the Idea of Transfer Nodes - Solutions for Smaller Rail Stations with an Example from Poland”, Sustainability, vol. 15, no. 3, art. no. 2106, 2023, doi: 10.3390/su15032106.
- [9] M. Owais, A.S. Ahmed, G.S. Moussa, and A.A. Khalil, “Design scheme of multiple-subway lines for minimizing passengers transfers in mega-cities transit networks”, International Journal of Rail Transportation, vol. 9, no. 6, pp. 540-563, 2021, doi: 10.1080/23248378.2020.1846632.
- [10] C.L. Mumford, “New Heuristic and Evolutionary Operators for the Multi-Objective Urban Transit Routing Problem”, in IEEE Congress on Evolutionary Computation, 20-23 June 2013, Cancún, México. IEEE, 2013, doi: 10.1109/CEC.2013.6557668.
- [11] S.A. Bagloee and A. Ceder, “Transit-network design methodology for actual-size road networks”, Transportation Research Part B, vol. 45, no. 10, pp. 1787-1804, 2011, doi: 10.1016/j.trb.2011.07.005.
- [12] Q. Wu, C. Cole, and T. McSweeney, “Applications of particle swarm optimization in the railway domain”, International Journal of Rail Transportation, vol. 4, no. 3, pp. 167-190, 2016, doi: 10.1080/23248378.2016.1179599.
- [13] Y. Zhang, S. Wang, and G. Ji, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications”, Mathematical Problems in Engineering, vol. 2015, art. no 31256, 2015, doi: 10.1155/2015/931256.
- [14] H. Khudov, V. Koval, I. Khizhnyak, Y. Bridnia, V. Chepurnyi, S. Prykhodko, O. Oleksenko, S. Glukhov, and V. Savran, “The Method of Transport Logistics Problem Solving by the MAX-MIN ACO Algorithm”, International Journal of Emerging Technology and Advanced Engineering, vol. 12, no. 7, pp. 108-119, 2022, doi: 10.46338/ijetae0722_12.
- [15] Z. Zukhiri and I. V. Paputungan, “A hybrid optimization algorithm based on genetic algorithm and ant colony optimization”, International Journal of Artificial Intelligence & Applications, vol. 4, no. 5, 2013, doi: 10.5121/ijaia.2013.4505.
- [16] B. Hickish, D.I. Fletcher, and R.F. Harrison, “Investigating Bayesian Optimization for rail network optimization”, International Journal of Rail Transportation, vol. 8, no. 4, pp. 307-323, 2020, doi: 10.1080/23248378.2019.1669500.
- [17] R. Tang, L. De Donato, N. Bes¢inović F. Flammini, R.M.P. Goverde, Z. Lin, R. Liu, T. Tang, V. Vittorini, and Z. Wang, “A literature review of Artificial Intelligence applications in railway systems”, Transportation Research Part C, vol. 140, pp. 1-25, 2022, doi: 10.1016/j.trc.2022.103679.
- [18] C. Blum, “Ant colony optimization: Introduction and recent trends”, Physics of Life Reviews, vol. 2, no. 4, pp. 353-373, 2005, doi: 10.1016/j.plrev.2005.10.001.
- [19] R.M. Hasany and Y. Shafahi, “Ant colony optimisation for finding the optimal railroad path”, Transport, vol. 170, no. 4, pp. 218-230, 2017, doi: 10.1680/jtran.15.00038.
- [20] J.E. Bell and P.R. McMullen, “Ant colony optimization techniques for the vehicle routing problem”, Advanced Engineering Informatics, vol. 18, no. 1, pp. 41-48, 2004, doi: 10.1016/j.aei.2004.07.001.
- [21] B. Alt and U. Weidmann, “A stochastic multiple area approach for public transport network design”, Public Transport, vol. 3, pp. 65-87, 2011, doi: 10.1007/s12469-011-0042-0.
- [22] M. Pang, X. Wang, and L. Ma, “Transit route planning for megacities based on demand density of complex networks”, Promet – Traffic & Transportation, vol. 34, no. 1, pp. 13-23, 2022, doi: 10.7307/ptt.v34i1.3752.
- [23] Z.Yang, B.Yu, and C. Cheng, “A Parallel Ant Colony Algorithm for BusNetwork Optimization”, Computer-Aided Civil and Infrastructure Engineering, vol. 22, pp. 44-55, 2007, doi: 10.1111/j.1467-8667.2006.00469.x.
- [24] G. Katona, J. Juhász, and B. Lénárt, “Travel habit based multimodal route planning”, Transportation Research Procedia, vol. 27, pp. 301-308, 2017, doi: 10.1016/j.trpro.2017.12.121.
- [25] S.K. Sahana, A. Jain, and P.K. Mahanti, “Ant Colony Optimization for Train Scheduling: An Analysis”, International Journal of Intelligent Systems and Applications, vol. 6, no. 2, pp. 29-36, 2014, doi: 10.5815/ijisa.2014.02.04.
- [26] A. Mishra, N. Kumar, and S. Kharb, “Priority Based Train Scheduling Method Using ACO in Indian Railway Perspective”, Institute of Physic Conference Series: Materials Science and Engineering, vol. 998, art. no. 012016, 2020, doi: 10.1088/1757-899X/998/1/012016.
- [27] Z. Li and J. Huang, “How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis”, Sustainability, vol. 11, no. 4, pp. 1-15, 2019, doi: 10.3390/su11041140.
- [28] Y. Bin, Y. Zhong-Zhen, and Y. Baozhen, “An improved ant colony optimization for vehicle routing problem”, European Journal of Operational Research, vol. 196, no. 1, pp. 171-176, 2009, doi: 10.1016/j.ejor.2008.02.028.
- [29] L. Yongqiang, C. Qing, and X. Huagang, “An Improved Ant Colony Algorithm for the Time-Dependent Vehicle Routing Problem”, in International Conference on Logistics Engineering and Intelligent Transportation Systems. IEEE, 2010, doi: 10.1109/LEITS.2010.5665028.
- [30] M. Yousefikhoshbakht, F. Didehvar, F. Rahmati, and Z. Ahmed, “Fixed fleet open vehicle routing problem: Mathematical model and a modified ant colony optimization”, Bulletin of the Polish Academy of Sciences Technical Sciences, vol. 72, no. 1, 2024, doi: 10.24425/bpasts.2023.148253.
- [31] W. Liu, “Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization”, IEEE Access, vol. 8, pp. 12179-12187, 2020, doi: 10.1109/ACCESS.2020.2964328.
- [32] W. Zhang, X. Gong, G. Han, and Y. Zhao, “An Improved Ant Colony Algorithm for Path Planning in One Scenic Area With Many Spots", IEEE Access, vol. 5, pp. 13260-13269, 2017, doi: 10.1109/ACCESS.2017.2723892.
- [33] Y. Wei, N. Jiang, Z. Li, D. Zheng, M. Chen, and M. Zhang, “An Improved Ant Colony Algorithm for Urban Bus Network Optimization Based on Existing Bus Routes”, International Journal of Geo-Information, vol. 11, no. 5, art. no. 317, 2022, doi: 10.3390/ijgi11050317.
- [34] M. Korzeń and M. Kruszyna, “Modified ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section”, International Journal of Environmental Research and Public Health, vol. 20, no. 6, art. no. 4960, 2023, doi: 10.3390/ijerph20064960.
- [35] M. Dorigo and T. Stützle, “Ant Colony Optimization: Overview and Recent Advances”, in Handbook of Metaheuristics, M. Gendreau and J.Y. Potvin, Eds. International Series in Operations Research & Management Science, vol. 146. Springer, 2010, pp. 227-263, doi: 10.1007/978-1-4419-1665-5_8.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c19196cf-a861-4e50-8e6c-e2d3434ca948
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.