Traffic Forecast Based on Statistical Data for Public Transport Optimization in Real Time
(*) Corresponding author
The intellectualization of transportation system is a relevant task for solving traffic-related problems such as vehicle traffic management and modern transport system improvement. The purpose of this work was to design a competitive pathway for real transport system to optimize the route planning. Modeling of the transport system refers to the problem of finding the K-shortest sustainable pathway in a multimodal network. The solution to this problem was fulfilled by applying a hybrid algorithm of an ant colony based on a differential evolution approach to the pheromone renewal and the division of the colony into teams. Experimental results showed that an advanced ants colony algorithm is highly efficient even with quite a small population of the colony. The obtained results were compared with those of the conventional ant colony algorithm. Due to its high efficiency, the elaborated method is applicable in improving the quality of the entire road network, especially in congestions and traffic jams, taking into account real-time traffic information.
Copyright © 2020 Praise Worthy Prize - All rights reserved.
Zimmermann, M., Mai, T., & Frejinger, E. (2017). Bike route choice modeling using GPS data without choice sets of paths. Transportation research part C: emerging technologies, 75, 183-196.
De Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., & Villas, L. A. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4), 1550147716683612.
Mavrovouniotis, M., Li, C., Ellinas, G., & Polycarpou, M. (2019, December). Parallel Ant Colony Optimization for the Electric Vehicle Routing Problem. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1660-1667). IEEE.
D. Gómez, J.F. Martínez, J. Sendra, G. Rubio, Development of a decision making algorithm for traffic jams reduction applied to intelligent transportation systems, Journal of Sensors, Vol. 1: 9271986, 2016.
K.L. Soon, J.M.Y. Lim, R. Parthiban, Coordinated Traffic Light Control in Cooperative Green Vehicle Routing for Pheromone-based Multi-Agent Systems, Applied Soft Computing, Vol. 81: 105486, 2019.
Hamidi, H., & Kamankesh, A. (2018). An approach to intelligent traffic management system using a multi-agent system. International Journal of Intelligent Transportation Systems Research, 16(2), 112-124.
B. Yang, Y.A. Chen, Evolution model and simulation of logistics outsourcing for manufacturing enterprises based on multi-agent modeling, Cluster Computing, Vol. 22(Issue 3): 6807-6815, 2019.
Sharma, U., & Aggarwal, S. (2018). Solving fully fuzzy multi-objective linear programming problem using nearest interval approximation of fuzzy number and interval programming. International Journal of Fuzzy Systems, 20(2), 488-499.
R. Abduljabbar, H. Dia, S. Liyanage, S.A. Bagloee, Applications of artificial intelligence in transport: An overview, Sustainability, Vol.11(Issue 1):189, 2019.
Yan, R., Jackson, L. M., & Dunnett, S. J. (2017). Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach. The International Journal of Advanced Manufacturing Technology, 92(5-8), 1825-1837.
Y.J. Gong, E. Chen, X. Zhang, L.M. Ni, J. Zhang, AntMapper: An ant colony-based map matching approach for trajectory-based applications, IEEE Transactions on Intelligent Transportation Systems, Vol. 19(Issue 2): 390-401, 2017.
Masoumi, Z., Van Genderen, J., & Sadeghi Niaraki, A. (2019). An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments. Geocarto International, 1-18.
Yu, C., Shen, Z., & Li, P. (2020). Route Optimization of Aquatic Product Transportation Based on an Improved Ant Colony Algorithm. Journal of Advanced Computational Intelligence and Intelligent Informatics, 24(4), 488-493.
F. Morlock, B. Rolle, M. Bauer and O. Sawodny, Time Optimal Routing of Electric Vehicles Under Consideration of Available Charging Infrastructure and a Detailed Consumption Model, in IEEE Transactions on Intelligent Transportation Systems.
K.K.H. Ng, C.K.M. Lee, S.Z. Zhang, K. Wu, W. Ho, A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion, Computers & Industrial Engineering, Vol. 109: 151-168, 2017.
P. Shunmugapriya, S. Kanmani, P.J. Fredieric, U. Vignesh, J.R. Justin, K. Vivek, Effects of Introducing Similarity Measures into Artificial Bee Colony Approach for Optimization of Vehicle Routing Problem, International Journal of Computer and Information Engineering, Vol. 10(Issue 3): 651-658, 2016.
B.Y. Chen, X.W. Chen, H.P. Chen, W.H. Lam, Efficient algorithm for finding k shortest paths based on re-optimization technique, Transportation Research Part E: Logistics and Transportation Review, Vol. 133: 101819, 2020.
W. Huang, Y. Zhang, Z. Shang, J.X. Yu, To meet or not to meet: finding the shortest paths in road networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 30(Issue 4): 772-785, 2017.
Idri, A., Oukarfi, M., Boulmakoul, A., Zeitouni, K., & Masri, A. (2017). A distributed approach for shortest path algorithm in dynamic multimodal transportation networks. Transportation Research Procedia, 27, 294-300.
López, D., & Lozano, A. (2020). Shortest hyperpaths in a multimodal hypergraph with real-time information on some transit lines. Transportation Research Part A: Policy and Practice, 137, 541-559.
Dib, O., Manier, M. A., Moalic, L., & Caminada, A. (2017). A multimodal transport network model and efficient algorithms for building advanced traveler information systems. Transportation Research Procedia, 22, 134-143.
M. Haqqani, X. Li, X. Yu, An evolutionary multi-criteria journey planning algorithm for multimodal transportation networks, Australasian Conference on Artificial Life and Computational Intelligence (Springer, Cham, 2017, pp. 144-156).
Zhao, T., Wang, J., & Zhang, J. (2017). Real-time multimodal transport path planning based on a pulse neural network model. International Journal of Simulation and Process Modelling, 12(3-4), 356-361.
J. Ning, Q. Zhang, C. Zhang, B. Zhang, A best-path-updating information-guided ant colony optimization algorithm, Information Sciences, Vol. 433: 142-162, 2018.
S. Das, S.S. Mullick, P.N. Suganthan, Recent advances in differential evolution–an updated survey, Swarm and Evolutionary Computation, Vol. 27: 1-30, 2016.
Mirjalili, S., Dong, J. S., & Lewis, A. (2020). Ant Colony optimizer: theory, literature review, and application in AUV path planning. In Nature-Inspired Optimizers (pp. 7-21). Springer, Cham.
Tseng, H. E., Chang, C. C., Lee, S. C., & Huang, Y. M. (2019). Hybrid bidirectional ant colony optimization (hybrid BACO): An algorithm for disassembly sequence planning. Engineering Applications of Artificial Intelligence, 83, 45-56.
H. Yu, P. Liu, R. Ma, L. Bai, Performance evaluation of integrated strategy of vehicle route guidance and traffic signal control using traffic simulation, IET Intelligent Transport Systems, Vol. 12(Issue 7): 696-702, 2018.
Rego, A., Garcia, L., Sendra, S., & Lloret, J. (2018). Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities. Future Generation Computer Systems, 88, 243-253.
Gunantara, N., Nurweda Putra, I., Antara, I., The Characteristics of Multi-Criteria Weight on Ad-Hoc Network with Ant Colony Optimization, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (4), pp. 249-256.
Prasetyono, E., Mohammad, L., Dwi Murdianto, F., Performance of ACO-MPPT and Constant Voltage Method for Street Lighting Charging System, (2020) International Review of Electrical Engineering (IREE), 15 (3), pp. 235-244.
Mesleh, A., Battery Power Clustering Using Ant Colony Optimization, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (1), pp. 62-70.
Jhajj, H., Garg, R., Saluja, N., Efficient Spectrum Sensing in Cognitive Radio Networks Using Hybridized Particle Swarm Intelligence and Ant Colony Algorithm, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 586-593.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize