Open Access Open Access  Restricted Access Subscription or Fee Access

A New Hybrid Multicriteria Approach Using Fuzzy Graph Controller and Dijkstra’s Algorithm for Urban Traffic Congestion


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/ireaco.v14i5.20502

Abstract


This paper presents a new multi-criteria approach to solve the problem of urban traffic congestion, which is based on a hybridization of the Dijkstra’s algorithm, and a fuzzy logic based controller (FLC-DA). In fact, this study is based on complete modeling of a road network by a graph, where the nodes represent the intersections and the edges represent the roads. Thus, the fuzzy controller generates at each moment the edge’s weights based on five inputs criteria. These latter are the information coming from the sensors representing the distance of the road, the number of vehicles on this road, the presence of accidents, the presence of the public works between two intersections, and the maximum speed allowed by the Highway Code. The generated weights are processed by the most optimal path calculation system adopting the Dijkstra’s algorithm. The proposed algorithm is compared to the Bellman-Ford Algorithm (BFA) in terms of accuracy and complexity to justify the choice of the Dijkstra Optimal Pathfinder designed (DOPF). The obtained results demonstrate the influence of each entry criterion on the generated weight. Then, the comparison shows that FLC-DA is less complex compared to BFA method applied to real graph studies chosen from the literature.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords


Dijkstra Algorithm; Fuzzy Logic; Multicriteria Study; Urban Traffic; Urban Congestion

Full Text:

PDF


References


X. Zheng, W. Peng, and M. Hu, Airport noise and house prices: A quasi-experimental design study, Land use policy, vol. 90, no. September 2019, p. 104287, 2020.
https://doi.org/10.1016/j.landusepol.2019.104287

K. Nellore and G. P. Hancke, A survey on urban traffic management system using wireless sensor networks, Sensors (Switzerland), vol. 16, no. 2. MDPI AG, Jan. 27, 2016.
https://doi.org/10.3390/s16020157

C. El Hatri and J. Boumhidi, Fuzzy deep learning based urban traffic incident detection, Cogn. Syst. Res., vol. 50, pp. 206-213, Aug. 2018.
https://doi.org/10.1016/j.cogsys.2017.12.002

O. Alzamzami and I. Mahgoub, Fuzzy logic-based geographic routing for urban vehicular networks using link quality and achievable throughput estimations, IEEE Trans. Intell. Transp. Syst., vol. 20, no. 6, pp. 2289-2300, Jun. 2019.
https://doi.org/10.1109/TITS.2018.2867177

H. Yin, W. Zhou, M. Li, C. Ma and C. Zhao, An Adaptive Fuzzy Logic-Based Energy Management Strategy on Battery/Ultracapacitor Hybrid Electric Vehicles, in IEEE Transactions on Transportation Electrification, vol. 2, no. 3, pp. 300-311, Sept. 2016.
https://doi.org/10.1109/TTE.2016.2552721

El Kari, B., Ayad, H., El Kari, A., Mjahed, M., Pozna, C., Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic, (2019) International Review of Automatic Control (IREACO), 12 (1), pp. 1-10.
https://doi.org/10.15866/ireaco.v12i1.14802

L. Qi, M. C. Zhou, and W. J. Luan, A Two-level Traffic Light Control Strategy for Preventing Incident-Based Urban Traffic Congestion, IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 13-24, Jan. 2018.
https://doi.org/10.1109/TITS.2016.2625324

Y. Idel Mahjoub, E. houcine Chakir El-Alaoui, and A. Nait-Sidi-Moh, Modeling and developing a conflict-aware scheduling in urban transportation networks, Futur. Gener. Comput. Syst., vol. 107, pp. 1026-1036, 2020.
https://doi.org/10.1016/j.future.2018.04.022

M. Xu, K. An, L. H. Vu, Z. Ye, J. Feng, and E. Chen, Optimizing multi-agent based urban traffic signal control system, J. Intell. Transp. Syst. Technol. Planning, Oper., vol. 23, no. 4, pp. 357-369, Jul. 2019.
https://doi.org/10.1080/15472450.2018.1501273

H. Zhang, X. Liu, H. Ji, Z. Hou, and L. Fan, Multi-agent-based data-driven distributed adaptive cooperative control in urban traffic signal timing, Energies, vol. 12, no. 7, 2019.
https://doi.org/10.3390/en12071402

M. R. Islam, N. I. Shahid, D. T. Ul Karim, A. Al Mamun, and M. K. Rhaman, An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system, Int. Conf. Adv. Commun. Technol. ICACT, vol. 2016-March, pp. 802-807, 2016.
https://doi.org/10.1109/ICACT.2016.7423566

R. Wagh, K. Karande, and T. Sayyed, A Genetic Algorithm Based Approach To Solve Carpool Service Problems in Cloud Computing, Int. J. Adv. Eng. Res. Dev., vol. 3, no. 01, pp. 78-84, 2016.
https://doi.org/10.21090/IJAERD.C73

L. Qi, M. Zhou, and W. Luan, A dynamic road incident information delivery strategy to reduce urban traffic congestion, IEEE/CAA J. Autom. Sin., vol. 5, no. 5, pp. 934-945, Sep. 2018
https://doi.org/10.1109/JAS.2018.7511165

C. Guo, K. Kidono, R. Terashima, and Y. Kojima, Humanlike Behavior Generation in Urban Environment Based on Learning-Based Potentials With a Low-Cost Lane Graph, IEEE Trans. Intell. Veh., vol. 3, no. 1, pp. 46-60, Jan. 2018.
https://doi.org/10.1109/TIV.2017.2788194

T. N. Chuang and J. Y. Kung, A new algorithm for the discrete fuzzy shortest path problem in a network, Appl. Math. Comput., vol. 174, no. 1, pp. 660-668, 2006.
https://doi.org/10.1016/j.amc.2005.04.097

I. Mahdavi, R. Nourifar, A. Heidarzade, and N. M. Amiri, A dynamic programming approach for finding shortest chains in a fuzzy network, Appl. Soft Comput. J., vol. 9, no. 2, pp. 503-511, 2009.
https://doi.org/10.1016/j.asoc.2008.07.002

M. Ghatee and S. M. Hashemi, Application of fuzzy minimum cost flow problems to network design under uncertainty, Fuzzy Sets Syst., vol. 160, no. 22, pp. 3263-3289, 2009.
https://doi.org/10.1016/j.fss.2009.04.004

C. P. Pappis and E. H. Mamdani, A Fuzzy Logic Controller for a Trafc Junction, IEEE Trans. Syst. Man. Cybern., vol. 7, no. 10, pp. 707-717, 1977.
https://doi.org/10.1109/TSMC.1977.4309605

D. Teodorović, Fuzzy logic systems for transportation engineering: The state of the art, Transp. Res. Part A Policy Pract., vol. 33, no. 5, pp. 337-364, 1999.
https://doi.org/10.1016/S0965-8564(98)00024-X

W. Zhang, B. Wu, and W. Liu, Anti-Congestion Fuzzy Algorithm for Traffic Control of a Class of Traffic Networks, in 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007, p. 124.
https://doi.org/10.1109/GrC.2007.138

A. S. Tomar, M. Singh, G. Sharma, and K. V Arya, Traffic management using logistic regression with fuzzy logic, Procedia Comput. Sci., vol. 132, pp. 451-460, 2018.
https://doi.org/10.1016/j.procs.2018.05.159

Mahesa, R., Yudoko, G., Anggoro, Y., Intelligent Traffic Monitoring System as Part of Realizing Smart City-A Case Study: Traffic Engineering in Northern Jakarta's Mixed-Use Area, (2020) International Review of Civil Engineering (IRECE), 11 (4), pp. 188-197.
https://doi.org/10.15866/irece.v11i4.18087

Krasniqi, R., Doci, I., Shala, A., Berisha, R., Regulation of Traffic Flow in Small Cities with High Number of Vehicles: Case of Malisheva City - Kosovo, (2018) International Review of Civil Engineering (IRECE), 9 (4), pp. 161-167.
https://doi.org/10.15866/irece.v9i4.14300

Naumova, N., Naumov, R., Method of Solving Some Optimization Problems for Dynamic Traffic Flow Distribution, (2018) International Review on Modelling and Simulations (IREMOS), 11 (4), pp. 245-251.
https://doi.org/10.15866/iremos.v11i4.13701

S. Broumi, A. Bakal, M. Talea, F. Smarandache, and L. Vladareanu, Applying Dijkstra algorithm for solving neutrosophic shortest path problem, in 2016 International Conference on Advanced Mechatronic Systems (ICAMechS), 2016, pp. 412-416.
https://doi.org/10.1109/ICAMechS.2016.7813483

A. Cianfrani, V. Eramo, M. Listanti, M. Marazza, and E. Vittorini, An energy saving routing algorithm for a green OSPF protocol, Proc. - IEEE INFOCOM, 2010.
https://doi.org/10.1109/INFCOMW.2010.5466646

M. Enayattabar, A. Ebrahimnejad, and H. Motameni, Dijkstra algorithm for shortest path problem under interval-valued Pythagorean fuzzy environment, Complex Intell. Syst., vol. 5, no. 2, pp. 93-100, 2019.
https://doi.org/10.1007/s40747-018-0083-y

O. V. Gnana Swathika and S. Hemamalini, Prims-Aided Dijkstra Algorithm for Adaptive Protection in Microgrids, IEEE J. Emerg. Sel. Top. Power Electron., vol. 4, no. 4, pp. 1279-1286, 2016.
https://doi.org/10.1109/JESTPE.2016.2581986

G. Qing, Z. Zheng, and X. Yue, Path-planning of automated guided vehicle based on improved Dijkstra algorithm, Proc. 29th Chinese Control Decis. Conf. CCDC 2017, pp. 7138-7143, 2017.
https://doi.org/10.1109/CCDC.2017.7978471

Y. Deng, Y. Chen, Y. Zhang, and S. Mahadevan, Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment, Appl. Soft Comput. J., vol. 12, no. 3, pp. 1231-1237, 2012.
https://doi.org/10.1016/j.asoc.2011.11.011

Ourabah, L., El Kari, B., Labriji, E., Fuzzy Graph-Based Controller for a Real-Time Urban Traffic Optimization, (2020) International Review on Modelling and Simulations (IREMOS), 13 (5), pp. 354-361.
https://doi.org/10.15866/iremos.v13i5.18117

X. Huang, Y. Zhao, C. Ma, J. Yang, X. Ye, and C. Zhang, TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data, IEEE Trans. Vis. Comput. Graph., vol. 22, no. 1, pp. 160-169, Jan. 2016.
https://doi.org/10.1109/TVCG.2015.2467771

F. Busato and N. Bombieri, An efficient implementation of the bellman-ford algorithm for kepler GPU architectures, IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 8, pp. 2222-2233, 2016.
https://doi.org/10.1109/TPDS.2015.2485994

S. Jukna and G. Schnitger, On the optimality of Bellman-Ford-Moore shortest path algorithm, Theor. Comput. Sci., vol. 628, pp. 101-109, 2016.
https://doi.org/10.1016/j.tcs.2016.03.014

D. A. Bader, H. Meyerhenke, P. Sanders, and D. Wagner, 10th DIMACS Implementation Challenge-Graph Partitioning and Graph Clustering. 2011.

C. Demetrescu, A. V Goldberg, and D. S. Johnson, The Shortest Path Problem: Ninth DIMACS Implementation Challenge, vol. 74. American Mathematical Soc., 2009.
https://doi.org/10.1090/dimacs/074

T. A. Davis and Y. Hu, The University of Florida sparse matrix collection, ACM Trans. Math. Softw., vol. 38, no. 1, pp. 1-25, 2011.
https://doi.org/10.1145/2049662.2049663

J. Leskovec and R. Sosič, Snap: A general-purpose network analysis and graph-mining library, ACM Trans. Intell. Syst. Technol., vol. 8, no. 1, pp. 1-20, 2016.
https://doi.org/10.1145/2898361


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize