HYBRID ANFIS-GA-RL TRAFFIC SIGNAL CONTROL: A SIMULATION STUDY OF NIGERIAN CITIES

Authors

  • Dodo, Enoch Jacob Department of Information System and Technology Faculty of Computing, National Open University of Nigeria, Jabi, FCT Abuja, Nigeria Author
  • Onwodi, Gregory Department of Information System and Technology Faculty of Computing, National Open University of Nigeria, Jabi, FCT Abuja, Nigeria Author
  • Okure, Obot Department of Software Engineering Faculty of Computing, University of Uyo, Uyo, Akwa Ibom State, Nigeria Author

Abstract

Urban freshwater ecosystems are increasingly threatened by anthropogenic activities that alter habitat quality and environmental conditions, with important implications for amphibian communities. This study assessed the influence of habitat characteristics and physicochemical variables on the diversity and distribution of anurans at Jabi Lake, Abuja, Nigeria. A total of 257 anurans were investigated, and findings revealed four anuran families: Bufonidae, Pipidae, Dicroglossidae, and Ranidae, comprising five species across four genera namely: Sclerophrys regularis, Xenopus fischbergi, Xenopus tropicalis, Hoplobatrachus occipitalis, and Amnirana galamensis. Anuran species richness and evenness were notably higher during the wet season, with biodiversity indices peaking in the rainy months. Simpson’s index showed the least probability in August (0.2049) with an exponential increase of 0.5062 and 0.5556 in January and February. It was observed that Shannon-H of 0 in March and May (Dry season) indicate no evenness among the species during those months. However, this increased subsequently in the rainy season with the highest (1.597) observed in August. The CCA revealed that dissolved oxygen, water temperature, pH, and turbidity were the principal environmental variables influencing anuran distribution. Xenopus fischbergi and Amnirana galamensis were strongly associated with habitats characterized by elevated dissolved oxygen, lower pH, and cooler water, indicating relatively narrow ecological requirements. In contrast, Sclerophrys regularis exhibited greater tolerance to elevated temperature, higher pH, and moderate turbidity, reflecting considerable ecological plasticity and persistence under dry-season conditions. Hoplobatrachus occipitalis and Xenopus tropicalis showed comparatively weak associations with the measured physicochemical variables, suggesting broader ecological tolerances and responses to multiple interacting habitat factors.

References

Auwalu, F. K., & Bello, M. (2023). Exploring the contemporary challenges of urbanisation and the role of sustainable urban development: A study of Lagos City, Nigeria. Journal of Contemporary Urban Affairs, 7(1), 175–188. https://doi.org/10.25034/ijcua.2023.v7n1-12

Bangalee, K., & Ahmed, S. (2024). A fuzzy graph colouring and deep reinforcement learning based hybrid traffic control system for a four-way traffic intersection (SSRN Working Paper). https://doi.org/10.2139/ssrn.4879403

Chala, T. D., & Kóczy, L. T. (2024). Intelligent fuzzy traffic signal control system for complex intersections using fuzzy rule base reduction. Symmetry, 16(9), 1177. https://doi.org/10.3390/sym16091177

Data.gov. (2023). Traffic Signal Performance Measures (TSPM) dataset. U.S. Department of Transportation. https://catalog.data.gov/dataset

Dodo, E. J., Onwodi, G., Okure, O., & Isaac, T. (2025). Intelligent traffic optimisation system using ANFIS, genetic algorithms, and deep reinforcement learning: A systematic literature review. FUDMA Journal of Sciences, 9(12), 287–296. https://doi.org/10.33003/fjs-2025-0912-4160

Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.

Hu, Y., Wang, W., Jia, H., Wang, Y., Chen, Y., Hao, J., Wu, F., & Fan, C. (2020). Learning to utilise shaping rewards: A new approach of reward shaping. Advances in Neural Information Processing Systems, 33, 15931–15941. https://proceedings.neurips.cc/paper/2020/hash/b710915795b9e9c02cf10d6d2bdb688c-Abstract.html

Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541

Jutury, D., Kumar, N., Sachan, A., Daultani, Y., & Dhakad, N. (2023). Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport networks. Applied Intelligence, 53(4), 2763–2780. https://doi.org/10.1007/s10489-023-04178-5

Kurniawan, F., Agustian, H., Dermawan, D., Nurdin, R., Ahmadi, N., & Dinaryanto, O. (2025). Hybrid rule-based and reinforcement learning for urban signal control in developing cities: A systematic literature review and practice recommendations for Indonesia. Applied Sciences, 15(19), 10761. https://doi.org/10.3390/app151910761

Mirbakhsh, N., & Azizi, M. (2024). Adaptive traffic signal's safety and efficiency improvement by multi-objective deep reinforcement learning approach. International Journal of Innovative Research in Multidisciplinary Education, 3(7), 1245–1256. https://doi.org/10.58806/ijirme.2024.v3i7n10

Olusanya, O. O., Owosho, Y., Daniyan, I., Elegbede, A. W., Sodipo, Q. B., Adeodu, A., Phuluwa, H. S., Ramasu, T. K., & Kana-Kana Katumba, M. G. (2025). Multi-agent reinforcement learning framework for autonomous traffic signal control in smart cities. Frontiers in Mechanical Engineering, 11, Article 1650918. https://doi.org/10.3389/fmech.2025.1650918

Olayode, I. O., Severino, A., Tartibu, L. K., Arena, F., & Cakici, Z. (2022). Performance evaluation of a hybrid PSO enhanced ANFIS model in prediction of traffic flow of vehicles on freeways: Traffic data evidence from South Africa. Infrastructures, 7(1), 2. https://doi.org/10.3390/infrastructures7010002

Olayode, I. O., Tartibu, L. K., & Alex, F. J. (2023). Comparative study analysis of ANFIS and ANFIS-GA models on flow of vehicles at road intersections. Applied Sciences, 13(2), 744. https://doi.org/10.3390/app13020744

Otuoze, S. H., Hunt, D. V. L., & Jefferson, I. (2021). Neural network approach to modelling transport system resilience for major cities: Case studies of Lagos and Kano (Nigeria). Sustainability, 13(3), 1371. https://doi.org/10.3390/su13031371

Our World in Data. (2025). Death rate from road injuries [Data set]. Adapted from Institute for Health Metrics and Evaluation, Global Burden of Disease (2025). https://ourworldindata.org/grapher/death-rates-road-incidents

Paul, A., & Mitra, S. (2022). Exploring reward efficacy in traffic management using deep reinforcement learning in intelligent transportation system. ETRI Journal, 44(2), 194–207. https://doi.org/10.4218/etrij.2021-0404

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. http://incompleteideas.net/book/the-book-2nd.html

Transportation Research Board. (2022). Highway capacity manual: A guide for multimodal mobility analysis (7th ed.). National Academies Press. https://doi.org/10.17226/26432

Udofia, K. M. (2019). ANFIS-based intelligent traffic signal control of two interconnected junctions. Science and Technology Publishing (SCI & TECH), 3(7), 337–343. https://www.scitechpub.org/wp-content/uploads/2020/09/SCITECHP420092..pdf

United Nations, Department of Economic and Social Affairs, Population Division. (2024). World population prospects 2024: Summary of results (UN DESA/POP/2024/TR/NO. 4). https://population.un.org/wpp/

Wang, B., He, Z., Sheng, J., & Chen, Y. (2022). Deep reinforcement learning for traffic light timing optimisation. Processes, 10(11), 2458. https://doi.org/10.3390/pr10112458

World Health Organization. (2023). Global status report on road safety 2023. WHO. https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023

Yang, G., Wen, X., & Chen, F. (2025). Multi-agent deep reinforcement learning with graph attention network for traffic signal control in multiple-intersection urban areas. Transportation Research Record: Journal of the Transportation Research Board, 2679(4), 880–898. https://doi.org/10.1177/03611981241297979

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Published

2026-07-26

How to Cite

HYBRID ANFIS-GA-RL TRAFFIC SIGNAL CONTROL: A SIMULATION STUDY OF NIGERIAN CITIES. (2026). Impact International Journals and Publications, 2(ISSUE 3), 451-470. https://impactinternationaljournals.com/publications/index.php/ojs/article/view/625

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