AccScience Publishing / IJOSI / Volume 9 / Issue 3 / DOI: 10.6977/IJoSI.202506_9(3).0003
Cite this article
3
Download
14
Citations
43
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
ARTICLE

Hybrid intelligence model for traffic management in intelligent transportation systems

Impana Appaji1,2 Pandian Raviraj3*
Show Less
1 GSSS Institute of Engineering and Technology for Women, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India
2 Infosys Limited, Mysuru, Karnataka, India
3 Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India
Submitted: 13 January 2024 | Revised: 16 March 2024 | Accepted: 16 July 2024 |
© 2025 by the Publisher. Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

A typical traffic environment in an intelligent transportation system (ITS) involves various infrastructural units that generate a vast amount of sophisticated traffic data. Such a form of complex data is challenging to analyze and hence poses a potential issue in designing an effective and responsive traffic management system. Therefore, this paper develops an analytical modeling approach to harness the potential of artificial intelligence and computational intelligence. The scheme presents a simplified predictive approach that is meant to mitigate current issues and promote intelligent traffic management. The simulated outcome of the study showcases that the proposed scheme offers a significant advantage in its predictive performance in ITS.

Keywords
Artificial Intelligence
Computational Intelligence Technologies
Intelligent Transportation System
Traffic Management
References

Akhtar, M., & Moridpour, S. (2021). A review of traffic congestion prediction using artificial intelligence. Journal of Advanced Transportation, 2021, 1-18. https://doi.org/10.1155/2021/8878011

 

Barodi, A., Zemmouri, A., Bajit, A., Benbrahim, M., & Tamtaoui, A. (2023). Intelligent transportation system based on smart soft-sensors to analyze road traffic and assist driver behavior applicable to smart cities. Microprocessors and Microsystems, 100, 104830. https://doi.org/10.1016/j.micpro.2023.104830

 

Benterki, A., Boukhnifer, M., Judalet, V., & Maaoui, C. (2020). Artificial intelligence for vehicle behavior anticipation: Hybrid approach based on maneuver classification and trajectory prediction. IEEE Access, 8, 56992–57002. https://doi.org/10.1109/access.2020.2982170

 

Bıyık, C., & Yigitcanlar, T. (2020). Intelligent transport systems in Turkish urban environments: a comprehensive review. International Journal of Knowledge-Based Development, 11(4), 382. https://doi.org/10.1504/ijkbd.2020.115037

 

Chavhan, S., Gupta, D., Chidambaram, R.K., Khanna, A., & Rodrigues, J.J.P.C. (2021). A novel emergent intelligence technique for public transport vehicle allocation problem in a dynamic transportation system. IEEE IEEE Transactions on Intelligent Transportation Systems, 22(8), 5389–5402. https://doi.org/10.1109/tits.2020.3011198

 

Chen, Z.G., Zhan, Z.H., Kwong, S., & Zhang, J. (2022). Evolutionary computation for intelligent transportation in smart cities: A survey [review article]. IEEE Computational Intelligence Magazine, 17(2), 83–102. https://doi.org/10.1109/mci.2022.3155330

 

Chowdhury, A., Kaisar, S., Khoda, M.E., Naha, R., Khoshkholghi, M.A., & Aiash, M. (2023). IoT-based emergency vehicle services in Intelligent transportation system. Sensors (Basel, Switzerland), 23(11), 5324. https://doi.org/10.3390/s23115324

 

Damaj, I., Al Khatib, S.K., Naous, T., Lawand, W., Abdelrazzak, Z.Z., & Mouftah, H.T. (2022). Intelligent transportation systems: A survey on modern hardware devices for the era of machine learning. Journal of King Saud University-Computer and Information Sciences, 34(8), 5921–5942. https://doi.org/10.1016/j.jksuci.2021.07.020

 

Eom, M., & Kim, B.I. (2020). The traffic signal control problem for intersections: A review. European Transport Research Review, 12(1), 50. https://doi.org/10.1186/s12544-020-00440-8

 

Haghighat, A.K., Ravichandra-Mouli, V., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. (2020). Applications of deep learning in intelligent trans- portation systems. Journal of Big Data Analytics in Transportation, 2(2), 115–145. https://doi.org/10.1007/s42421-020-00020-1

 

Husnain, G., Anwar, S., Sikander, G., Ali, A., & Lim, S. (2023). A bio-inspired cluster optimization schema for efficient routing in vehicular ad hoc networks (VANETs). Energies, 16(3), 1456. https://doi.org/10.3390/en16031456

 

Iliopoulou, C., & Kepaptsoglou, K. (2019). Combining ITS and optimization in public transportation planning: state of the art and future research paths. European Transport Research Review, 11(1), 27. https://doi.org/10.1186/s12544-019-0365-5

 

Jain, R., Chakravarthi, M.K., Kumar, P.K., Hemake-Savulu, O., Ramirez-Asis, E., Pelaez-Diaz, G., et al. (2022). Internet of Things-based smart vehicles design of bio-inspired algorithms using artificial intelligence charging system. Nonlinear Engineering, 11(1), 582–589. https://doi.org/10.1515/nleng-2022-0242

 

Karthikeyan, H., & Usha, G. (2023). A secured IoT-based intelligent transport system (IoT-ITS) framework based on cognitive science. Soft Computing, 28, 13929–13939. https://doi.org/10.1007/s00500-023-08410-7

 

Kołodziej, J., Hopmann, C., Coppa, G., Grzonka, D., & Widłak, A. (2022). Intelligent transportation systems-models, challenges, security aspects. In: Cybersecurity of Digital Service Chains. Berlin: Springer International Publishing. pp56–82.

 

Kumari, S., Kumari, S., Vikram, V., Kumari, S., & Gouda, S.K. (2020). Smart traffic management system using IoT and machine learning approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3647656

 

Lee, S., Lim, D.E., Kang, Y., & Kim, H.J. (2021). Clustered multi-task sequence-to-sequence learning for autonomous vehicle repositioning. IEEE Access, 9, 14504–14515. https://doi.org/10.1109/access.2021.3051763

 

Li, C., Zhao, H., Huang, K., & Chen, Y.H. (2021). Optimal design for anti-skid control of electric vehicles by fuzzy approach. Chinese Journal of Me-Chanical Engineering, 34(1), 125. https://doi.org/10.1186/s10033-021-00642-8

 

Li, S., Shu, K., Chen, C., & Cao, D. (2021). Planning and decision-making for connected autonomous vehicles at road intersections: A review. Chinese Journal of Mechanical Engineering, 34(1), 133. https://doi.org/10.1186/s10033-021-00639-3

 

Liu, Y., Xu, Q., Guo, H., & Zhang, H. (2022). A type-2 fuzzy approach to driver-automation shared driving lane keeping control of semi-autonomous vehicles under imprecise premise variable. Chinese Journal of Mechanical Engineering, 35(1), 46. https://doi.org/10.1186/s10033-022-00706-3

 

Lu, Y., Lang, M., Sun, Y., & Li, S. (2020). A fuzzy intercontinental road-rail multimodal routing model with time and train capacity uncertainty and fuzzy programming approaches. IEEE Access, 8, 27532–27548. https://doi.org/10.1109/access.2020.2971027

 

Lytras, M.D., Chui, K.T., & Liu, R.W. (2020). Moving towards intelligent transportation via artificial intelligence and internet-of-things. Sensors (Basel, Switzerland), 20(23), 6945. https://doi.org/10.3390/s20236945

 

Malek, Y. N., Najib, M., Bakhouya, M., & Essaaidi, M. (2021). Multivariate deep learning approach for electric vehicle speed forecasting. Big Data Min. Anal., 4(1), 56–64. https://doi.org/10.26599/bdma.2020.9020027

 

Mohammadi, S., & Farahani, G. (2020). Computational intelligence-based connectivity restoration in wireless sensor and actor networks. EURASIP Journal on Wireless Communications and Networking, 2020(1), 198. https://doi.org/10.1186/s13638-020-01831-0

 

Njoku, J.N., Nwakanma, C.I., Amaizu, G.C., & Kim, D.S. (2023). Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems. IET Intelligent Transport Systems, 17(1), 1–21. https://doi.org/10.1049/itr2.12252

 

Ojala, R., Vepsalainen, J., Hanhirova, J., Hirvisalo, V., & Tammi, K. (2020). Novel convolutional neural network-based roadside unit for accurate pedestrian localisation. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3756–3765. https://doi.org/10.1109/tits.2019.2932802

 

Qadri, S.S.S.M., Gökçe, M.A., & Öner, E. (2020). State-of-art review of traffic signal control methods: Challenges and opportunities. European Transport Research Review, 12(1), 1-23. https://doi.org/10.1186/s12544-020-00439-1

 

Rajkumar, S.C., & Deborah, L.J. (2021). An improved public transportation system for effective usage of vehicles in intelligent transportation system. International Journal of Communication Systems, 34(13), e4910. https://doi.org/10.1002/dac.4910

 

Sayed, S. A., Abdel-Hamid, Y., & Hefny, H.A. (2023). Artificial intelligence-based traffic flow prediction: A comprehensive review. Journal of Electrical Systems and Information Technology, 10(1), 13. https://doi.org/10.1186/s43067-023-00081-6

 

Servizi, V., Pereira, F.C., Anderson, M.K., & Nielsen, O.A. (2021). Transport behavior-mining from smartphones: A review. European Transport Research Review, 13(1), 57. https://doi.org/10.1186/s12544-021-00516-z

 

Severino, A., Curto, S., Barberi, S., Arena, F., & Pau, G. (2021). Autonomous Vehicles: An analysis both on their distinctiveness and the potential impact on urban transport systems. Applied Sciences (Basel, Switzerland), 11(8), 3604. https://doi.org/10.3390/app11083604

 

Shaaban, K., Elamin, M., & Alsoub, M. (2021). Intelligent transportation systems in a developing country: Benefits and challenges of implementation. Transportation Research Procedia, 55, 1373–1380. https://doi.org/10.1016/j.trpro.2021.07.122

 

Sharma, V., Kumar, L., & Sergeyev, S. (2021). Recent developments and challenges in intelligent transportation systems (ITS)-A survey. In: Algorithms for Intelligent Systems. Berlin: Springer Singapore. pp37–44.

 

Shi, Q., Wang, M., He, Z., Yao, C., Wei, Y., & He, L. (2022). A fuzzy-based sliding mode control approach for acceleration slip regulation of battery electric vehicle. Chinese Journal of Mechanical Engineering, 35(1), 72. https://doi.org/10.1186/s10033-022-00729-w

 

Simić, V., Lazarević, D., & Dobrodolac, M. (2021). Picture fuzzy WASPAS method for selecting last-mile delivery mode: A case study of Belgrade. European Transport Research Review, 13(1), 43. https://doi.org/10.1186/s12544-021-00501-6

 

Tak, S., Lee, J.D., Song, J., & Kim, S. (2021). Development of AI-based vehicle detection and tracking system for C-ITS application. Journal of Advanced Transportation, 2021, 1–15. https://doi.org/10.1155/2021/4438861

 

Tang, J., Zhang, X., Yin, W., Zou, Y., & Wang, Y. (2021). Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory. Journal of Intelligent Transportation Systems, 25(5), 439–454. https://doi.org/10.1080/15472450.2020.1713772

 

Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., et al. (2019). CoLight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management.

 

Zhou, F., Yang, Q., Zhong, T., Chen, D., & Zhang, N.(2021). Variational graph neural networks for road traffic prediction in intelligent transportation systems. IEEE Transactions on Industrial Informatics, 17(4), 2802–2812. https://doi.org/10.1109/tii.2020.3009280

 

Zulkarnain, & Putri, T.D. (2021). Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach. Heliyon, 7(12), e08615. https://doi.org/10.1016/j.heliyon.2021.e08615

 

Share
Back to top
International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing