International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 11, Issue 10 (October 2024), Pages: 166-175

----------------------------------------------

 Original Research Paper

Enhancing traffic flow and congestion management in smart cities utilizing SVM-based linear regression approach

 Author(s): 

 Shahzada Atif Naveed 1, Umer Farooq 2, Muhammad Asan Raza 3, Zia Ur Rehman 4, Muhammad Saleem 5, *, Taher M. Ghazal 6, 7

 Affiliation(s):

 1Department of Computer Science, National College of Business Administration and Economics, Rahim Yar Khan, Pakistan
 2Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
 3Department of Information Sciences, University of Education, Lahore, Pakistan
 4Department of Computer Science, Government College University, Lahore, Pakistan
 5School of Computer Science, Minhaj University Lahore, Lahore, Pakistan
 6Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
 7Applied Science Research Center, Applied Science Private University, Amman, Jordan

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5209-8375

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.10.019

 Abstract

With the development of smart cities, it is essential to monitor traffic flow and manage congestion effectively to ensure smooth movement for people and address their social and economic needs. As these needs continue to change, roadside infrastructure faces challenges in meeting the demands of citizens in smart cities. Traffic congestion is a major issue in road networks and occurs when the number of vehicles exceeds the capacity of the roads. Emerging technologies like Vehicular Networks (VN) and Support Vector Machine (SVM)-based linear regression offer promising solutions for vehicle-to-vehicle communication and managing autonomous roadside infrastructure. SVM-based linear regression is a well-known and effective method for addressing various issues related to roadside infrastructure, traffic management, data integration, analytics, and environmental monitoring. The main goal of using SVM-based linear regression in this research is to help citizens and city authorities make informed decisions and better understand and control traffic. This study demonstrates the application of SVM-based linear regression in integrating autonomous roadside infrastructure, achieving a high accuracy rate of 92% and reducing errors by 8%, showing a notable improvement compared to previous methods.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Smart cities, Traffic flow monitoring, Congestion management, SVM-based linear regression, Vehicular network

 Article history

 Received 12 September 2023, Received in revised form 22 February 2024, Accepted 6 October 2024

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Naveed SA, Farooq U, Raza MA, Rehman ZU, Saleem M, and Ghazal TM (2024). Enhancing traffic flow and congestion management in smart cities utilizing SVM-based linear regression approach. International Journal of Advanced and Applied Sciences, 11(10): 166-175

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1 Table 2 Table 3

----------------------------------------------   

 References (48)

  1. Abbas S, Khan MA, Athar A, Shan SA, Saeed A, and Alyas T (2022). Enabling smart city with intelligent congestion control using hops with a hybrid computational approach. The Computer Journal, 65(3): 484-494. https://doi.org/10.1093/comjnl/bxaa068   [Google Scholar]
  2. Abualkishik A, Saleem M, Farooq U, Asif M, Hassan M, and Malik JA (2023). Genetic algorithm based adaptive FSO communication link. In the International Conference on Business Analytics for Technology and Security, IEEE, Dubai, UAE: 1-4. https://doi.org/10.1109/ICBATS57792.2023.10111157   [Google Scholar]
  3. Agarwal V, Sharma S, and Agarwal P (2021). IoT based smart transport management and vehicle-to-vehicle communication system. In: Pandian A, Fernando X, and Islam SMS (Eds.), Computer networks, big data and IoT: Proceedings of ICCBI 2020: 709-716. Springer, Singapore, Singapore. https://doi.org/10.1007/978-981-16-0965-7_55   [Google Scholar]
  4. Al Mamlook RE, Abdulhameed TZ, Hasan R, Al-Shaikhli HI, Mohammed I, and Tabatabai S (2020). Utilizing machine learning models to predict the car crash injury severity among elderly drivers. In the IEEE International Conference on Electro Information Technology, IEEE, Chicago, USA: 105-111. https://doi.org/10.1109/EIT48999.2020.9208259   [Google Scholar]
  5. Asif M, Abbas S, Khan MA, Fatima A, Khan MA, and Lee SW (2022). MapReduce based intelligent model for intrusion detection using machine learning technique. Journal of King Saud University-Computer and Information Sciences, 34(10): 9723-9731. https://doi.org/10.1016/j.jksuci.2021.12.008   [Google Scholar]
  6. Asif M, Khan MA, Abbas S, and Saleem M (2019). Analysis of space and time complexity with PSO based synchronous MC-CDMA system. In the 2nd International Conference on Computing, Mathematics and Engineering Technologies, IEEE, Sukkur, Pakistan: 1-5. https://doi.org/10.1109/ICOMET.2019.8673401   [Google Scholar]
  7. Ata A, Khan MA, Abbas S, Ahmad G, and Fatima A (2019). Modelling smart road traffic congestion control system using machine learning techniques. Neural Network World, 29(2): 99-110. https://doi.org/10.14311/NNW.2019.29.008   [Google Scholar]
  8. Ata A, Khan MA, Abbas S, Khan MS, and Ahmad G (2021). Adaptive IoT empowered smart road traffic congestion control system using supervised machine learning algorithm. The Computer Journal, 64(11): 1672-1679. https://doi.org/10.1093/comjnl/bxz129   [Google Scholar]
  9. Atta A, Abbas S, Khan MA, Ahmed G, and Farooq U (2020). An adaptive approach: Smart traffic congestion control system. Journal of King Saud University-Computer and Information Sciences, 32(9): 1012-1019. https://doi.org/10.1016/j.jksuci.2018.10.011   [Google Scholar]
  10. Aujla GS, Jindal A, and Kumar N (2018). EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation system. Computer Networks, 143: 247-262. https://doi.org/10.1016/j.comnet.2018.07.008   [Google Scholar]
  11. Bharadwaj N, Edara P, and Sun C (2019). Risk factors in work zone safety events: A naturalistic driving study analysis. Transportation Research Record, 2673(1): 379-387. https://doi.org/10.1177/0361198118821630   [Google Scholar]
  12. Bokaba T, Doorsamy W, and Paul BS (2022). A comparative study of ensemble models for predicting road traffic congestion. Applied Sciences, 12(3): 1337. https://doi.org/10.3390/app12031337   [Google Scholar]
  13. Borst C, Bijsterbosch VA, Van Paassen MM, and Mulder M (2017). Ecological interface design: Supporting fault diagnosis of automated advice in a supervisory air traffic control task. Cognition, Technology and Work, 19: 545-560. https://doi.org/10.1007/s10111-017-0438-y   [Google Scholar]
  14. Bresciani S, Ferraris A, and Del Giudice M (2018). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 136: 331-338. https://doi.org/10.1016/j.techfore.2017.03.002   [Google Scholar]
  15. Cao Y, Ahmad N, Kaiwartya O, Puturs G, and Khalid M (2018). Intelligent transportation systems enabled ICT framework for electric vehicle charging in smart city. In: Maheswaran M and Badidi E (Eds.), Handbook of smart cities: Software services and cyber infrastructure: 311-330. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-97271-8_12   [Google Scholar]
  16. Ferrer JR (2017). Barcelona’s smart city vision: An opportunity for transformation: Field actions science reports. The Journal of Field Actions, (Special Issue 16): 70-75.   [Google Scholar]
  17. Hagras H (2018). Toward human-understandable, explainable AI. Computer, 51(9): 28-36. https://doi.org/10.1109/MC.2018.3620965   [Google Scholar]
  18. He K, Zhang X, Ren S, and Sun J (2016). Deep residual learning for image recognition. In the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA: 770-778. https://doi.org/10.1109/CVPR.2016.90   [Google Scholar] PMid:26180094
  19. Khan AH, Abbas S, Khan MA, Farooq U, Khan WA, Siddiqui SY, and Ahmad A (2022). Intelligent model for brain tumor identification using deep learning. Applied Computational Intelligence and Soft Computing, 2022: 8104054. https://doi.org/10.1155/2022/8104054   [Google Scholar]
  20. Khan F, Khan MA, Abbas S, Athar A, Siddiqui SY, Khan AH, and Hussain M (2020a). Cloud-based breast cancer prediction empowered with soft computing approaches. Journal of Healthcare Engineering, 2020: 8017496. https://doi.org/10.1155/2020/8017496   [Google Scholar] PMid:32509260 PMCid:PMC7254089
  21. Khan MA, Abbas S, Atta A, Ditta A, Alquhayz H, Khan MF, and Naqvi RA (2020b). Intelligent cloud based heart disease prediction system empowered with supervised machine learning. Computers, Materials and Continua, 65(1): 139-151. https://doi.org/10.32604/cmc.2020.011416   [Google Scholar]
  22. Khan MA, Umair M, Saleem MA, Ali MN, and Abbas S (2019). CDE using improved opposite based swarm optimization for MIMO systems. Journal of Intelligent and Fuzzy Systems, 37(1): 687-692. https://doi.org/10.3233/JIFS-181127   [Google Scholar]
  23. Krizhevsky A, Sutskever I, and Hinton GE (2012). ImageNet classification with deep convolutional neural networks. In the 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, USA: 1106-1114.   [Google Scholar]
  24. Li L, Wang S, and Zhou X (2020). Fastest path query answering using time-dependent hop-labeling in road network. IEEE Transactions on Knowledge and Data Engineering, 34(1): 300-313. https://doi.org/10.1109/TKDE.2020.2981062   [Google Scholar]
  25. Lingli J (2015). Smart city, smart transportation: Recommendations of the logistics platform construction. In the International Conference on Intelligent transportation, big data and Smart City, IEEE, Halong Bay, Vietnam: 729-732. https://doi.org/10.1109/ICITBS.2015.184   [Google Scholar]
  26. Malik JA and Saleem M (2022). Blockchain and cyber-physical system for security engineering in the smart industry. In: Motahhir S and Maleh Y (Eds.), Security engineering for embedded and cyber-physical systems: 51-70. CRC Press, Boca Raton, USA. https://doi.org/10.1201/9781003278207-6   [Google Scholar]
  27. Matloob F, Ghazal TM, Taleb N, Aftab S, Ahmad M, Khan MA, and Soomro TR (2021). Software defect prediction using ensemble learning: A systematic literature review. IEEE Access, 9: 98754-98771. https://doi.org/10.1109/ACCESS.2021.3095559   [Google Scholar]
  28. Mehmood S, Ghazal TM, Khan MA, Zubair M, Naseem MT, Faiz T, and Ahmad M (2022). Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access, 10: 25657-25668. https://doi.org/10.1109/ACCESS.2022.3150924   [Google Scholar]
  29. Miller J (2008). Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture. In the IEEE intelligent vehicles symposium, IEEE, Eindhoven, Netherlands: 715-720. https://doi.org/10.1109/IVS.2008.4621301   [Google Scholar] PMCid:PMC2963648
  30. Mostowfi S and Buttlar WG (2020). Vehicle-to-infrastructure and human-to-infrastructure models for smart civil infrastructure systems. In: Stanton N (Ed.), Advances in human aspects of transportation: Virtual conference on human aspects of transportation: 147-155. Springe, Cham, Switzerland. https://doi.org/10.1007/978-3-030-50943-9_20   [Google Scholar]
  31. Mueller ST, Hoffman RR, Clancey W, Emrey A, and Klein G (2019). Explanation in human-AI systems: A literature meta-review, synopsis of key ideas and publications, and bibliography for explainable AI. Arxiv Preprint Arxiv:1902.01876. https://doi.org/10.48550/arXiv.1902.01876   [Google Scholar]
  32.  Muller C (2020). The impact of artificial intelligence on human rights, democracy and the rule of law. Council of Europe, Strasbourg, France.   [Google Scholar]
  33. Raad MW, Deriche M, and Sheltami T (2021). An IoT-based school bus and vehicle tracking system using RFID technology and mobile data networks. Arabian Journal for Science and Engineering, 46: 3087-3097. https://doi.org/10.1007/s13369-020-05111-3   [Google Scholar]
  34. Raj EFI and Kamaraj V (2013). Neural network based control for switched reluctance motor drive. In the IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology, IEEE, Tirunelveli, India: 678-682. https://doi.org/10.1109/ICE-CCN.2013.6528586   [Google Scholar]
  35. Sadhukhan P and Gazi F (2018). An IoT based intelligent traffic congestion control system for road crossings. In the International Conference on Communication, Computing and Internet of Things, IEEE, Chennai, India: 371-375. https://doi.org/10.1109/IC3IoT.2018.8668131   [Google Scholar]
  36. Sajjad G, Khan MBS, Ghazal TM, Saleem M, Khan MF, and Wannous M (2023). An early diagnosis of brain tumor using fused transfer learning. In the International Conference on Business Analytics for Technology and Security, IEEE, Dubai, UAE: 1-5. https://doi.org/10.1109/ICBATS57792.2023.10111263   [Google Scholar] PMCid:PMC9933798
  37. Saleem M, Abbas S, Ghazal TM, Khan MA, Sahawneh N, and Ahmad M (2022b). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3): 417-426. https://doi.org/10.1016/j.eij.2022.03.003   [Google Scholar]
  38. Saleem M, Khadim A, Fatima M, Khan MA, Nair HK, and Asif M (2022a). ASSMA-SLM: Autonomous system for smart motor-vehicles integrating artificial and soft learning mechanisms. In the International Conference on Cyber Resilience, IEEE, Dubai, UAE: 1-6. https://doi.org/10.1109/ICCR56254.2022.9995824   [Google Scholar]
  39. Saleem M, Khan MA, Abbas S, Asif M, Hassan M, and Malik JA (2019). Intelligent FSO link for communication in natural disasters empowered with fuzzy inference system. In the International Conference on Electrical, Communication, and Computer Engineering, IEEE, Swat, Pakistan: 1-6. https://doi.org/10.1109/ICECCE47252.2019.8940752   [Google Scholar]
  40. Seth I, Guleria K, and Panda SN (2024). A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements. Peer-to-Peer Networking and Applications, 17: 2090–2122. https://doi.org/10.1007/s12083-024-01683-1   [Google Scholar]
  41. Sharma H and Kanwal N (2023). Smart cities: A worldwide journey into intelligent urbanism and state-of-the-art technologies. Scientific and Technical Information Processing, 50: 328-355. https://doi.org/10.3103/S0147688223040081   [Google Scholar]
  42. Siddiqui SY, Haider A, Ghazal TM, Khan MA, Naseer I, Abbas S, and Ateeq K (2021). IoMT cloud-based intelligent prediction of breast cancer stages empowered with deep learning. IEEE Access, 9: 146478-146491. https://doi.org/10.1109/ACCESS.2021.3123472   [Google Scholar]
  43. Simonyan K and Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. In the 3rd International Conference on Learning Representations, San Diego, USA.   [Google Scholar]
  44. Sutar SH, Koul R, and Suryavanshi R (2016). Integration of smart phone and IOT for development of smart public transportation system. In the International Conference on Internet of Things and Applications, IEEE, Pune, India: 73-78. https://doi.org/10.1109/IOTA.2016.7562698   [Google Scholar]
  45. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, and Rabinovich A (2015). Going deeper with convolutions. In the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, USA: 1-9. https://doi.org/10.1109/CVPR.2015.7298594   [Google Scholar]
  46. Tamimi S and Zahoor M (2010). Link delay estimation using fuzzy logic. In the 2nd International Conference on Computer and Automation Engineering, IEEE, Singapore, Singapore, 2: 406-411. https://doi.org/10.1109/ICCAE.2010.5451575   [Google Scholar]
  47. Wang D, Liu Q, Ma L, Zhang Y, and Cong H (2019). Road traffic accident severity analysis: A census-based study in China. Journal of Safety Research, 70: 135-147. https://doi.org/10.1016/j.jsr.2019.06.002   [Google Scholar] PMid:31847989
  48. Zaino R, Ahmed V, Alhammadi AM, and Alghoush M (2024). Electric vehicle adoption: A comprehensive systematic review of technological, environmental, organizational and policy impacts. World Electric Vehicle Journal, 15(8): 375. https://doi.org/10.3390/wevj15080375   [Google Scholar]