Design and Implementation of an IoT-based Traffic and Parking Management System Integrated with GIS for Urban Environments

Year : 2024 | Volume : 11 | Issue : 03 | Page : 23-32
    By

    Raju R. Kulkarni,

  • Deshvena Y.N.,

Abstract

As urbanization accelerates, managing traffic flow and parking availability has become increasingly challenging. This article presents the design and implementation of an internet of things (IoT)-based traffic and parking management system integrated with geographic information systems (GIS) to address these challenges in urban environments. The proposed system utilizes a network of IoT sensors to monitor traffic flow, congestion levels, and parking space availability in real time. The data collected by these sensors is processed and analyzed to generate actionable insights and integrated with GIS to produce dynamic, real-time maps. The system architecture comprises various IoT sensors including cameras, ultrasonic sensors, and inductive loops, which provide comprehensive data on traffic conditions and parking occupancy. This data is transmitted wirelessly to a central processing unit, where it is aggregated and analyzed using advanced algorithms to detect patterns and predict congestion. The results are then visualized on interactive GIS maps, offering users real-time updates on traffic conditions and available parking spaces. The implementation involved deploying sensors across key urban locations, developing a data processing pipeline, and integrating the output with GIS software to create user-friendly dashboards and maps. The system was tested in a controlled urban environment to assess its accuracy and effectiveness. The results demonstrated significant improvements in traffic management and parking utilization, highlighting the system’s potential for enhancing urban mobility. This research contributes to the field of smart cities by providing a scalable solution for real-time traffic and parking management. The integration of IoT and GIS technologies offers a powerful tool for urban planners and traffic management authorities, promoting more efficient and sustainable urban transportation systems.

Keywords: Internet of things (IoT), traffic management, parking management, urban environments, geographic information systems (GIS), real-time monitoring, sensors, traffic flow, congestion levels, parking availability, data processing, dynamic maps, interactive dashboards, smart cities, urban mobility, scalable solution, data integration, wireless communication, predictive analytics, urban planning

[This article belongs to Trends in Transport Engineering and Applications ]

How to cite this article:
Raju R. Kulkarni, Deshvena Y.N.. Design and Implementation of an IoT-based Traffic and Parking Management System Integrated with GIS for Urban Environments. Trends in Transport Engineering and Applications. 2024; 11(03):23-32.
How to cite this URL:
Raju R. Kulkarni, Deshvena Y.N.. Design and Implementation of an IoT-based Traffic and Parking Management System Integrated with GIS for Urban Environments. Trends in Transport Engineering and Applications. 2024; 11(03):23-32. Available from: https://journals.stmjournals.com/ttea/article=2024/view=197046



References

  1. Almeida JP, Silva PS, Miranda P. Challenges in the deployment of IoT sensors for traffic management. J Traffic Transport Eng. 2021; 8 (3): 321–332.
  2. Batty M, Hudson-Smith A, Milton R. Smart cities of the future. Eur Phys J Spec Top. 2012; 214 (1): 481–518.
  3. Chen L, Zhang C, Wang J. Adaptive traffic signal control using real-time data from IoT sensors. Transport Res Part C Emerg Technol. 2020; 112: 289–305.
  4. Gao J, Wu S, Wang Y. Real-time traffic information and driver behavior: insights from IoT and GIS. IEEE Trans Intell Transport Syst. 2021; 22 (4): 2516–2525.
  5. Goodchild MF, Li L. Assuring the quality of spatial data. J Spatial Inform Sci. 2012; 5: 7–16.
  6. Jin H, Lee S, Lee H. Predictive analytics for traffic management using machine learning techniques. J Traffic Manage. 2019; 10 (2): 125–137.
  7. Khan SA, Kim S, Yang Y. Smart parking management using IoT and GIS: a case study. Urban Stud. 2021; 58 (7): 1420–1435.
  8. Lee J, Park C, Kim S. Real-time parking space detection and guidance system using IoT sensors and GIS. Sensors. 2019; 19 (6): 1332.
  9. Li X, Liu J, Zhang Y. Advanced traffic flow monitoring with IoT sensors. Int J Transport Sci Technol. 2020; 9 (2): 89–101.
  10. Park H, Lee J, Kim T. Dynamic parking management system using IoT and GIS technologies. J Urban Technol. 2020; 27 (3): 29–45.
  11. Reddy K, Manohar S, Kumar A. Challenges in integrating IoT and GIS for smart city applications. Computers Environ Urban Syst. 2022; 90: 101725.
  12. Singh A, Mishra S. Machine learning approaches for real-time traffic data analysis. IEEE Access. 2022; 10: 4567–4578.
  13. Wang H, Zhang S, Li J. A smart traffic management system based on IoT and GIS integration. J Intell Transport Syst. 2018; 22 (1): 24–36.
  14. Yuan J, Chen S, Yang Z. Real-time traffic mapping with GIS: applications and case studies. J Spatial Sci. 2019; 64 (2): 225–239.
  15. Zhang Y, Wang H, Li L. Traffic flow analysis using computer vision and IoT sensors. Int J Computer Vision. 2021; 129 (5): 1345–1360.
Regular Issue Subscription Original Research
Volume 11
Issue 03
Received 03/09/2024
Accepted 16/09/2024
Published 19/09/2024
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