LANE MORPH: Machine Learning Powered Divider For Traffic Volume Adaptation

Authors

  • Lavanya N L Department of Computer Science and Engineering, East West College of Engineering, Visveswaraya Technological University, Bengaluru, Karnataka 560064.
  • Anvith Krishna N Department of Computer Science and Engineering, East West College of Engineering, Visveswaraya Technological University, Bengaluru, Karnataka 560064.
  • Arun Kumar V Savanvur Department of Computer Science and Engineering, East West College of Engineering, Visveswaraya Technological University, Bengaluru, Karnataka 560064.
  • Shrivatsa R S Department of Computer Science and Engineering, East West College of Engineering, Visveswaraya Technological University, Bengaluru, Karnataka 560064.
  • Udaya Kumar Shetty Department of Computer Science and Engineering, East West College of Engineering, Visveswaraya Technological University, Bengaluru, Karnataka 560064.

DOI:

https://doi.org/10.5281/zenodo.14811747

Keywords:

Smart Traffic Management, Machine Learning, Real-time Vehicle Detection, IoT, Dynamic Road Dividers

Abstract

LaneMorph is a machine learning-powered system designed to optimize urban traffic management using IoT and real-time video processing. By dynamically adjusting road dividers based on traffic density, the system enhances lane utilization, reduces congestion, and prioritizes emergency vehicles. This paper details the architecture, implementation, and potential impact of LaneMorph in smart city infrastructure. Additionally, the system integrates various sensor technologies, predictive algorithms, and automation mechanisms to improve traffic flow efficiency and ensure road safety.

References

Khanke, P., & Kulkarni, P. S. (2014). A technique on road traffic analysis using image processing. International Journal of Engineering Research, 3(2).

Sundar, R., Hebbar, S., & Golla, V. (2014). Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sensors Journal, 15(2), 1109–1113. https://doi.org/10.1109/JSEN.2014.2360288

Nagaraj, U., Rathod, J., Patil, P., Thakur, S., & Sharma, U. (2013). Traffic jam detection using image processing. International Journal of Engineering Research and Applications, 3(2), 1087–1091.

Bhusari, S., Patil, S., & Kalbhor, M. (2015). Traffic control system using Raspberry Pi. Global Journal of Advanced Engineering Technologies, 4(4), 413–415.

Lavanya, N. L., Bhat, A., Bhanuranjan, S. B., & Narayan, K. L. (2023). Enhancing the capabilities of remotely piloted aerial systems through object detection, face tracking, digital mapping, and gesture control. International Journal of Human Computations & Intelligence, 2(3), 147–158.

Ahmed, S. T., Kumar, V. V., & Kim, J. (2023). AITel: eHealth augmented-intelligence-based telemedicine resource recommendation framework for IoT devices in smart cities. IEEE Internet of Things Journal, 10(21), 18461-18468.

Kawle, A., Shah, D., Doshi, K., Bakhtiani, M., Gajja, Y., & Singh, P. (2017). Movable traffic divider: A congestion release strategy. International Journal of Recent Advances in Engineering & Technology (IJRAET).

Agrawal, S., & Maheshwari, P. (2021, January). Controlling of smart movable road divider and clearance ambulance path using IoT cloud. 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–4). IEEE. https://doi.org/10.1109/ICCCI50826.2021.9402443

Dalmia, H., Damini, K., & Nakka, A. G. (2018, September). Implementation of movable road divider using Internet of Things (IoT). 2018 International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 968–971). IEEE. https://doi.org/10.1109/GUCON.2018.8674942

Sun, Z., Bebis, G., & Miller, R. (2006). On-road vehicle detection: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 694–711. https://doi.org/10.1109/TPAMI.2006.104

Kumar, S. S., Ahmed, S. T., Xin, Q., Sandeep, S., Madheswaran, M., & Basha, S. M. (2022). Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques. Computers, Materials & Continua, 72(1).

Temkar, R., Asrani, V., & Kannan, P. (2015). IoT: Smart vehicle management system for effective traffic control and collision avoidance. International Journal of Science and Research (IJSR), 4(05).

Lokesh, S., & Reddy, T. P. (2014). An adaptive traffic control system using Raspberry Pi. International Journal of Engineering Sciences & Research Technology, 3(6), 831–835.

Ahmed, S. T., & Basha, S. M. (2022). Information and communication theory-source coding techniques-part II. MileStone Research Publications.

Vijayananda, N., Lavanya, N. L., Sattigeri, N. N., Nisarga, R. K., & Pooja, N. M. (2023). Abnormal event detection and signaling in multiple video surveillance scenes using CNN. International Journal of Human Computations & Intelligence, 2(3), 106–116.

Downloads

Published

2025-02-05

How to Cite

Lavanya N L, Anvith Krishna N, Arun Kumar V Savanvur, Shrivatsa R S, & Udaya Kumar Shetty. (2025). LANE MORPH: Machine Learning Powered Divider For Traffic Volume Adaptation. International Journal of Human Computations & Intelligence, 3(6), 378–385. https://doi.org/10.5281/zenodo.14811747

Issue

Vol. 3 No. 6 (2024): November / December : Issue - 06

Section

Articles

License

Copyright (c) 2025 Lavanya N L, Anvith Krishna N, Arun Kumar V Savanvur, Shrivatsa R S, Udaya Kumar Shetty

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC