Evaluating the Role and Significance of Dynamic Intelligence Resource Management in Applications Designed for Intelligent Transportation Systems


  • Cuddapah Anitha Associate Professor, Department of Computer Science and Engineering, School of Computing, Mohan Babu University, (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh
  • Prakash Chandra Swain Assistant Professor, Department of Commerce, School of Social, Financial & Human Sciences, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India
  • Kavita Nitin Khadse Assistant Professor, Department of Systems / IT, Chetana's Ramprasad Khandelwal Institute of Management & Research, affiliated to Mumbai University, Mumbai, Maharashtra
  • Pooja Kulkarni Assistant Professor, Faculty of Science , Vishwakarma University, Kondhwa, Pune, Maharashtra
  • Yeligeti Raju Associate Professor, Vignana Bharathi Institute of Technology Hyderabad, Telangana, India
  • Ram Bajaj RNB Global University, Bikaner, Rajasthan
  • S. P. Pandey Professor, Glocal School of Science and Technology, Glocal University, Delhi-Yamunotri Marg, Mirzapur Pole, Saharanpur, U.P


DIRM, DITS, Intelligent Networks, Transport Networks, Applications


As Intelligent Transportation Systems (ITS) evolve, dynamic intelligent resource management (DIRM) integration becomes increasingly important for optimizing system performance. This paper assesses the role and significance of DIRM (K. Smith et al., 2021) in ITS applications. The study looks into how dynamic resource allocation and utilization, such as compute power, communication bandwidth, and data storage, might improve the efficiency, adaptability, and responsiveness of ITS applications. This study evaluate major DITS (designed for intelligent transportation systems) methodologies and their implications for various ITS applications using a comprehensive analysis of existing research. It looks into how real-time resource allocation changes affect traffic management, vehicle-to-infrastructure communication, and overall system resilience (M. Johnson and R. Patel, 2017). A. Wang et al. (2018) discussed technological limits, cybersecurity consequences, and the necessity for standardized protocols to promote seamless integration across heterogeneous systems. According to the study's findings, DIRM and DITS play an important role in improving the adaptability and efficiency of ITS applications (S. Lee and B. Kim, 2019). The findings of this review influence future ITS advancements, emphasizing the importance of a dynamic and intelligent resource management approach to meet the growing demands of modern transport networks.


Download data is not yet available.


Adhikari, J. P., & Das, D. R. (2015). Assessing the Best Path in Routing for Network Security. Kaav International Journal of Science, Engineering & Technology, 2(1), 95-105.

Breiman, L., Using Time Data To Get Space Headway-Speed Distributions in Traffic Flows. Systems Development Corp., Tech. Memo. TM-3858/018/00 to U.S. Bureau of Public Roads under Contract No. FH116623 (30 April 1959).

D., A. M. (2023). Deep Neural Network for Classification of Inside Scene. Kaav International Journal of Science, Engineering & Technology, 10(2), 1-6. https://doi.org/10.52458/23485477.2023.v10.iss2.kp.a1

Federal Highway Administration, "Intelligent Transportation Systems Joint Program Office," [Online]. Available: https://www.its.dot.gov/.

Gafarian, A. V., Lawrence, R. L., and Munjal, P. K., An experimental validation of various methods of obtaining relationships between traffic flow, concentration, and speed on multi-lane highways. Highw. Res. Rec. 349 (1971),pp.13-30).

K. Smith et al., "Dynamic Resource Allocation in Intelligent Transportation Systems," Journal of Intelligent Transportation Systems, vol. 20, no. 3, pp. 123-145, 2021.

Maslekar, A. A., O., Lanjewar, D. U. A., & N. (2014). Development of Intelligent Parking System Using Processing of Images. Kaav International Journal of Science, Engineering & Technology, 1(4), 39-45.

Mori, M., Takata, H., and Kisi, T., Fundamental considerations on the speed distributions of road traffic flow. Transp. Res., 2 ( 1) : 31-39 (1968).

M. Johnson and R. Patel, "Towards Resilient Intelligent Transportation Systems: A Dynamic Resource Management Perspective," Transportation Research Part C: Emerging Technologies, vol. 78, pp. 210-228, 2017.

Rajput, A. C. (2015). Intelligent Counselor : An Intelligent Advisory System for Educational Decision. Kaav International Journal of Science, Engineering & Technology, 2(2), 11-21.

S. Lee and B. Kim, "Adaptive Resource Allocation for Real-Time Traffic Management in Intelligent Transportation Systems," Proceedings of the International Conference on Intelligent Transportation Systems, 2019.

Tewari, A., & Kumar, D. A. (2014). Different Routing Algorithm for computer Networks. Kaav International Journal of Science, Engineering & Technology, 1(1), 21-34.

Wright, C. C., A second method of estimating traffic speeds from flows observed at the ends of a road link. Traffic Eng. Control, 15(9): 432-434 (1974).

Wang et al., "Dynamic Intelligence Resource Management for Vehicular Networks," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 8, pp. 2661-2674, 2018.




How to Cite

Anitha, C. ., Swain, P. C. ., Khadse, K. N. ., Kulkarni, P. ., Raju, Y. ., Bajaj, R. ., & Pandey, S. P. . (2024). Evaluating the Role and Significance of Dynamic Intelligence Resource Management in Applications Designed for Intelligent Transportation Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 509–519. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4775



Research Article

Most read articles by the same author(s)