banner
Home > Open Journal Systems

Enhance traffic flow prediction with Real-Time Vehicle Data Integration

Rishabh Jain, Sunita Dhingra, Kamaldeep Joshi, Arun Kumar Rana, Nitin Goyal

Abstract


This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.


Keywords


traffic management systems; control technologies; traffic signal timing; financial gains; real-time

Full Text:

PDF

References


1. Djenouri Y, Belhadi A, Srivastava G, Lin JCW. Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Generation Computer Systems 2023; 139: 100–108. doi: 10.1016/j.future.2022.09.018

2. Jain R, Dhingra S, Joshi K, Grover A. An improved traffic flow forecasting based control logic using parametrical doped learning and truncated dual flow optimization model. Wireless Networks 2022; 28(7): 3101–3110. doi: 10.1007/s11276-022-03020-x

3. Deekshetha HR, Shreyas Madhav AV, Tyagi AK. Traffic prediction using machine learning. In: Suma V, Fernando X, Du KL, et al. (editors). Evolutionary Computing and Mobile Sustainable Networks. Springer; 2022. pp. 969–983.

4. Montoya-Torres JR, Moreno S, Guerrero WJ, Mejía G. Big data analytics and Intelligent Transportation Systems. IFAC-PapersOnLine 2021; 54(2): 216–220. doi: 10.1016/j.ifacol.2021.06.025

5. Chen Q, Song Y, Zhao J. Short-term traffic flow prediction based on improved wavelet neural network. Neural Computing and Applications 2021; 33: 8181–8190. doi: 10.1007/s00521-020-04932-5

6. Miyim AM, Muhammed MA. Smart traffic management system. In: Proceedings of 2019 15th International Conference on Electronics, Computer and Computation (ICECCO); 10–12 December 2019; Abuja, Nigeria. pp. 1–6.

7. Zhang W, Yu Y, Qi Y, et al. Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Transport Science 2019; 15(2): 1688–1711. doi: 10.1080/23249935.2019.1637966

8. Ata A, Khan MA, Abbas S, et al. Modelling smart road traffic congestion control system using machine learning techniques. Neural Network World 2019; 29(2): 99–110. doi: 10.14311/NNW.2019.29.008

9. Mandhare PA, Kharat V, Patil CY. Intelligent road traffic control system for traffic congestion: A perspective. International Journal of Computer Sciences and Engineering 2018; 6(7): 908–915. doi: 10.26438/ijcse/v6i7.908915

10. Rath M. Smart traffic management system for traffic control using automated mechanical and electronic devices. IOP Conference Series: Materials Science and Engineering 2018; 377(1): 012201. doi: 10.1088/1757-899X/377/1/012201

11. Hamidi H, Kamankesh A. An approach to intelligent traffic management system using a multi-agent system. International Journal of Intelligent Transportation Systems Research 2018; 16: 112–124. doi: 10.1007/s13177-017-0142-6

12. Zhang H, Wang X, Cao J, et al. A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series. Applied Intelligence 2018; 48(10): 3827–3838. doi: 10.1007/s10489-018-1181-7

13. Lana I, Del Ser J, Velez M, Vlahogianni EI. Road traffic forecasting: Recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine 2018; 10(2): 93–109. doi: 10.1109/MITS.2018.2806634

14. Guo L, Yuan Y. Forecast method of short-term passenger flow on urban rail transit. In: Proceedings of 2017 VI International Conference on Network, Communication and Computing; 8–10 December 2017; Kunming, China.

15. Lanke N, Koul S. Smart traffic management system. International Journal of Computer Applications 2013; 75(7): 19–22. doi: 10.5120/13123-0473




DOI: https://doi.org/10.32629/jai.v6i2.574

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Rishabh Jain, Sunita Dhingra, Kamaldeep Joshi, Arun Kumar Rana, Nitin Goyal

License URL: https://creativecommons.org/licenses/by-nc/4.0