A New Method to Control Traffic Congestion by Calculating Traffic Density
Keywords:
Canny, CmapGen, Detecting Edges, Image processing, Traffic density, VehiclesAbstract
Congestion in huge cities around the world has recently become one of their biggest issues. The rapid growth of automobiles and the lack of appropriate roads to handle a big number of vehicles are the causes of the traffic jams. Various existing methods like Conventional or static time-based traffic management system provides drawbacks such as low edge detection accuracy, image blurring, etc. In this paper to improve these drawbacks Capacity map Generator (CmapGen) algorithm is proposed. The proposed CmapGen algorithm uses image processing methods to determine the current area-wide traffic density at the intersection of traffic lights. The traffic density calculated for the live roads using the CmapGen method will be used to identify the available traffic light durations. The vehicle density will be determined using the frames produced from the traffic video files. The outcome demonstrates that by processing the edge detection technique results through several image processing techniques like thresholding, blur, etc., the suggested CmapGen algorithm increases the edge detection technique's accuracy. The proposed method is contrasted with other approaches, including Canny techniques and conventional or static time-based traffic management systems. The average traffic density for Canny is 23.91, whereas it is 31.12 for CmapGen. The suggested CmapGen has a higher average traffic density as compared to the traditional Canny approaches. The proposed technique yields superior results compared to the canny edge identification method. Therefore, it is more beneficial in a smart traffic control system to determine when to change traffic lights by computing the area-based traffic density in real time.
Downloads
References
R. Sundar, S. Hebbar and V. Golla, "Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection," in IEEE Sensors Journal, Feb. 2015, vol. 15, no. 2, pp. 1109-1113.
Mohamed Fazil Mohamed Firdhous, B. H. Sudantha and Naseer Ali Hussien "A framework for IoT-enabled environment aware traffic management", International Journal of Electrical and Computer Engineering (IJECE), Vol 11, No. 1 February 2021 pp 518-527.
R Vijaya Kumar reddy, K Prudvi Raju, M Jogendra Kumar, L Ravi Kumar, P Ravi Prakash and S Sai Kumar " Comparative Analysis of Common Edge Detection Algorithms using Pre-processing Technique", International Journal of Electrical and Computer Engineering (IJECE), Vol 7, No. 5 October 2017 pp 2574-2580.
C. Akinlar and E. Chome, "CannySR: Using smart routing of edge drawing to convert Canny binary edge maps to edge segments," 2015 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 2015, pp. 1-6.
R. Cucchiara, M. Piccardi and P. Mello, "Image analysis and rule-based reasoning for a traffic monitoring system," in IEEE Transactions on Intelligent Transportation Systems, June 2000, vol. 1, no. 2, pp. 119-130.
M Fathy, and MY Siyal, "Real-time image processing approach to measure traffic queue parameters," IEE Proc.-Vis. Image Signal Process, October 1995, vol 142, no. 5, pp. 297-303.
Yoichiro Iwasaki, "An image processing system to measure vehicular queues and an adaptive traffic signal control by using the information of the queues," in IEEE Conference on Intelligent Transportation System, November 1997, pp. 195-200.
M. Blosseville, C Krafft,F.Lenoir, V. Motyka, and S. Beucher, 'TIT AN: a traffic measurement system using image processing techniques," in Second International Conference on Road Traffic Monitoring, pp. 84-88, February 1989.
Madhavi Arora, V. K. Banga, "Real Time Traffic Light Control System", 2nd International Conference on Electrical, Electronics and Civil Engineering (ICEECE'2012), Singapore, April 28-29, 2012, pp. 172-176.
Van Li and Xiaoping FAN," Design of signal controllers for urban intersections based on fuzzy logic and weightings," in 6th IEEE Conference on Intelligent Transportation Systems, October 2003, vol 1, pp. 867- 871.
G. Nakamiti and F. Gomide, "Fuzzy sets in distributed traffic control," in Proc. 5th IEEE Int Conf Fuzzy Systems, September 1996, vol 3, pp. 1617-1623.
S. Chiu and S. Chand, "Self-organizing traffic control via fuzzy logic," Proceedings of 32nd IEEE Conference on Decision and Control, 1993, vol.2, pp. 1897-1902.
A Tzes, W. R. McShane, and S. Kim, "Expert fuzzy logic for traffic signal control for transportation networks," in Proc. Institute of Transportation Engineers 65th Annual Meeting, August 1995, pp.154-158.
TantaouiMouad, Laanaoui My Driss and Kabil Mustapha "Big data traffic management in vehicular ad-hoc network", International Journal of Electrical and Computer Engineering (IJECE), Vol 11, No. 4 August 2021, pp 3483-3491.
Celil Ozkurt and FatihCamci," Automatic Traffic Density Estimation and Vehicle Classification For Traffic Surveillance Systems Using Neural Networks", Journal of Mathematical and Computational Applications, 2009, Vol. 14, No. 3, pp. 187-196.
Dipti Srinivasan, Min Chee Choy, and Ruey Long Cheu,"Neural networks for real-time traffic signal control," IEEE Transactions on Intelligent Transportation Systems, September 2006, vol. 7(3), pp. 261-272.
Jiuyi Hua, and ArdeshirFaghri, "Development of neural signal control system: toward intelligent traffic signal control,"in Transportation Research Record 1497, Trans. Res. Board, Washington, DC, July 1995, pp. 53-61.
R. K. Saraf,"Adaptive traffic control using neural networks," Ph.D. Dissertation, Dept Civil Environ. Eng., Vanderbilt Univ., Nashville, TN, 1994.
D. C Chin, l C Spall, and R. H. Smith, "Evaluation of system-wide traffic signal control using stochastic optimization and neural networks," in Proc. 1999 American Control Conf, June 1999. voL3,pp. 2188-2194.
Ian Goodfellow, YoshuaBengio, Aaron Courville, "Deep Learning," MIT Press, 2016. Online]. Available:http://www.deplearningbook.org/.
Julian Nubert, Nicholas Giai Truong, Abel Lim, Herbert Ilhan Tanujaya, Leah Lim, Mai Anh Vu, "Traffic Density Estimation using a Convolutional Neural Network Machine Learning," National University of Singapore, 2018.
I Summersgill, J V Kennedy, R D Hall, A Hickford, S R Barnard, "Accidents at junctions on one-way urban," University of Southampton, 2001.
J. Růžička, J. Šilar, Z. Bělinová, and M. Langr, "Methods of traffic surveys in cities for comparison of traffic control systems — A case study," 2018 Smart City Symposium Prague (SCSP), 2018, pp. 1-6,
T. Tahmid and E. Hossain, " Density-based smart traffic control system using canny edge detection algorithm for congregating traffic information," 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), 2017, pp. 1-5,
Papageorgiou M., Diakaki C., Dinopoulou V., Kotsialos, A.,"Review of road traffic control strategies", Proceedings of IEEE, November 2004.Vol. 91, Issue 12, pp. 2043-2067.
Georgios Vigos, Markos Papageorgiou, YibingWangb, "Real-time estimation of vehicle-count within signalized links", Journal of Transportation Research Part C: Emerging Technologies, February 2008, Volume 16, Issue 1, pp.18–35.
Michael W. Szeto and Denos C. Gazis, "Application of Kalman Filtering to the Surveillance and Control of Traffic Systems", Journal of Transportation Science, November 1972, vol. 6 pp.. 4419-439.
Nisha, Rajesh Mehra, Lalita Sharma "Comparative Analysis of Canny and Prewitt Edge Detection Techniques used in Image Processing", International Journal of Engineering Trends and Technology (IJETT) – Volume 28 Number 1 - October 2015.
Susmitha .A, Ishani Mishra, Divya Sharma, Parul Wadhwa, Lipsa Dash "Implementation of Canny's Edge Detection Technique for Real World Images", International Journal of Engineering Trends and Technology (IJETT) – June 2017, Volume 48, NO:4.
Mohammad Shahab Uddin, Ayon Kumar Das, Md. Abu Taleb "Real-time Area Based Traffic Density Estimation by Image Processing for Traffic Signal Control System: Bangladesh Perspective" in 2nd International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 21-23 May 2015.
Er. Navreet Kaur, Er. Meenakshi Sharma "Comparative Analysis of Techniques used for Traffic Prediction", International Journal of Engineering Trends and Technology (IJETT) – Volume 50 Number 4 August 2017.
Mrs. Manasi Patil, Aanchal Rawat, Prateek Singh, Srishti Dixit "Accident Detection and Ambulance Control using Intelligent Traffic Control System", International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 8- April 2016.
Yonghong Yue "A Traffic-Flow Parameters Evaluation Approach Based on Urban Road Video", International Journal of Intelligent Engineering Systems, Vol. 2, No. 1, 2009.
Li Li1, Jian Huang, Xiaofei Huang, Long Chen "A Real-time Traffic Congestion Estimation Approach from Video Imagery", International Journal of Intelligent Engineering Systems, 2008.
M Venu Gopalachari, Morarjee Kolla, Rupesh Kumar Mishra, Zarin Tasneem, "Design and Implementation of Brain Tumor Segmentation and Detection Using a Novel Woelfel Filter and Morphological Segmentation", Complexity, vol. 2022, Article ID 6985927, 9 pages, 2022. https://doi.org/10.1155/2022/6985927
Divya Jegatheesan Chandrasekar Arumugam "Intelligent Traffic Management Support System Unfolding the Machine Vision Technology Deployed using YOLO D-NET", International Journal of Intelligent Engineering Systems, 2021, Vol. 14, No. 5.
E. Amarnatha Reddy, Ilaiah Kavati, K. Srinivas Rao, and G. Kiran Kumar. "A secure railway crossing system using IoT." In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 2, pp. 196-199. IEEE, 2017.
Kavati, Ilaiah, G. Kiran Kumar, M. Venu Gopalachari, E. Suresh Babu, Ramalingaswamy Cheruku, and V. Dinesh Reddy. "Non-invertible Cancellable Template for Fingerprint Biometric." In International Conference on Hybrid Intelligent Systems, pp. 615-624. Springer, Cham, 2021.
Gupta, Sangeeta, Kavita Agarwal, and M. Venu Gopalachari. "Smart Home Infrastructure with Blockchain-Based Cloud IoT for Secure and Scalable User Access." Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences: PCCDS 2021. Springer Nature, 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
