Road traffic optimization using image processing and clustering algorithms

• Published in: Advances in engineering software (1992) Vol. 181; p. 103460
• Main Authors: Chaurasia, Atul, Gautam, Aman, Rajkumar, R., Chander, A. Sai
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Analysis; Classification; Clustering; Image processing; Machine learning; Traffic parameters; Image processing; Traffic parameters; Clustering; Analysis; Machine learning; Classification
• ISSN: 0965-9978
• DOI: 10.1016/j.advengsoft.2023.103460

•Traffic congestion is a major problem in many cities of India along with other countries.•Some common and widespread methods used for the predictionsss of traffic parameters include regression analysis, machine learning, image processing, classification, clustering etc.•We also need to explore the more efficient ways to optimize traffic at minimal costs.•We will compare working of sensors and IoT bases techniques.•Image processing methods and come up with better and improves algorithms that can work at both day and night effectively. Traffic congestion is a major problem in many cities of India along with other countries. Some common and widespread methods used for the predictions of traffic parameters include regression analysis, machine learning, image processing, classification, clustering etc. The objective of this research is to explore the usage and compliance and efficiency of various algorithms and find out whether image processing techniques or other clustering techniques can be better for the traffic optimization purposes. We also need to explore the more efficient ways to optimize traffic at minimal costs. We will compare working of sensors and IoT bases techniques along with image processing methods and come up with better and improves algorithms that can work at both day and night effectively, improve pre processing techniques and reduce the complicacy of present systems to increase the response time for better optimization.