Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bindu Madavi, K.P.; Krishna Sowjanya, K.; Sardar, Tanvir H.; Reddy, Manoj Seetharama; Nimmalapudi, Sri Ram Title A name given to the resource Smart Traffic Systems: Revolutionizing Road Transport with AI and Image Processing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Springer Tracts on Transportation and Traffic;Volume;22;pp.185-198 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-87627-1_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-87627-1_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006791159?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006791159?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bindu Madavi K.P., Christ University, Bengaluru, India; Krishna Sowjanya K., CMR Institute of Technology, Bengaluru, India; Sardar T.H., Dayananda Sagar University, Bengaluru, India; Reddy M.S., Rutgers University, New Brunswick, NJ, United States; Nimmalapudi S.R., University of Central Missouri, Warrensburg, MO, United States Description An account of the resource In the past few years, Big data, machine learning, and artificial intelligence (AI) inclusion have changed the dynamics in various industries, including road transport. This research explores machine learning and artificial intelligence in road transportation, accentuating the advanced image processing methods. We apply these technologies to enhance how traffic is managed, how safe vehicles are operated, and how the most efficient routes are planned. This study also expands to various techniques and methods for analysis, such as image processing, object recognition, and recognition systems, and is effective in implementation. Through extensive experiments and several case studies, we have shown substantial improvements in accuracy and efficiency achieved when image processing in road transport, including machine learning and artificial intelligence. This study points out the tremendous prospects posed by these technologies in shaping the future of transport and suggests further developments and applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Artificial intelligence; Image processing; Machine learning; Road transport; Traffic management; Vehicle safety Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21948119; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online