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Title	A Detailed Case Study On Various Challenges In Vehicular Networks For Smart Traffic Control System Using Machine Learning Algorithms
ID_Doc	1444
Authors	Vamsi B.; Doppala B.P.; Mahanty M.; Veeraiah D.; Rao J.N.; Rao B.V.S.
Year	2024
Published	Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications
DOI	http://dx.doi.org/10.1201/9781003409502-3
Abstract	Because of limited land plans and overcrowded transport networks in metropolitan regions, traffic control networks are becoming an essential resource of human’s life. The basic challenge in transport networks has indeed been congestion due to traffic jam, which should be rectified in order to minimize the energy waste, accidental deaths, congested roads, and driver distress. The most of traffic problems in urban areas are caused by the large number of vehicles. As an outcome of this traffic congestion, many traffic related issues have emerged in urban areas where people are moving fast between two locations. By controlling traffic conditions and making adjustment of signal timing, a Machine Learning (ML) based traffic control transportation system provides a solution can enhance flow of traffic in smart cities. The main aim of this chapter is to identify and support sustainable modes of transportation, as well as to improve a Smart Traffic Control System (STCS) by utilizing real-time updated information. Vehicle junction signals are extremely important role in reducing overcrowding. A lack of public connectivity is another cause of accidents in traffic jams. As a consequence, the driver’s average waiting time has multiplied. This is mainly due to lack of proper traffic signal operating condition. Existing Vehicle-to-Vehicle communication-focused works are still unable to accurately measure the traffic flow. Furthermore, fixed-period traffic sensing systems cannot manage changing traffic flow, resulting in prolonged traffic waiting times at road junctions. This case study focuses on how Vehicle Networks uses ML techniques to measure the intensity of traffic jams and provides users with a time-saving alternative approach for journeys through a smartphone application. © 2024 selection and editorial matter, T. V. Ramana, G. S. Pradeep Ghantasala, R. Sathiyaraj, Mudassir Khan individual chapters, the contributors.
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