INTELLIGENT TRANSPORTATION SYSTEM: CURRENT TRENDS AND FUTURE DIRECTIONS USING DEEP LEARNING

The Intelligent Transportation System (ITS) is a modern technology that aims to provide efficient, safe, and reliable transportation services. ITS is an integrated system that comprises several technologies such as sensors, communication devices, and software. The study covers the need of deep learning in various areas of ITS, including traffic prediction, traffic flow optimization, and intelligent vehicle control. The research paper concludes that deep learning has significant potential for improving ITS performance and recommends its adoption in the transportation industry. Intelligent Transportation Systems (ITS) have been widely researched and developed to improve transportation safety, efficiency, and sustainability. With the advancement of deep learning techniques, ITS can now process large amounts of transportation data and make accurate real-time predictions. we analyse the challenges and opportunities in implementing deep learning in ITS and propose future research directions to address them. Our review aims to provide researchers and practitioners with a clear understanding of the potential of deep learning in ITS and guide them towards developing more efficient and effective transportation systems. Transportation infrastructure works in a field that is far from uncomplicated. Many shows both geographical and temporal features at different sizes and in different situations caused by different external causes. It can be difficult to describe the interaction of variables, create generalised representations, and then apply them to an individual issue. These circumstances are but a small portion of the challenges that contemporary ITS must overcome. The survey emphasises the impact that deep learning's role-modelling approaches have had in ITS. We concentrate on the difficulties that practitioners have created to tackle these varied issues, and we detail the architectural and problem specific factors that went into creating solutions. We anticipate that this poll will act as a link among the robotics and transportation communities, illuminating upcoming areas and factors.