Transportation has been a fundamental need for human societies since it first emerged. The development of more advanced modes of transportation over time has resulted in the widespread utilization of rail, road, and air travel. We all recognize that cities face a variety of complicated difficulties; for today's smart cities, the conventional methods used in the past to manage transportation, environmental pollution, and garbage disposal are insufficient. Because of this, smart transportation necessitates the use of modern architecture and technology. To improve the effectiveness of urban infrastructure and services concerning road and people safety, this study employs the smart transportation concept by incorporating cutting-edge technologies such as the Internet of Things (IoT) and Deep Learning (DL). The proposal that was suggested will be implemented in three stages. First, use surveillance camera footage to automatically determine the current traffic conditions. Second, there is constant monitoring of air pollution levels, with alerts generated if anything seems off. Third, the waste management system assists in locating trashcans that have become overflowing. Smart transportation's end goal is to improve people's daily travel experience by utilizing the knowledge gained through the analysis of data gathered by a network of sensors and other electronic gadgets. To display, analyse, and interpret real-time data, all of it must be kept in a location known as the cloud. The mobile app was created to help the general population comprehend traffic, pollution, and waste bin status in their surroundings.

Transportation, Cloud, Sensor, Waste Management, Traffic, Pollution, Mobile Application, Deep Learning