Transportation Regulation in Smart Cities: A Study in Cognition Sensor Network Development

Smart cities are designed to maximize efficiency and equity in resource utilization and distribution. One of the primary goals of a smart city is to improve communication between citizens and their government at all levels. People can live peacefully and harmoniously in a city with a well-developed public transportation system. A shortage of traffic data, inefficient use of available resources, and inadequate demographic and ethnic data are all factors that complicate the design of a system for managing transportation. Real-time data that can be relied upon is essential for any transportation system. Congestion causes delays in the flow of people, goods, and government services. Sharing information about traffic congestion is sufficient for efficient operation. Intelligent transportation systems improve the quality of life in a city by easing traffic, reducing pollution, and improving accessibility. A cognitive sensor network framework (CSNF) is offered to manage transportation in smart cities better. The CSNF plans a transportation system to aid in the smart city's design, which constantly analyzes traffic flow and attends to potential problems in advance. The results of the CSNF's experiments show that it can cut traffic volumes by 92.23 percent while simultaneously achieving an efficiency of 96.67 percent, a performance that is unreachable using conventional methods.