Computational Intelligence Approaches for Smart Traffic Flow Optimization and Urban Mobility Analytics
High growth of urban populations poses numerous challenges to the urban transport system that are characterized by long travel time, congestion, and pollution. Traditional techniques of traffic management may not be responsive enough to these problems because it is seldom able to react promptly to changing, real-time traffic scenarios. The study explores the computational traffic flow, mobility analytics, machine learning, and other subfields of informatics, like reinforcement learning and optimization methods, to analyze traffic management and the improvement of urban mobility analytics. The new approach is proposed, which forecasts traffic, traffic jams, and real-time management of traffic lights by synthesizing real-time data of traffic sensors, GPS, and city cameras. Deep Neural Networks are a form of machine learning that predicts traffic demand. Traffic signal timing control is performed using reinforcement learning. Genetic algorithms and particle swarm optimization are some of the optimization methods used to offer real-time route suggestions to minimize congestion and offer better travel times. The performance of the system is compared against the traditional methods, and the optimization of the traffic flow and the informatics analytics enhancement of the performance of the urban mobility system by the new method outperforms traditional methods in overwhelming ratios. The new regime reduces the waiting times by 1/4 and boosts the vehicular traffic flow by 1/3, and also reduces the fuel consumption of vehicles by 1/5, which reduces the CO2 emission by the city by 15%. The system was shown to be able to adjust to different conditions of traffic, such as peak and off-peak traffic. The system performance in the latter sections provided the research directions that were to be taken in the next stage, including incorporating autonomous vehicles and intelligent city models, and applying the advanced technologies of deep learning to enhance urban mobility and facilitate the creation of sustainable and efficient urban transportation.