Evolutionary algorithms for dynamic optimization of plug-in charging station networks

Abstract

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of IoT on urban mobility, using empirical data obtained from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its alignment with actual data. The data reveals a link between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of anticipating congestion and AQI, hence confirming the effectiveness of the models. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban mobility, enhancing decision-making processes, and creating sustainable urban environments via the use of data-driven insights and proactive interventions.