Data-driven decarbonization: Optimizing P+R in Istanbul with machine learning energy modeling and ITS - Kartal, Mehmet Akif (Frontiers in Energy Research, 2024)
Due to the rapidly developing technologies, fast and practical solutions are offered to the problems encountered in daily life. Metropolitan cities are greatly affected by the ever-increasing population and migrations to big cities, the increase in production with the economy and job opportunities. At this point, with the introduction of smart transportation systems, fast and effortless solutions can be produced by saving time and space. City life can be facilitated by applying more efficient and rational solutions with smart transportation systems. In this study, it is aimed to investigate information about the Intelligent Transportation Systems and one of its applications, park and ride, which has created a significant agenda within the scope of transportation engineering in the recent past, and to provide information about the investments made by examining the application for Istanbul along with its various applications in the world. Some suggestions will be made by emphasizing the importance of the park and Ride smart city application for Istanbul. In conclusion, predictions of P + R application and energy consumption in periods of 1–24 months were made through machine learning. By obtaining energy consumption data thanks to machine learning, carbon gas emissions and its effects on greenhouse gases were also examined. It can be thought that by obtaining energy consumption data for the long term thanks to machine learning, it can make significant contributions to future investments, green environment-green world, and climate change studies.