Modelling of CO2 Emission Prediction for Dynamic Vehicle Travel Behavior Using Ensemble Machine Learning Technique

Urban growth in most developing countries mainly results from vast economic development. As, consequences, capital cities have become the center of many activities. A large amount of population become permanently resides in capital cities thereby raising a need for living space, social activity areas as well as transportation. One of the major challenges in urbanizing cities is poor air quality due to transportation emission particularly CO2 from vehicles. Continuous CO2 emission could lead to irreversible air pollution which causes a significant negative impact on the environment and human health. To date, most studies have employed a specific emission factor to estimate CO2 emission from vehicles. However, the emission factor varies based on vehicle type and climate. Therefore, this study aims to develop a vehicle travel CO2 model using the ensemble technique by incorporating with large volume of data collected from laboratory. The advantage of this study may assist the urban transportation planner to design smart transportation planning that enables them to respond to the current carbon footprint map.