Conceptual Model of Citizen Science with Machine Learning to Increase The Effectiveness of Land Transportation of Urban Communities

Land transportation systems in big cities play an important role in facilitating the mobility of city residents. Demographic changes demand changes in the land transportation system to suit the needs of urban communities. Urban communities, as users of land transportation, have the potential to contribute knowledge to the creation of a land transportation system that is in accordance with the demographic changes of urban communities. Problems arise when the transportation system cannot respond quickly to changes in the demographics of urban society. On the other hand, citizen science is the concept of involving citizens in a scientific project for a specific purpose, so that citizens can be involved in developing land transportation systems in big cities. Artificial intelligence, especially machine learning, is used in transportation systems with citizen science to process data collected from citizen science projects and other sources that will produce alternative decision-making for land transportation systems in big cities. Thus, the purpose of this research is to create an integration model between citizen science and machine learning to increase the effectiveness of land transportation systems in big cities. The research method uses a qualitative approach through a literature review of previous research on citizen science, transportation systems, and machine learning.