Liang, Yu; Wu, Dalei
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
According to the United Nations Department of Economic and Social Affairs, 64% of the developing world and 86% of the developed world will be urbanized by 2050. This presents both new challenges and wonderful opportunities. Thanks to the fast, steady growth of technologies such as the Internet of Things (IoT), and Internet of People, the process of collecting the data required to solve the challenges that urbanization brings forth has been alleviated; thus, improving the quality of life for the citizens of urban environments. This thesis focuses on solutions to two of the challenges facing urbanized areas: vehicular crashes and public transportation fuel consumption by utilizing innovative machine learning models. These solutions can assure the safety of citizens, assist with urban planning, emission reduction, smart city development, etc.
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Machine learning; Fuel consumption--Forecasting; Traffic accidents--Forecasting
xii, 65 leaves
Phan, Le, "Addressing smart city challenges utilizing machine learning: vehicular crash and public transportation fuel consumption prediction" (2022). Masters Theses and Doctoral Dissertations.