Addressing smart city challenges utilizing machine learning: vehicular crash and public transportation fuel consumption prediction

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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Phan, Le

Contributors dc:contributor

• Sartipi, Mina
• Liang, Yu; Wu, Dalei
• College of Engineering and Computer Science

• Machine learning
• Fuel consumption--Forecasting
• Traffic accidents--Forecasting

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/768

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1943