AI-driven smart cities: Digital twin simulation, V2X communication, and EV infrastructure optimization

As urbanization accelerates, cities face mounting challenges in transportation efficiency, safety, and sustainability. This dissertation explores the integration of Digital Twin (DT) technology, Connected Vehicle Communication (C-V2X), and Electric Vehicle (EV) infrastructure optimization to advance smart city mobility solutions. The research presents a comprehensive framework leveraging real-time data analytics, machine learning, and simulation technologies to enhance urban transportation systems. The first component focuses on Digital Twin-driven traffic simulation, which enables scenario testing, predictive modeling, and real-time decision-making. A key contribution is the calibration of traffic simulation models using real-world speed data, facilitating optimized traffic management, transit planning, and road safety assessments. The study includes BTE-Sim, a fast simulation environment for public transit, and a Digital Twin-based road diet analysis for Chattanooga’s Frazier Avenue, demonstrating how simulation can enhance urban mobility. The second component investigates C-V2X technology for pedestrian safety, particularly in vehicle-to-pedestrian (V2P) communication. The research conducts a comparative study of V2P architectures and pre-crash scenarios, along with field tests evaluating LTE, DSRC, WiFi, and Bluetooth-based safety systems. These findings contribute to the development of intelligent transportation networks that improve pedestrian protection through real-time communication technologies. The third component explores machine learning techniques for EV charging infrastructure optimization, leveraging embedding vector representations, matrix factorization, and clustering methods. By analyzing real-world EV charging station data, the study uncovers key utilization patterns, proposes location optimization strategies, and introduces Non-Intrusive Load Monitoring (NILM) techniques for identifying EV charging events in residential settings. This dissertation advances the scientific and practical understanding of next-generation urban mobility systems, providing a scalable, data-driven framework for intelligent transportation planning, enhanced road safety, and sustainable EV infrastructure development. The methodologies and findings offer valuable insights for policymakers, urban planners, and transportation engineers, contributing to the realization of smart, connected, and sustainable cities.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Khaleghian, Seyedmehdi

Contributors dc:contributor

• Sartipi, Mina
• Liang, Yu (Hugh); Cox, Christopher; Wu, Dalei
• College of Engineering and Computer Science

• Battery charging stations (Electric vehicles)
• Digital twins (Computer simulation)
• Intelligent transportation systems
• Smart cities
• Urban transportation
• Vehicle-infrastructure integration

Licence

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

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/1008

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-2178