Advancing Sustainable Transportation with Cutting-Edge Technologies

The transportation sector accounts for the most greenhouse gas emissions among all economic sectors. Thus, making our transportation systems sustainable is crucial for achieving the national 2050 net-zero emission goal. This thesis aims to explore the application of cutting-edge technologies for advancing sustainable transportation. A framework that analyzes technologies and their potential applications across different transportation modes and scales is proposed and demonstrated through two case studies.

The first case study focuses on developing a digital twin-based cooperative driving system for Connected and Automated Vehicles (CAVs) at roundabouts. An experiment conducted in a digital twin simulation environment shows that the cooperative driving system reduces emissions and energy consumption significantly while enhancing safety and maintaining efficiency.

The second case study proposed a Multi-Layer Perceptron (MLP)-based prediction structure to iteratively predict future flight demand with historical data and future population and personal income data. Compared with Terminal Area Forecasting (TAF) projection data, the difference in the year 2050 is only around 8%. Ultimately, the applications of future aviation fuel demand estimation and future Sustainable Aviation Fuel (SAF) distribution strategy exploration are showcased, leveraging the prediction result of the flight demand in 2050.

Through the two case studies, the thesis demonstrates the effectiveness of the proposed framework and reveals the significant potential of cutting-edge technologies, such as digital twin, CAVs, and SAF, in achieving sustainability in the transportation sector. These findings provide valuable insights for policymakers, industry stakeholders, and researchers to promote innovation and application of sustainable transportation technologies and ultimately achieve the net-zero emission goal.