Collision Risk Model Simulations with Data-Driven Models and Examining the Impact and Strategies of Electrical Vehicle Grid Integration

Abstract

Collision risk models (CRMs) and electric vehicle (EV) grid integration are pivotal aspects in the domain of transportation and energy management. CRMs aim to assess and predict the probability of collisions involving diverse airplane models with the ultimate goal of enhancing safety in transportation. These models consider a variety of factors, such as airplane models, environmental conditions, traffic patterns, and human behavior, providing valuable insights to implement effective safety measures and traffic management strategies. CRMs have shifted towards data-driven models, departing from traditional physics-based approaches, to attain more precise trajectory paths for airplanes, leveraging large-scale real-world data and machine learning algorithms. Multiple algorithms were investigated for the purpose of carrying out a simulation study and analyzing flight trajectories. Concurrently, with EV grid integration, a systematic screening analysis is aimed at identifying utility company executives for a focus group study, exploring the impact of large-scale EVs on the electric grid, with a specific focus on electricity price dynamics. This interdisciplinary investigation merges principles from engineering and social sciences to comprehensively examine the challenges and opportunities arising from EV integration into the grid. By leveraging this collaborative approach, valuable insights can be gained, aiding in the development of effective strategies to address grid management and pricing concerns, and ultimately fostering a sustainable and efficient EV-grid integration. In conclusion, the intersection of collision risk models and electric vehicle grid integration represents a comprehensive approach to enhance safety in transportation and promote sustainable energy management. By embracing data-driven models and interdisciplinary investigations, we gain valuable insights into collision probabilities and electricity price dynamics, enabling the development of effective strategies for safer and more efficient transportation.