The primary objective of the research is to explore the cybersecurity risks associated with AI-enabled intelligent transportation systems (Transportation 5.0) within the context of developing future smart cities. In Transpiration 5.0, the critical infrastructure increasingly relies on automated systems, which enhances the threat of ransomware attacks, specifically targeting vital cyber-physical systems (CPS), smart grids, and intelligent transportation systems (ITS). The paper explores transportation ransomware attack data to incorporate advanced visualization and extract relevant data attributes for cyber risk evaluation. The study uses the NIST risk management framework and ISACA risk quantification to develop a cyber risk evaluation method. With the findings of the literature review, the research highlights ransomware as a significant threat to intelligent transportation systems (ITS), given the negative impact of malware on IoT, IIoT, and network-connected devices. While substantial research exists on malware detection techniques, performance, accuracy, and cloud-based strategies, it’s crucial for organizations to comprehend the risks of ransomware attacks to implement effective security controls and enable cyber excellence. The research executes exploratory data analysis on the given dataset to find out the correlation between different attributes such as severity, data loss, affected systems, and ransom amount for evolving a risk method to evaluate cyber risk. The focus is to simplify the risk evaluation process by fitting the right data attributes into a standard risk management framework and risk formula for broader reusability. The method was applied to different transportation modes to calculate the risk scores respectively and generate insights accordingly. The overall analysis supports that effective risk predictions along with consistent monitoring can help to control ransomware attack incidents and improve cyber resiliency. This paper proposes a data-d...