Peter Stasinopoulos’s research while affiliated with RMIT University and other places

Connected and Automated Vehicles (CAVs) cybersecurity is an inherently complex, multi-dimensional issue that goes beyond isolated hardware or software vulnerabilities, extending to human threats, network vulnerabilities, and broader system-level risks. Currently, no formal, comprehensive tool exists that integrates these diverse dimensions into a unified framework for CAV cybersecurity assessment. This study addresses this challenge by developing a System Dynamics (SD) model for strategic cybersecurity assessment that considers technological challenges, human threats, and public cybersecurity awareness during the CAV rollout. Specifically, the model incorporates a novel SD-based Stock-and-Flow Model (SFM) that maps six key parameters influencing cyberattacks at the system level. These parameters include CAV communication safety, user adoption rates, log file management, hacker capabilities, understanding of hacker motivations (criminology theory maturity), and public awareness of CAV cybersecurity. The SFM's structure and behaviour were rigorously tested and then used to analyse five plausible scenarios: i) Baseline (Technological Focus Only), ii) Understanding Hacker Motivations, iii) CAV User and OEM Education, iv) CAV Penetration Rate Increase, and v) CAV Penetration Rate Increase with Human Behavior Analysis. Four metrics are used to benchmark CAV cybersecurity: communication safety, probability of hacking attempts, probability of successful defence, and number of CAV adopters. The results indicate that while baseline technological advancements strengthen communication framework robustness, they may also create new vulnerabilities that hackers could exploit. Conversely, a deeper understanding of hacker motivations (Criminology Theory Maturity) effectively reduces hacking attempts. It fosters a more secure environment for early CAV adopters. Additionally, educating CAV users and OEM increases the probability of defending against cyberattacks. While CAV penetration increases the likelihood of hack defence due to a corresponding rise in attempts, there is a noticeable decrease in hacking attempts with CAV penetration when analysing human behaviour. These findings, when translated into policy instruments, can pave the way for a more optimised and resilient cyber-safe ITS.

The integration of various stakeholders in the advancement of Automated Vehicles (AVs) has given rise to a range of technical, legal, and social challenges. Among these challenges, deterring cyber-criminal activities through the implementation of robust cybersecurity protocols and regulations stands out as the most urgent. Although individual stakeholders possess a solid understanding of cybersecurity regulations, there is a lack of a comprehensive decision-making tool that can dynamically visualise the macro-level implications of the AVs Cyber Regulatory Framework (CRF) among Intelligent Transportation System (ITS) stakeholders. To bridge this knowledge gap, this study employs the Causal Loop Diagram (CLD) to dynamically evaluate the progress of interdisciplinary ITS stakeholders in cyber-regulatory advancements. The CLD framework formulates the "tragedy of the commons" system archetype, wherein stakeholders prioritise their self-interests in enhancing cybersecurity, making decisions based on their specific needs without fully considering the potential consequences for others, AV adoption, and the long-term implications for CRF. The findings highlight three key leverage points for decision-makers to focus on. Firstly, establishing a CRF grounded in automakers' innovation is crucial. Secondly, sharing risks and addressing negative externalities associated with underinvestment and knowledge asymmetries in cybersecurity are essential. Lastly, capitalising on the vast amount of AV-generated data in AV operations and urban planning holds significant potential. Moreover, achieving an effective CRF requires striking a delicate balance among four factors: i) managing the limitations on data accessibility for AV automakers and ITS service providers; ii) establishing appropriate thresholds for regulatory command and control; iii) ensuring the safeguarding of automakers' business investments; and iv) protecting consumers' data privacy.

Considering that many fresh food items require domestic cold storage, understanding the role of appliances such as freezers is critical to reducing greenhouse gas emissions relating to domestic food waste and energy use. Using a social practices approach, this article reports on a study of cold storage and food waste practices, particularly freezing relating to meat, in 20 Australian households. Householders were interviewed twice, over winter and summer, 2022–2023. We studied intersecting practices of eating, shopping, cooking and cold storage, and their connection to larger social phenomena such as cold supply chains to gain insights into how and why meat was wasted. Although we set out to study household practices, we found that retailer practices of pricing specials and bulk buying must also be considered. Retailer practices combined with ambiguity in consumer understandings of date labels, shelf life and freezing times mean that domestic cold storage has become a normalised extension of the cold supply chain of food, especially for meat. We argue that understandings such as convenience and thriftiness, in conjunction with retailer expectations of domestic cold storage, may lead to the ratcheting up of household freezer use for food storage. We illustrate through our findings that this may potentially increase energy consumption and accelerate food waste, which becomes particularly problematic for carbon-intensive foods such as red meat. In innovatively reimagining domestic cold storage as an extension of industrial and retailer cold supply chains, we explore what kind of capabilities may be required for an equitable and low waste cold supply chain that reduces reliance on household freezers.

Growing global research utilizes user acceptance models to investigate the public acceptance of automated vehicles (AVs). A growing body of literature suggests it is essential to recognize cultural differences that may influence people's decisions and the intention to use (AVs). While the influence of perceived safety on AVs adoption has been examined globally, it has often been overlooked in Australia. To address this knowledge gap, this study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) model by incorporating perceived safety and socio-demographic factors in assessing behavioral intention for fully AVs in Australia. This study is the first in Australia to include perceived safety in the UTAUT model and look at how different factors like age, gender, experience, income, education, and travel habits affect people's intention to use technology. The model was evaluated with Structural Equation Modelling using a dataset of 804 respondents from Australia. Perceived Safety (PS) holds comparable importance to Social Influence (SI) and Facilitating Conditions (FC). Our analysis revealed that younger age groups exhibit a more substantial positive correlation between Performance Expectancy (PE) and Behavioral Intention (BI) compared to older age groups. Notably, there are significant distinctions in the impact of PS on BI between older and younger age groups, as well as between those with and without prior experience with AVs. Moreover, gender has a moderating effect akin to age in the PE-BI relationship. Our findings also reveal that age moderates the relationship between PE and BI, with younger individuals exhibiting less susceptibility to social influence compared to older counterparts. Gender also emerges as a moderator, affecting the relationship between FC and BI. Additionally, income moderates the relationships between both EE (Effort Expectancy) and FC with BI. However, qualifications do not significantly moderate the relationships between latent variables and BI. The multigroup analysis highlights a significant divergence in the influence of PE on BI between groups with no experience and experienced people. Additionally, the study shows that the higher-income group displays a lower coefficient of FC towards BI, potentially due to their pre-existing knowledge base. The findings from this study assist decision-makers by providing insights into public attitudes towards AVs by revealing the key factors influencing public acceptance.

The current study focuses on the critical role of efficient cold supply chain logistics (CSCL) within the beef meat supply chain (SC), ensuring the timely delivery of premium products. Despite its significance, substantial food loss and waste (FLW) in CSCL pose multifaceted challenges across economic, social, and environmental dimensions. This comprehensive literature review aims to identify state-of-the-art CSCL for reducing food waste, key research themes, and their potential roles in red meat waste reduction, as well as identify future research directions. It explores four pivotal themes—management, sustainability, network design (ND), and new information technologies (IT)—each contributing uniquely to mitigating FLW. A review of papers published in the last two decades reveals management as the predominant theme, followed by sustainability, ND, and IT. The study underscores the interconnectedness of these themes and highlights gaps in current research, particularly the need for multi-objective optimisation models. These models should integrate uncertainties, sustainability dimensions, and technological advancements, while aligning strategic, tactical, and operational decisions to enhance CSCL sustainability and reduce FLW in the beef meat industry. This review informs stakeholders—researchers, policymakers, practitioners, the government, and the public—about emerging trends and opportunities in addressing food waste, thereby fostering more efficient and sustainable CSCL practices.

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and$7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions.

There is a significant lack of comprehensive research that systematically examines public perceptions of liability (related to cyber risks), consumer data, and how these factors influence the adoption of automated vehicles (AVs). To fill this knowledge gap, the authors' research used a survey of 2062 adults across Australia, New Zealand, the UK, and the US to develop a scale for Liability, Data concerns, Data sharing and Patching and updates. This analytical approach employed various statistical methods to analyze the data (summarizing, finding patterns, measuring relationships). The results indicate that 70% of respondents express concerns about AV liability based on cyber risks, highlighting a significant level of liability anxiety. Individuals with high liability concerns also exhibit heightened concerns about AV data, are less comfortable sharing AV data, and display lower intent to adopt AVs. Conversely, individuals comfortable with data sharing are more willing to engage in patching and express a greater intent to adopt AVs. Interestingly, individuals with AV data concerns do not exhibit a negative correlation with their intent to adopt AVs. Additionally, those willing for patches also show a stronger intent to adopt AVs, challenging the notion that software updates hinder AV adoption.

No study has systematically investigated the public's perceptions of cybersecurity regulation, data generated by Autonomous Vehicles (AVs), and their relationship with the acceptance of AVs. To fill this knowledge gap, we conducted an exploratory study on public perceptions of cybersecurity regulation and consumer data in AVs acceptance by surveying nationally representative individuals from four OECD countries (US, UK, Australia, and New Zealand). A total of 2062 responses collected from the survey underwent Exploratory Factor Analysis (EFA) to examine constructs such as Cybersecurity Regulation, Data Sharing, Data Usage, Data Concerns, and intention to use AVs. Correlation analysis further explored the relationships between these constructs, while Mann-Whitney U and Kruskal-Wallis H tests assessed the significance of differences across participant groups. The empirical findings indicate that 80% of respondents agreed on the necessity of cybersecurity regulation for AV operations, with 67% perceiving it as a means to enhance AV safety. Surprisingly, 66% supported cybersecurity regulation despite the potential risk of exposing their personal information. Individuals who are more willing to share AV data also expressed a higher likelihood of using AVs. Furthermore, those who agreed more with cybersecurity regulations were more inclined to be compensated for their data transmission while expressing concerns about data storage and processing. Moreover, around 53% of participants feel they should be compensated for sharing their AV data, with 68% expressing concern about AVs' data storage and processing and 71% supporting the destruction of AV data post-sale. Regarding data privacy concerns, “In-vehicle Private Conversation” draws notable attention, rated very important or extremely important by 64% of the participants. The findings highlight the importance of cybersecurity regulation, data sharing, and data concerns in shaping individuals' intentions to use AVs, as well as the influence of socio-technological attributes.