Using System Dynamics Approach to Explore the Mode Shift between Automated Vehicles, Conventional Vehicles, and Public Transport in Melbourne, Australia.

With the increasing use of automated vehicles (AVs) in the coming decades, government authorities and private companies must leverage their potential disruption to benefit society. Few studies have considered the impact of AVs towards mode shift by considering a range of factors at the city level, especially in Australia. To address this knowledge gap, we developed a system dynamic (SD)-based model to explore the mode shift between conventional vehicles (CVs), AVs, and public transport (PT) by systematically considering a range of factors, such as road network, vehicle cost, public transport supply, and congestion level. By using Melbourne's Transport Network as a case study, the model simulates the mode shift among AVs, CVs, and PT modes in the transportation system over 50 years, starting from 2018, with the adoption of AVs beginning in 2025. Inputs such as current traffic, road capacity, public perception, and technological advancement of AVs are used to assess the effects of different policy options on the transport systems. The data source used is from the Victorian Integrated Transport Model (VITM), provided by the Department of Transport and Planning, Melbourne, Australia, data from the existing literature, and authors' assumptions. To our best knowledge, this is the first time using an SD model to investigate the impacts of AVs on mode shift in the Australian context. The findings suggest that AVs will gradually replace CVs as another primary mode of transportation. However, PT will still play a significant role in the transportation system, accounting for 50% of total trips by person after 2058. Cost is the most critical factor affecting AV adoption rates, followed by road network capacity and awareness programs. This study also identifies the need for future research to investigate the induced demand for travel due to the adoption of AVs and the application of equilibrium constraints to the traffic assignment model to increase model accuracy. These findings can be helpful for policymakers and stakeholders to make informed decisions regarding AV adoption policies and strategies.

A multinational empirical study of perceived cyber barriers to automated vehicles deployment.

The digital transformation of Automated Vehicles (AVs) has raised concerns in the cyber realm among prospective AV consumers. However, there is a dearth of empirical research on how cyber obstacles may impact the operation of AVs. To address this knowledge gap, this study examines the six critical cyber impediments (data privacy, AV connectivity, ITS infrastructure, lack of cybersecurity regulations, AV cybersecurity understanding, and AV cyber-insurance) that influence the deployment of AVs. The impact of gender, age, income level, and individual AV and cybersecurity knowledge on these obstacles are statistically assessed using a sample of 2061 adults from the United States, the United Kingdom, New Zealand, and Australia. The research revealed intriguing empirical findings on all cyber barriers in the form of a trichotomy: participants' education level, understanding of AVs, and cybersecurity knowledge. As education levels increase, the significance of a cyber barrier to AV deployment decreases; however, as AV comprehension and cybersecurity knowledge increase, the perception of a cyber barrier becomes significantly more important. In addition, the study demonstrates differences in perceptions of cyber barriers and AV deployments based on gender, age, income, and geographic location. This study's findings on cyber barriers and AV deployment have implications for academia and industry.

Cyber-attacks in the next-generation cars, mitigation techniques, anticipated readiness and future directions.

Modern-day Connected and Autonomous Vehicles (CAVs) with more than 100 million code lines, running up-to a hundred Electronic Control Units (ECUs) will create and exchange digital information with other vehicles and intelligent transport networks. Consequently, ubiquitous internal and external communication (controls, commands, and data) within all CAV-related nodes is inevitably the gatekeeper for the smooth operation. Therefore, it is a primary vulnerable area for cyber-attacks that entails stringent and efficient measures in the form of "cybersecurity". There is a lack of systematic and comprehensive review of the literature on cyber-attacks on the CAVs, respective mitigation strategies, anticipated readiness, and research directions for the future. This study aims to analyse, synthesise, and interpret critical areas for the roll-out and progression of CAVs in combating cyber-attacks. Specifically, we described in a structured way a holistic view of potentially critical avenues, which lies at the heart of CAV cybersecurity research. We synthesise their scope with a particular focus on ensuring effective CAVs deployment and reducing the probability of cyber-attack failures. We present the CAVs communication framework in an integrated form, i.e., from In-Vehicle (IV) communication to Vehicle-to-Vehicle (V2X) communication with a visual flowchart to provide a transparent picture of all the interfaces for potential cyber-attacks. The vulnerability of CAVs by proximity (or physical) access to cyber-attacks is outlined with future recommendations. There is a detailed description of why the orthodox cybersecurity approaches in Cyber-Physical System (CPS) are not adequate to counter cyber-attacks on the CAVs. Further, we synthesised a table with consolidated details of the cyber-attacks on the CAVs, the respective CAV communication system, its impact, and the corresponding mitigation strategies. It is believed that the literature discussed, and the findings reached in this paper are of great value to CAV researchers, technology developers, and decision-makers in shaping and developing a robust CAV-cybersecurity framework.