Research on Automated Vehicles (AVs) has attracted lots of attention in the last decade. The most significant contributor to road accidents and fatalities is human error [1]. A transport system relying less on a human being, such as an AV, can help humans save time and reduce human-related road accidents.

The idea of AV was proposed almost a century ago [2], and the significant technological barriers regarding sensing and computing were not resolved until the 1980s, when it could be realistically implemented. The early research [3] concentrated on automated driving on automated highway systems—the California PATH program commencing in 1986 demonstrated automated driving on the I-15 in San Diego. During the most recent practice occurring in the USA, a Silicon Valley-based start-up is going to deploy thousands of self-driving vehicles for delivering groceries or pizza on US streets [4]. In Australia, the NSW government, partnering with Transurban [5], launched four trials in 2018, including ten automated cars on the major motorways in Sydney by conducting different urban scenarios [5]. In Asia, Baidu (a Chinese multinational technology company) reached a stage of testing driverless cars on public roads. Toyota announced an investment in artificial intelligence by setting up a research institute in the US [6].

Autonomous driving is expected to be part of everyday life with technological advancement in AVs and cutting edge research in this field conducted worldwide. After several years of research, the Society of Automotive Engineers [7] has categorized six levels of driving automation, from level 0 (human being dominated) to level 5 (full automation in any given driving scenario). Connected and Automated Vehicles (CAVs) will slowly become a reality by combining the AVs’ function and wireless technology so that they can communicate with other vehicles, infrastructure, and the road. CAVs can operate more safely and reliably by sharing and coordinating information with other road users and infrastructure. For example, CAVs will talk to other infrastructures located in a congested area to reroute earlier to avoid the congestion [8].

With the accelerated introduction of AVs in the coming decades, government authorities and private companies need to be aware of facing this disruptive technology and leveraging it to benefit the community; therefore, it is important to understand how an individual perceives an autonomous vehicle and how their feelings spread into other people. However, there is a lack of research concerning a comprehensive onreview of how people will adopt autonomous vehicles which considers a wide range of factors, including demographics, real interaction with AVs, trust, awareness of techniques, risks, level of automation, driving conditions, and penetration level. In addition, knowledge of how individuals feel about AVs, and how it can affect other people’s adoption levels, is also limited.

2. Synthesis of Factors Affecting AVs Adoption

In the following sections, it wase described the factors affecting AVs adoption under three major themes: driverless technology and uncertainty, policy impact, and road traffic environment.

2.1. Driverless Technology and Uncertainty

Ref. ^[9][11] introduced AV sensor technology, the theory of localization, and mapping techniques for level 1 to level 3. Many aspects need to be improved, particularly the reduction of uncertainty for perception, cost reduction for perception systems, and operating safety for algorithms and sensors; therefore, the development of the AVs’ technique will focus more on increasing safety, driving safety, sustainability, and mobility in the coming future; however, accompanied by the advancement of technology, data security and privacy issues are put forward as barriers to adoption. Ref. ^[10][12] presented the CAV communication framework and provided all the interfaces of CAV related cyber attacks in the intelligent transport system. Integrated management framework for AV cybersecurity involving automotive manufacturers, equipment manufacturers, data aggregators, and data processors would adopt a shared problem-solving approach. Ref. ^[11][13] proposed several methodologies that can secure positioning vision, as well as sensing and network technologies in driverless vehicles. They also identified several aspects of data privacy, such as sensing technologies, positioning technologies, vehicular networks and vision technologies. Vehicular Ad Hoc Networks, known as VANETS, can be deployed to prevent these issues. Nevertheless, it has technical and socio-economic challenges, such as consistency of data, latency control, and high mobility. An automated driving system (ADS) will be commercially available in a decade; therefore, ref. ^[12][14] discussed the implication of ADS and the state of the art factors in the field of ADS. From the traffic operation side, ADS can have several advantages: reduced congestion from reduced incidents, more effective navigation, more accessibility, a reduced number of vehicles on the road due to the increased usage of ride-sharing, and less parking space. For vehicle users, it will make drivers feel less stressed, have fewer accidents, and be a more efficient mode of transportation. Apart from that, low speed and weight shuttle vehicles will be introduced into selected communities on a small scale, and driverless vehicles will be deployed on the highway with technologically viable conditions. Ref. ^[13][15] discussed how traffic light control can be helpful for CAVs in terms of integrating with conventional traffic to smooth traffic flow and minimize energy emission. It only covers a small scale without researching at a network level. It proposes two optimal control frameworks, including a free driving mode, and CAV following non-CAV to achieve energy efficiency, as validated by a MATLAB and VISSIM simulation. During the simulation, four driving modes (free driving, approaching, following, and braking) and conflict areas are set up, and the energy impacts regarding different CAV penetration rates are compared. It concludes that the energy efficiency can be improved by integrating with CAVs until it reaches a certain threshold. The implementation of AVs also needs preparation. Ref. ^[14][16] discussed the possibility of enacting AV certification, which will cause insurance and liability issues. For example, how can the AV minimize injuries towards their passengers or crash-involved parties when an AV hits another car? Another issue associated with implementation is market penetration evaluation. As ^[15][17] indicated, China has the largest percentage of people who have used ride-hailing services, whereas India, Japan, and Hungary have the lowest percentage; therefore, geographical location may greatly impact riding-hailing services when AVs are introduced. The methodology of trip generation for AVs needs to be reassessed. Ref. ^[16][18] estimated the car trip generation for all age groups with regard to AVs by measuring gaps at different life stages for road users’ travel needs; however, it only covers level 4 automation, and it assumes a 100% penetration level of AVs. In reality, the number of people using AVs depends on various reasons, such as perceived safety, acceptance of innovation, and so on. Although ^[17][19] estimated that it would take 10 to 20 years for the public to adopt level 3 and level 5 AVs, respectively, it is necessary for manufacturers and policymakers to better educate the public about the benefits and drawbacks of AVs to address public’s opinions, beliefs, and consumer needs. By conducting surveys in Germany, ref. ^[17][19] observed that the participants were willing to pay 10.6% and 14.5% more for level 4 and level 5 automated vehicles, respectively. Although it washe study identified no significant main effect for gender, it was observed that age could be an important factor, with people under the age of 24 willing to pay significantly more than other age groups.

Mixed Traffic

Ref. ^[18][20] systematically focusreviewed on the existing traffic flow models with various levels of detail, especially in mixed traffic conditions, and investigated the relationship between the management of transportation systems, AV based strategies, and traffic dynamics. The situation includes car-following models with AV-involved traffic, lane changing models, and the key differences between human-driving vehicle (HV) models and AV models. It wasThe study concluded that most of the existing models are too simple to capture AV traffic’s key features, and do not represent how human drivers act in the presence of AVs. One ^[19]of the studies [21] conducted the impact of AVs on the uncertainty of HV behaviour with different penetration levels by using a stochastic Lagrangian model. The simulation shows the increase of the AV penetration rate from 5% to 50%, which can significantly reduce uncertainty and improve mixed traffic system stability, whereas the position of AVs does not impact uncertainty and stability. Another ^[20]study [22] provided formulations regarding operational traffic capacity consisting of AVs and HVs by taking into account penetration rate, different lane policies, and vehicles’ characteristics. Strict segregation of AVs and HVs can result in a lower capacity, whereas mixed-use of both vehicles could increase the road capacity; however, the formulations were based on average speed as well as spacing features, and more research should pay attention to driver/vehicle characteristics. Moreover, the transportation infrastructure management plan should be revisited because of the interaction with mixed traffic flow ^[18][20].

2.2. Policy Impact and Uncertainty Analysis

Ref. ^[21][23] found that free public charging and access to bus lanes are the most functional incentive after subsidy due to China’s unique recurring congestion situation, with nearly one-third of people in China, stuck in congestion. It examined Chinese consumer preferences regarding electric vehicle policy incentives by doing a discrete choice survey, including cruise range, purchase price, road toll exemption, and access to the bus lane. Another onarticle by ^[22][24] investigates the adoption of EV by examining perceived risk factors, consumers’ knowledge, and financial policy in China. Education can be the most efficient way to promote EV, although the experiment does not consider perceived cost, trust, and ease of use, which are critical factors for adoption in past studies. Ref. ^[23][25] proposed a novel AV incentive program by considering the purchase price and deployment of AV lanes. It involved the first stage for deployment of AV lanes and the second stage for optimal purchase. In addition to that, with more AVs coming onto the road, the landscape of the parking infrastructure will be transformed due to AVs’ increasing ownership from operators. Ref. ^[24][26] suggested that policymakers need to adjust minimum parking requirements because more people will be adopting AVs or shared AVs, thus avoiding the necessity of large parking areas. Hence, planners may need to rearrange the landscape of parking infrastructure, such as introducing dynamic pricing for loading zones and unloading zones, and reducing on-street parking places. Ref. ^[25][27] conducted a summary of all related shared vehicle policies. It discussed components of shared AVs modellings, including demand, fleet, traffic assignment, vehicle assignment, vehicle redistribution, pricing, and parking, and how the different components interact with each other; however, it did not address bike and scooter sharing systems. Street redesign strategies, economic instruments, and service provision can be focus areas from the government level. More research on the dynamic pricing structure and how they can impact the car-sharing system and fleet size elasticity need to be explored in future studies. By comparing the US and Germany model results developed for 2035, ref. ^[26][28] suggested the following policy that can fully harness the advantages of AVs: stringent regulation of vehicles, leverage of AVs to facilitate public transport (PT) usage, land use planning and zoning, as well as transition to more sustainable vehicles.

2.3. Road Traffic Environment

The influence of the road traffic environment has been explored from various perspectives, as described in the following sub-sections.

Fuel Economy Testing of AVs

Ref. ^[27][29] assessed the impacts of AV technology on fuel economy levels by considering a range of automated driving cycles; however, it does not investigate the complex environment, such as traffic control systems and curves. The targeted AVs are for level 2 and 3 automation, not for level 4 and 5 automation. Ref. ^[28][30] conducted different penetration levels in an urban scenario, with stop and go conditions. ItThe wasstudy guideds when to use battery power and gasoline for electric vehicles with AV technology. Experiments by ^[29][31] on single-lane ring track demonstrate that the entire fleet can reduce approximately 15% CO₂ with 5% of CAVs due to CAVs’ capability of dampening stop and go conditions. The vehicle trajectory data is obtained by a 360-degree camera. Ref. ^[30][32] proposed a framework with a 100% penetration level, considering VMT, travel demand, and historical speeds of road links that can predict fuel-savings of AVs. Results show a 45% reduction in the “optimistic” case and 30% in the “pessimistic” case; however, it does not provide details regarding the various automation levels.

AVs Driving Safety

Ref. ^[31][33] tested how many years (or miles) of AV travel can demonstrate reliability regarding injuries and fatalities by setting up different confidence levels. Typically, it will take 400 years to drive AVs in order to demonstrate their reliability; however, it dhe study did not investigate other levels of automation. Ref. ^[32][34] summarised the past onliterature to identify AV safety quantification onstudies at a strategic level by using six approaches: traffic simulation, crash population, safety effectiveness estimation, road test analysis, and system failure risk assessment. A rigorous process has been conducted; for example, quantification of AVs’ impacts on traffic safety, AV as a vehicle for ground transportation, and safety of various levels of automation were listed as criteria for choosing the relevant ones. It wasstudies. The research concluded that the existing methodologies for the AV safety evaluation have some shortcomings, such as potential AV passengers’ behaviour, AV safety from an AV implementation perspective, and emerging safety issues because of AV implementations. Ref. ^[33][35] utilized a simulation-based approach to investigate the safety impact of AVs, and concluded that the number of crashes would be reduced by around 12% with AVs. In the simulation conducted using VISSIM (a traffic simulation software), time-to-collision, conflict points, and post-encroachment time were the considered three measures to evaluate the scenarios between conventional vehicles and full penetration of AVs. Another wastudy ^[34][36] also addressed the same problem by developing an AV control algorithm in the VISSIM and implementing the algorithm in a motorway. The control algorithm considered adjacent vehicles, a rule-set associated with motorway operations, and lateral decisions. The research demonstrated improvements in road safety by reducing conflicts significantly. The conflicts could be reduced by 12% to 47%, 50% to 80%, 82% to 92% and 90% to 94%, for 25%, 50%, 75%, and 100% penetration of AVs, respectively. Due to various driving styles, freeway on-ramp merging areas are at high risk for vehicle crashes. Ref. ^[35][37] proposed a conflict index, in theory, as the main indicator, and introduces a merging conflict model to estimate the safety impacts in different scenarios by considering main and ramp vehicles. ItThe was showedstudy shows clear benefits of AVs for improving safety, although several assumptions were postulated for the AVs’ merging conflict model because of a lack of data. An on-ramp cellular automata model proposed by ^[36][38] considered safe distance and traffic flow, and assessed traffic efficiency and safety under different penetration levels. The research showed that AVs positively impact traffic efficiency compared with human-driven vehicles, and traffic safety will be greatly improved with the increase of AVs’ penetration in the congested scenario; however, it is worthwhile considering building a dedicated lane for AVs, because AVs could travel faster than a human-driven vehicle. Ref. ^[37][39] utilized the National Motor Vehicle Crash Causation Survey (NMVCCS) database to categorize human-related crashes into the following factors: sensing, predicting, planning, execution, and incapacitation. The research shows that even with the upcoming fully autonomous system, AVs will need to be programmed to avoid human-driven errors because of multiple types of factors leading to crashes. For example, speeding and illegal manoeuvres emphasize the necessity for specialists to program these issues into the safety protocols; therefore, regulators need to re-establish a framework that enforces AV design philosophies to replace default assumptions. Moreover, AVs would have superior performance against impairment by alcohol, incapacitation, and other impairments.

Cybersecurity

Ref. ^[38][40] showtudied 151 papers from 2008 to 2019 for comprehensive research into attacks and defences for AVs. The study classified attacks into the autonomous control system, vehicle to vehicle communications, driving system components and defence into intrusion detection, security architecture, and anomaly detection. As it is difficult to respond quickly to cyber attacks, artificial intelligence with big-data analysis can improve the specifications of electronic control units. Ref. ^[9][11] analyzed, synthesized, and interpreted critical areas for the roll-out and progression of connected and automated vehicles (CAVs) in combating cyber-attacks. More specifically, it washe study described, in a structured way, a holistic view of six potentially critical avenues, which lies at the heart of CAV cybersecurity research (CAV communication framework, physcial/proximity access attacks, CAV supply chain, human factors, regulatory laws and policy framework, and integrated management framework). In a follow up onestudy, ref. ^[39][41] developed a conceptual model to analyze cybersecurity in the deployment of CAVs by integrating six critical avenues and mapping their respective parameters which either trigger or mitigate cyber-attacks in operation. Ref. ^[40][42] concluded that the attack models and defence strategies need further experiments under realistic environments by systematically investigating CAVs’ cyber security issues. ItThe wasstudy also pointed out that the main approaches recommended by the industry to address security issues are “Secure development lifecycle” and “Machine learning models embedded in CAVs”. The first approach is to integrate security into the product development and maintenance processes, whereas the second approach involves selecting data for model training, model evaluation, deployment, and monitoring. Nevertheless, the security and privacy challenges that would be evident after the convergence of the cellular 5G and 6G networks in V2X-C architecture need to be addressed in good time ^[41][43]. An appropriate insurance scheme also plays a key role in reducing the impacts of cyber security issues. With the rolling out of the 5G network, software updates will be a critical component of deployment, and this could cause mass hacking. Ref. ^[42][44] pointed out two insurance models; one operates through a public guarantee fund whereas the other operates in an agreement between the state and the insurance industry body, which operates to decrease the negative impacts of uninsured drivers; therefore, the establishment insurance scheme will facilitate the advancement of the CAV industry.

Infrastructure Requirement

To better prepare a seamless integration of AVs and conventional vehicles, there is a need fto dor d a systematic review and development of the policies and guidelines for road infrastructure ^[43][45]. ItThe wasstudy generated a grading framework for assessing infrastructure plans from safety, efficiency, and accessibility perspectives by considering mixed traffic, autonomous corridors, and separated areas; however, the framework does not consider the advancement of automotive technology, which can impact AV operations, and more research should focus on cost (construction and maintenance), governance (operation management and responsibility), and interoperability (consistent with the adjacent area). Ref. ^[43][45] pointed out the proposed framework should also incorporate industrial and freight logistic space because these modes of transport will be one of the major beneficiaries. The future of transportation will be a combination of vehicle automation, shared mobility, and vehicle electrification ^[44][46]; therefore, charging facilities are important to facilitate the public adopting AVs. Ref. ^[45][47] proposed a smart charging framework using an aggregator-based approach to optimize charging activities by shifting electricity demand from high-peak hours into other generation periods. The results suggest that EV battery capacity is essential for a flexible charging scheme. In addition to that, adding more charging infrastructure only can increase overall energy infrastructure expenses because of limited battery capacity under a real-time pricing scheme; however, ref. ^[45][47] do not consider the VMT induced by smart charging activities, therefore, new opportunities for the integration of the AV network into the energy network and telecommunication network are expected for the innovative design of urban infrastructure ^[46][48]. From an engineering perspective, less headway distance from AVs could result in high road capacity. In the shared-used AVs situation, ref. ^[46][48] concluded that the spare capacity from enhanced road capacity would free up the public space, which can be developed into other infrastructure for other active modes. In terms of car parking proximity, more flexibility for the allocation of car parking spaces was found in the shared-used AV situation because the passenger may use the nearest available vehicle ^[46][48]. From a planning perspective, ref. ^[47][49] utilized two-hybrid multi-criteria analysis models to assess an array of choices based on safety and sustainability, such as dynamic or stationary charging, plug-in or wireless charging, and mixed flow with HVs. By evaluating construction, operation, and maintenance costs, road safety, charging time, traffic congestion, impact on health due to radiation, and charging system energy efficiency, the optimal solution is “lanes dedicated to autonomous electric vehicles, with plug-in charging stations beside the roadway, along the route”. By analysing regional transportation plans from 52 metropolitan planning organizations in the US, ref. ^[48][50] concluded that maintaining and upgrading existing transportation infrastructure that accommodates the needs of AVs are the main policies. For example, Las Vegas, Dallas-Ft., Worth, and Philadelphia have policies to maintain the roads to a higher standard than the current standard. The policies include “making lanes narrower, providing clear lane markings and maximising pavement quality” to be compatible with AV testing and commissioning.

3. Modelling Approach

Ref. ^[49][51] summarised findings from the approach of spatial models and social-economic models, where the ultimate goal is to help operators and policymakers forecast future transportation systems with different AV scenarios. It does include model parameters (energy consumption, urban parking change, AV production cost, market penetration), modelling approaches (agent-based model, four steps model), and factors that can affect adoption levels; however, the focus of the research should be shifted to various stakeholders, such as transport authorities, transit operators, car manufacturers and insurance companies. Statistical methods have been mainly used to determine the factors that could influence AVs’ adoption, such as logit regression, structural topic modelling, descriptive statistics, the ANOVAs method, the discrete choice model, and confirmatory factor analysis. These methods, including demographics, and psychological factors, particularly perceived safety, perceived benefits, and perceived ease of use, have been discussed in previous onstudies; however, there is a lack c of research concerning how transport attributes can determine human adoption, such as congestion and public travel behaviour. System dynamic modelling also plays an important role in modelling complex scenarios regarding AVs. It enables the development of various techniques for understanding problems, particularly in the case of CAVs. Ref. ^[39][41] presented a conceptual model based on System Dynamics (SD) to analyze cybersecurity in the complex and unpredictable deployment of CAVs. ItThe waauthors investigated five dimensions: the framework for CAV communication, protected physical access, human aspects, CAV penetration, regulatory laws and policy framework, and trust, both inside the industry (OEMs) and amongst the general public. Ref. ^[50][52] discussed the potential outcomes for the adoption of AVs by using a SD approach for four different scenarios: no change in behaviour and ownership, change of behaviour, no change in ownership, and a complete change in ownership (all vehicles are shared AVs); however, it dhe study did not consider the adoption process (penetration and level of service change over time), and the data was obtained by the workshop. Although the research ^[51][53] in the Netherlands conducted research with four scenarios (AV in bloom, demand, doubt and standby), using the SD modelling approach, by taking into account the adoption process and policy test, it did not involve the traffic congestion from the usage of AVs and its relevant policy impact (e.g., congestion charging policy). Another similar one wastudy ^[52][54] assessed the impacts of AVs on mode choice via the SD modelling approach, by focusing on levels 1 to 3. It was divided into two situations: autonomous vehicle and cooperative vehicle (can communicate to infrastructure and other vehicles); however, the base year data is from 2013, which may need updating given it is an earlier onestudy and the technology may have advanced quickly. Ref. ^[53][55] developed a model that could forecast Australia’s AV greenhouse gas emissions in the medium- and long-term by using a SD approach. The research has considered the technological intervention of AVs when gradually replacing conventional vehicles with AV adoption starting in 2030; however, the SD model did not consider the impacts of the dynamic fleets on GHG emissions. Similarly, ref. ^[54][56] also developed a SD model to analyze the impacts of different subsidy policies in Korea (the subsidy cliff, phase-out, phase-in 50%, and phase-in 350% subsidy scenarios) towards electric vehicle’s environmental benefits. VMT, coupled with a combination of subsidy scenarios, was assessed by using a life cycle assessment to gain insight into AV environmental impacts. Another onestudy ^[55][57], using the SD approach, demonstrates the impacts of AVs using the following aspects: AV technology, law enforcement, infrastructure projects/improvements, fleet size, and vehicle density. Although the model does not consider the change of trip purpose, trip length, occupancy, business innovation, land-use change, and climate change, it proposes a framework for focstusdying the usage of AV technology. The framework is useful in forecasting system performance based on different public policies and investment decisions.

AVs On-Demand System

Ref. ^[56][58] used an agent-based modelling tool called “Commuter” to simulate the on-demand AV impacts by choosing a small area in Melbourne and a shorter simulation period (7:00 to 9:00 a.m.). Although Avs’ on-demand system could decrease the current fleet size by 84% compared with the scenario of the conventional vehicle, it could lead to a 77% increase in VKT because of the empty vehicle relocation; therefore, more arstudies regarding first and last kilometre travel connecting to PT are worth exploring in detail by considering the land-use changes, such as fewer parking spaces and more lane capacities. The updated onestudy ^[57][59] simulated a shorter period and focused on car-shared systems (impacts of ride-sharing are not explored). By considering the empty AV relocations for servicing customers, a fleet that is 58% to 84% smaller than the current fleet size will service the same demand, but it can result in a 47% to 77% increase in VKT; however, it dhe study did not consider the other trips, such as AVs needing to recharge batteries, to be maintained, and to be cleaned. Ref. ^[58][60] pointed out that shared AVs (SAVs) might become a preferable model choice for individuals, and SAVs could replace transit modes and release the car parking space. As a complementary mode, SAVs might be the solution for the first/last mile situation by enhancing the convenience of the mass transport system; therefore, integrated PT-SAVs systems, including demand sides, will become the future focus. Although AVs may encourage more people to use AVs instead of public transit, some US planning organizations have already established the policy of protecting the transit’s core strengths by funding projects to experiment with new approaches in terms of connecting people to public transit and promoting active and shared trips ^[48][50]. Ref. ^[59][61] had developed the integrated model by incorporating SAVs into the network by using Kuala Lumpur’s base traffic model in VISUM. By investigating waiting time, the operating cost of cars, and the cost of riding SAVs, SAVs could help passengers reduce walking time to the nearest PT station and reduce car’s VKT by 6% due to the mode shift from cars to PT. ItThis was showedstudy shows that SAVs can be used as traffic demand management, and the higher adoption of SAVs could decrease a car’s VKT through better SAVs-PT integration; however, extra VKT from charging activities should be considered in the future.

4. Factors That Affect Public to Adopt AVs

Ref. ^[60][62] discusses three key factors that cause people to accept AVs via a psychological model by testing driver behaviour and an online survey. These key factors are: perceived usefulness, perceived ease of use, and perceived safety. The experiment was conducted in China, and the candidates were testified in a level 3 automation vehicle, assuming that similar behaviour may be observed for the adoption of level 5 automation; however, only attitudinal acceptance was measured (not behavioural recognition), and it may not capture other factors, such as willingness to pay. A study in Switzerland  was [63] ^[61] showed that different people have different views about the level of automated smart cars. Most people believe that smart cars have some assisted driving functionality. The differences in public perceptions could lead to different adoption levels. In addition to that, the participants are young students who may be skewed in terms of accepting AVs, as they are more familiar with the technology. More factors should be taken into account for acceptance, such as demographic factors, vehicle-related factors, and people’s cognitive as well as behavioural responses. Unlike ^[62][64], it states that unemployed, less educated, and older people are unlikely to accept AVs, particularly in the EU area; however, it does not consider the technological advancement and corresponding attitude change over time. Another wasrticle researched gender and age and how they can affect attitudes towards AVs. Ref. ^[63][65] declared that women are consistently less willing to ride in driverless vehicles than males. The factors including familiarity, fun, value, complexity, and awareness of technology need to be considered. Ref. ^[64][66] engaged with similar onresearch, but it focused on plug-in electric vehicles. It demonstrates the first group of buyers who are likely to be highly educated, male, high income, and tech-savvy. Conversely, by conducting a short questionnaire of disabled residents in the UK, ref. ^[65][67] demonstrated that prior knowledge of AVs positively impacts adoption attitude, whereas age and income are not associated with the possibility of adopting AVs; however, it hhe research has restricted a number of explanatory variables; for example, housing conditions are not covered in the study. The results are similar to those of a survey study in Finland by ^[66][68]; however, ithe study ^[66]of [68] does not capture the view of the wider population due to the low response rate (20%), and Finland’s unique geographical factors, such as the fact that it is a sparsely populated and cold country. Ease of use, cost of technology, and perceived usefulness are the most critical factors that can determine the adoption of AVs. Another study was conducted in a European country, Norway ^[67][69], that used an online survey regarding the adoption of the driverless shuttle in a population that frequently uses a private vehicle. Unlike the other articles about driverless cars, the result indicates that better access to PT is not helpful in convincing people to adopt public transit, and that travel time with public transportation still remains a barrier to the adoption of driverless shuttles; however, itthe article does not address distrust issues towards driverless shuttles, as the public could perceive driverless vehicles as less risky than traditional buses. Ref. ^[68][70] investigated how performance expectancy, reliability, security, piracy, and trust can impact adoption by distributing online questionnaires in Australia. A pre-test was conducted to represent key experts in the field. The experiment includes several situations regarding AVs adoption, such as running in a closed environment, finding a car park, and riding on highways where drivers could have full control. ItThe wasstudy provided a unique insight into early test case environments for government agencies to evaluate the transport investments in the development of driverless technology. Ref. ^[62][64] assumed public acceptance of AVs is related to the attitude of new technology, as well as socio-economic and demographic factors. The attitudes towards AVs were determined by general attitudes toward robots tested through a questionnaire survey across Europe; however, it did not address how the attitude could change as technology evolves. Another similar onarticle ^[69][71] conducted face to face interviews and online surveys in Austin by asking questions about safety, short distance or long-distance trips, concern for data privacy, willingness to pay, residential location, mode of frequency, and so on. Although it presents some interesting results, such as the fact that only half of the respondents were likely to use AVs, there are some limitations. One of the limitations is that the participants were unaware of AVs’ future challenges and benefits, and how those factors can impact the transport network configuration. The factors are dynamic and cannot be answered by face to face interviews without experiencing the technology on the spot. Ref. ^[70][72] conducted a similar survey, which explores consumers’ attitudes towards autonomous, connected, and electric vehicles (ACEV). Consumers care more about financial cost and are less concerned about vehicle technology and data privacy. In addition to that, the reduction of driver fatigue is the biggest attraction, and accessibility of charging stations is the most critical reason for adoption. Ref. ^[71][73] conducted a comprehensive onestudy to investigate the factors affecting AVs purchase towards partial AV adoption and full AV adoption. Type of parking and housing, as well as socio-economic and demographic attributes, were found to significantly affect the likelihood of purchasing AVs. This observation will help policymakers devise policies to promote the adoption of AVs. Ref. ^[72][74] suggested that producing different AV models with different characteristics could match the personalities of products and consumers. For example, some people look for self-expression (quirky styling and a price premium). “Mini Cooper” could be a suitable design, and the design could facilitate the adoption of AVs. It is to be noted that it wahe studies discussed above do not investigate how the attitudes toward the adoption of AVs can impact travel patterns, VMT, and car ownership. Understanding those impacts may assist policymakers in developing strategies to promote the benefits of AVs as the technology evolves. Another interesting onestudy by ^[73][75] coupled the theory of “Diffusion of Innovation” (peer to peer communication) and agent-based modelling to predict the adoption of CAVs in the long term (25 year period) from 2025; however, ithe study does not cover market penetration change, multiple technology generations, and the interaction between AVs and human-controlled vehicles. A similar onestudy ^[26][28] used the combination of a vehicle technology diffusion model and a spatial travel demand model to assess travel behaviour impacts, AV penetration rates, and vehicle mileage by comparing Germany and the USA. The most important factor affecting public behaviour is the new automobile group, particularly for people with mobility impairments, given that there is a lack of a PT system and lower cost of fuel in the US ^[26][28]. Ref. ^[74][76] focstused on died the overtaking manoeuvre during the interaction of autonomous vehicles with conventional vehicles, and concluded that the acceptability of overtaking increases with a pull-in distance up to 28 m for both overtaking, and being overtaken. The interaction onestudy did not categorize the level of automation when the experiment conducted trials between AVs and conventional vehicles.

4.1. Willingness to Pay

Ref. ^[75][77] undertook more detailed research on the adoption of AVs by using online surveys. Nevertheless, it only focuses on WTP by investigating its potential demographic determinants as well as psychological determinants. This research was conducted in two major cities in China; therefore, it is specific to the local Chinese context and may not be representative of other geographic regions. More research is needed on a real driving simulation with different automation levels, examining how demographic factors can impact adoption. By conducting surveys in Germany ^[17][19], ita wasstudy showed that the participants were willing to pay 10.6% and 14.5% more for level 4 and level 5 automated vehicles, respectively. Although it wasthe study identified no significant main effect for gender, age could be an important factor, with people under the age of 24 willing to pay significantly more than other age groups. Ref. ^[76][78] focstused on died the no automation, partial automation, and full automation level of vehicles in terms of how households perceive the value of AV technology by using discrete choice experimental methodologies. It has been found that people from the US want to pay USD 3500 for partial automation, and USD 4900 for full automation; however, ref. ^[77][79] concludes that Indian people tend to adopt AVs more than American people. ItThe wasarticle investigateds the relationship between information type, willingness to ride, gender, and nationalities; therefore, the manufacturer should be concerned with developing the technology and focusing on promoting the technology in the media and among the public.

4.2. Value of Time

Ref. ^[78][80] compared the value of time of privately-owned vehicles and vehicles on demand by conducting stated choice approaches through the animation-based survey. The result shows that a privately-owned vehicle is more attractive than a shared one; however, as with other studies, it does not address the effect of the level of automation. Furthermore, it the study is restricted to Germany. Ref. ^[79][81] compared the value of time for work and leisure activities between conventional vehicles and AVs by utilizing a stated choices experiment in conjunction with the discrete choice model, which involves socio-demographic variables and behavioural intents for sets of questions. Although the value of time for leisure activities stays the same between conventional vehicles and AVs, the value of time (VoT) concerning work-related activities is found to be lower than conventional vehicles. This may explain the reason why people want to pay less money to reduce their travel time compared with conventional vehicle travellers when they are going to work. VoT can also impact willingness to share trips. Ride-sharing services could be a significant presence when AV technology matures. Ref. ^[80][82] utilized multivariate integrated choice, as well as the latent variable approach, to estimate individuals’ willingness to share AVs between commute trips and a leisure-activity trip. Privacy, time, and interest in better leveraging travel time are the three critical factors in this research. It concludes that people are less sensitive to commuting trips than leisure activities. Compared with conventional vehicles, ref. ^[81][83] concluded that the value of travel time savings (VTTS) for SAVs has a strong impact on the modal split; the total trip share for SAV mode increases from 1% to 5.5% and from 2.8% to 8.5% (reduced fare situation); however, the analysis was based on an agent-based model, which needs to consider the transport system and the real environment. The most substantial impact for SAVs on long trips is to allow productive activities in the car.

4.3. Trust

Ref. ^[82][84] discussed how trust in AV technology could impact the adoption of autonomous vehicles by examining behaviour during automated and manual driving scenarios, and demographic factors. The experiments tested eight scenarios, transferring from automated to manual driving conditions by measuring human behaviour, such as hands-on wheel response time; however, there is no proper way to measure trust relating to automated vehicle technology, and the most significant limitation is considered to be not using real automated vehicles.

4.4. Cost Structure

Ref. ^[83][85] investigated three generic operational structures, including PT, pooled, or individual and private cars, and their corresponding automation impacts. Fixed cost, variable cost, and fleet effects are considered; for example, average operating hours, occupancy, speed, and passenger trip length are quantified. However, it does not consider the level of automation, the demand for infrastructure, such as parking, and how AVs can impact the ownership of private cars, thus impacting cost structure. ItThe wasstudy concludeds that private cars still stand as an attractive option for AVs, although fleets of shared AVs may become cheaper than other modes. Other factors, such as travel time, comfort, waiting times, can still substantially impact mode choice.