9 CHAPTER II LITERATURE REVIEW II.1 Introduction This chapter will discuss previous research on smart city, smart mobility, and autonomous vehicles. This chapter is divided into several sections. First, it discussed the previous research regarding smart city and smart mobility as Indonesia prepared the new capital city as smart city and smart mobility. Followed by the discussion about autonomous vehicles, which is the highlighted mode of transport that provided in IKN. Second the is position of this study relative to previous studies demonstrates the gaps in the literature and the originality of this study. Last, it discussed the attributes or factors that influence people’s decision in choosing autonomous vehicles that will form the hypotheses and general conceptual framework. II.2 Smart City Many cities around the world have been relying on information and communications technology (ICT) for more efficient and effective city management and to establish resilient, sustainable, smart, and inclusive urban development (Savastano et al., 2022). Smart city concept was first introduced in 1990 in order to incorporate advanced information and communication technology (ICT) based hardware and software in urban planning (Bibri & Krogstie, 2017). Smart city utilizes ICT to enhance ‘citizens’ quality of life, foster economy, facilitate a process to resolve transport and traffic problems through proper management, encourage a clean and sustainable environment, and provide accessible interaction with the relevant authority of the government (Ismagilova et al., 2019). However, the definition of smart city (SC) itself according to Zhuhadar et al., (2017) are those cities that have the greatest quality of life and economic wellbeing for their citizens. Yeh (2017) stated that a city is designated as smart if it balances economic, social, and environmental development, and if it links up to democratic processes through a participatory government. SC involves the implementation and development of information and communication technology (ICT) infrastructures to support social and urban growth through improving the economy, citizens’ involvement, and government efficiency. Zoonen, (2016) in a smart city, ICT-infused infrastructures enable the extensive monitoring and steering of city of city maintenance, mobility, air and water quality, energy usage, visitor movements, neighbourhood sentiment, and so on. Peng et al., (2017) stated smart cities are essentially built by utilising a set of advanced information and communication technologies (ICT) including smart hardware devices (e.g., wireless sensors, smart meters, smart vehicles, and smart phones), mobile networks (e.g., Wi-Fi, 3G/4G/5G network), data storage technologies (e.g., 10 data warehouse, cloud platform), and software applications (e.g., back-office control systems, mobile apps, big data, and analytical tools). II.3 Smart Mobility Smart city policies hold out the promise of improving the quality of life for citizens. A key strategic area for such sustainability investment focuses on the introduction of smart mobility solutions. The motivations for this focus is quite clear because more than 90% of the worlds population live in locations where air pollution fails to meet the agency’s guidelines (Richter et al., 2022). Appio et al., (2019) also mentions that one of the key motivations of smart city projects is to improve the current state of congestion in most urban areas. Solutions range from autonomous vehicles that reduce the need for car ownership to deploying sensors in critical urban infrastructures such as roads, rails, subways, bridges, tunnels, seaports, and airports. These sensors can provide valuable data on how to fluidify traffic, reduce accidents, improve public transport and make parking faster and easier. Long before self-driving cars become the norm, Vehicular Social Networks (VSNs) are emerging as one of the main short-term smart mobility trends (Ning et al., 2017). VSNs (such as the community around Google’s Waze app) can integrate GPS data from thousands real- time drivers and their smartphones with anomaly detection mechanism (both human and algorithmic). In a near future, vehicle to vehicle and vehicle-to-infrastructure communication frameworks will complete this ecosystem to enable not only one more accurate traffic information but also better cooperative navigation solutions, car sharing, theft control, safety warnings, and cruise control (Appio., 2019). Furthermore, some researchers have discussed the issue in the context of intelligent transportation systems (ITS) and how these can benefit smart cities (Adart et al., 2017; Dimitrakopoulos & Demestichas, 2010). Study from Adart et al., (2017) discussed urban traffic management and proposed solution to help road users reach their destination by avoiding road congestion. The proposal was succesful in testing simple cases of modelling, but does not cover all complex scenarios faced by road users. Lee et al., (2017) proposed a new method that allows tracking of moving vehicles robustly in real time. By using simulations the study proposed that the method can effectibely track multiple vehicles by predicting the next probable centroid area of tracker. As transportation accounts for nearly one-quarter of all greenhouse gases (Conibear, et al., 2020). Investing in smart mobility helps to further the goals of a smart city strategy by presenting environmentally friendly transportation (Zawieska & Pieriegud, 2018). New 11 emerging smart mobility technologies are thought to have a significant impact on social changes as well as on people’s lives in the cities of the future (Gurumurthy & Kockelman, 2020; Marletto, 2019). Autonomous vehicles (AVs) are regarded as one possible technology that may prove to be part of a multi-faceted approach to support achievement of smart city goals (Woo et al., 2021). II.4 Autonomous Vehicle The personal automobile powered by an internal combustion engine ushered in a decades-long era of personal freedom and collective prosperity. But with that freedom came a number of challenges form of pollution, congestion, and accidents. According to the World Health Organization, more than 1.3 million people die each year in traffic accidents across the globe. According to the National Highway Traffic Safety Administration, 94 percent of crashes are caused by human error. Advanced technologies such as all-electric autonomous cars are poised to alleviate these challenges. From the bottom line, electric vehicles allow for simpler integration of the advanced technologies required for the cleanest and safest operation of autonomous vehicles. In the long term, building all-electric vehicles with autonomous capabilities integrated from beginning is the most efficient way to unlock the tremendous potential societal benefits of self-driving cars (General Motors, 2022). Autonomous vehicle or self-driving vehicles are defined as “those in which operation of the vehicle occurs without direct driver input to control the steering, acceleration, and braking, and are designed to that the driver is not expected to constantly monitor the roadway while operating in self-driving mode. Autonomous vehicle (AVs) also can be defined as vehicles where some aspects of safety-critical control function (e.g., steering, throttle, or braking) occur without direct driver input by the use of technologies such as sensors, cameras, light detection, and global positioning systems (GPS) (Zmud & Sener, 2016). Fully autonomous vehicles (AVs) also hold the potential to reshape the urban transportation landscape by significantly reducing traffic congestion, GHG emission, and requirement parking areas while improving the overall traffic safety and road network performance (Bansal & Kockleman, 2017; Hossain & Fatmi, 2022; Menon et al., 2019). Autonomous vehicle (AV) technology offers the possibility of fundamentally changing transportation. Technological advancements are creating a continuum between conventional, fully human-driven vehicles and AVs, which partially or fully drive themselves and which may ultimately require no driver et all. The technologies that also enable a vehicle to assist and 12 make decisions for a human driver, such as crash warning systems, adaptive cruise control (ACC), lane keeping systems and self-parking technology (Anderson, et al., 2016). Fully AV are promising technology contributing to better safety with fewer accidents. However, AVs are not necessarily fully autonomous. The National Highway Traffic Safety Administration (NTHSA) has identified different levels of automation around AVs (Zmud & Sener, 2016). Those are: 1. Level 0 (Momentary Driver Assistance): System provides momentary driving assistance, like warning and alerts, or emergency safety interventions while driver remains fully engaged and attentive.