## 2 Dimensions of Artificially Intelligent Transport Systems
### Shared Autonomous Mobility Solutions  
Preceded by existing, commercial, car-sharing rental services (e.g., ZipCar and Car2 Go), probably one of the most widely researched applications of autonomous vehicles as a solution to urban transit problems is the SAV. The concept of shared car mobility came from the relatively low ratio between the number of private vehicles owned and the actual number of vehicles in use during a typical day. [PERSON] et al. [2] suggested that approximately 10% of vehicles (models \(>\) 10 years) are on road on a representative day, which became slightly higher (17%) when newer vehicles (models \(\leq\) 10 years) were considered. Buying more cars is not the mobility solution for the present trip demands in most areas, and a transitioning towards a subscribed or pay-as-you-go vehicle in suitable areas may have more economical and environmental viability as the vehicle kilometres travelled are reduced. [PERSON] and [PERSON] [18] found that the ride-sharing users increased to be 890,000 in the U.S. by 2013 from 12,000 in 2002. A follow-up survey by [PERSON] and [PERSON] [19] found that around 104,000 shared vehicles are currently serving 4.8 million users across 1531 cities globally, with the fleet distribution in Figure 3, and with Mexico and Italy accounting for the highest (131:1) and second highest (107:1) member-vehicle ratios, respectively. To the end of on-demand, autonomous mobility or aTaxis, as dubbed by [PERSON] [20] complementing passenger transit in urban centres, a number of research works have been commissioned. For example, the replacement of private cars in metropolitan areas by SAV taxi services was analysed by [PERSON] [21] and [PERSON] [20], where passengers were required to travel to taxi stations, similar to the Car2 Go services. [PERSON] [22] in the metropolitan Stockholm region, focused on developing a framework to assist in dynamically allocating SAVs. This was achieved through the use of the  
Figure 3: Percentile carsharing membership and vehicle deployment distribution of shared vehicle services. present roadway network depiction, private commute-demand and multi-criteria evaluation (i.e., fleet size, travelling and waiting times), along with rerouting vacant vehicles based on varying values of these variables. The life-cycle analysis was performed to evaluate the environmental burdens of both traditional (diesel and petrol) and electric cars. Their results showed that by only utilising under 10% parking spots and private vehicles, an on-demand personal SAV transportation system can provide highly efficient \"door-to-door\" services. However, the results of the study are exceedingly hampered by the decision of users to accept ride-sharing. On average around a 15% increase in passenger travel times and 10 min starting time window was expected along with lower traffic congestion and environmental impacts, which greatly increased as ride-sharing was replaced with vacant car drives between trips. Results are compatible with the market need for a shared vehicle which gave rise to projects such as ZipCar, for example, which aims for a \"simple and responsible urban living\" [23], and is supported through smart cards and Android/iPhone apps for identifying vehicle location. Affordability has been cited [1] as the primary driving force for the popularity of ZipCar with millennials, and especially in densely populated urban areas, with approximately 470 cities supported by 10,000 cars or so. Therefore, the potential application of SAVs on the mobility spectrum is promising. Evaluation of the benefits of on-demand autonomous vehicles was conducted by [PERSON] and [PERSON] [11] and [PERSON] and [PERSON] [24]. Approximately 8.7% of the distances travelled by the 56,000 SAVs every day were found to be vacant, reduced to 4.5% by allowing for car-sharing.