Page - (000425) - in Autonomes Fahren - Technische, rechtliche und gesellschaftliche Aspekte

Autonomous Mobility-on-Demand Systems for Future Urban Mobility410 destination distance EijO ijD [Y í X ], the demand distributions ijO and ijD, and the average velocity v. Given such quantities, equation (19.1) yields that at least 92,693 self-driving vehicles are required to ensure the transportation demand remains uniformly bounded. To gain an appreciation for the level of vehicle sharing possible in an AMoD system of this size, consider that at 1,144,400 households in Singapore, there would be roughly one shared car every 12.3 households. Note, however, that this should only be seen as a lower bound on the fleet size, since customer waiting times would be unacceptably high. 19.3.2.2 Fleet sizing for acceptable quality of service To ensure acceptable quality of service, one needs to increase the fleet size. To characterize such increase, we use the same techniques outlined in Section 19.3.1, which rely on the lumped approach. Vehicle availability is analyzed in two representative cases. The first is chosen as the 2–3 pm bin, since it is the one that is the closest to the “average” traffic con- dition. The second case considers the 7–8 am rush-hour peak. Results are summarized in Figure 19.5 (left). With about 200,000 vehicles, availability is about 90 percent on average, but drops to about 50 percent at peak times. With 300,000 vehicles in the fleet, availability is about 95 percent on average and about 72 percent at peak times. As in Section 19.3.1, waiting times are characterized through simulation. For 250,000 vehicles, the maximum wait time during peak hours is around 30 minutes, which is comparable with typical con- gestion delays during rush hour. With 300,000 vehicles, peak wait times are reduced to less than 15 minutes, see Figure 19.5 (right). To put these numbers into perspective, in 2011 there were 779,890 passenger vehicles operating in Singapore [35]. Hence, this case study suggests that an AMoD system can meet the personal mobility need of the entire population of Singapore with a number of robotic vehicles roughly equal to 1/3 of the current number of passenger vehicles. Fig. 19.5 Case study of Singapore [18]. Left figure: Performance curve with 100 regions, showing the availability of vehicles vs. the size of the system for both average demand (2–3 pm) and peak demand (7–8 am). Right figure: Average wait times over the course of a day, for systems of different sizes.