Seite - (000423) - in Autonomes Fahren - Technische, rechtliche und gesellschaftliche Aspekte

Autonomous Mobility-on-Demand Systems for Future Urban Mobility408 Collectively, the results presented in this section provide a preliminary, yet rigorous eval- uation of the benefits of AMoD systems based on real-world data. We mention that both case studies do not consider congestion effects – a preliminary discussion about these effects is presented in Section 19.4. 19.3.1 Case Study I: AMoD in New York City This case study applies the lumped approach to characterize how many self-driving vehi- cles in an AMoD system would be required to replace the current fleet of taxis in Manhat- tan while providing quality service at current customer demand levels [13]. In 2012, over 13,300 taxis in New York City made over 15 million trips a month or 500,000 trips a day, with around 85 percent of trips within Manhattan. The study uses taxi trip data collected on March 1, 2012 (the data is courtesy of the New York City Taxi & Limousine Commis- sion) consisting of 439,950 trips within Manhattan. First, trip origins and destinations are clustered into N = 100 stations, so that a customer is on average less than 300 m from the nearest station, or approximately a 3-minute walk. The system parameters such as arrival rates {Ȝi}, destination preferences {pi j} and travel times {Ti j} are estimated for each hour of the day using trip data between each pair of stations. Vehicle availability (i.e., probability of finding a vehicle when walking to a station) is calculated for three cases – peak demand (29,485 demands/hour, 7–8 pm), low demand (1,982 demands/hour, 4–5 am), and average demand (16,930 demands/hour, 4–5 pm). For each case, vehicle availability is calculated by solving the linear program discussed in Section 19.2.2.1 and then applying mean value analysis [29] techniques to recover vehicle availabilities. (The interested reader is referred to [13] for further details). The results are summarized in Figure 19.4. Fig. 19.4 Case study of New York City [13]. Left figure: Vehicle availability as a function of system size for 100 stations in Manhattan. Availability is calculated for peak demand (7–8 pm), low demand (4–5 am), and average demand (4–5 pm). Right figure: Average customer wait times over the course of a day, for systems of different sizes.