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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 {Čśi}, 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.
Autonomes Fahren
Technische, rechtliche und gesellschaftliche Aspekte
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