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40919.3
Evaluating AMoD systems
For high vehicle availability (say, 95 percent), one would need around 8,000 vehicles
(~70 percent of the current fleet size operating in Manhattan, which, based on taxi trip data,
we approximate as 85 percent of the total taxi fleet) at peak demand and 6,000 vehicles
at average demand. This suggests that an AMoD system with 8,000 vehicles would be able
to meet 95 percent of the taxi demand in Manhattan, assuming 5 percent of customers are
impatient and leave the system when a vehicle is not immediately available. However, in a
real system, customers would wait in line for the next vehicle rather than leave the system,
thus it is important to determine how vehicle availability relates to customer waiting times.
Customer waiting times are characterized through simulation, using the receding horizon
control scheme mentioned in Section 19.2.2.1. The time-varying system parameters Èœi,
pi j, and average speed are piecewise constant, and change each hour based on values esti-
mated from the taxi data. Travel times Ti j are based on average speed and Manhattan
distance between stations i and j, and self-driving vehicle rebalancing is performed every
15 minutes. Three sets of simulations are performed for 6,000, 7,000, and 8,000 vehicles,
and the resulting average waiting times are shown in Figure 19.4 (right). Specifically,
Figure 19.4 (right) shows that for a 7,000 vehicle fleet the peak averaged wait time is less
than 5 minutes (9–10 am) and, for 8,000 vehicles, the average wait time is only 2.5 minutes.
The simulation results show that high availability (90–95 percent) does indeed correspond
to low customer wait time and that an AMoD system with 7,000 to 8,000 vehicles (
roughly
70 percent of the size of the current taxi fleet) can provide adequate service with current
taxi demand levels in Manhattan.
19.3.2 Case Study II: AMoD in Singapore
This case study discusses an hypothetical deployment of an AMoD system to meet the
personal mobility needs of the entire population of Singapore [18]. The study, which should
be interpreted as a thought experiment to investigate the potential benefits of an AMoD
solution, addresses three main dimensions: (i) minimum fleet size to ensure system stabil-
ity (i.e., uniform boundedness of the number of outstanding customers), (ii) fleet size to
provide acceptable quality of service at current customer demand levels, and (iii) financial
estimates to assess economic feasibility. To support the analysis, three complementary data
sources are used, namely the 2008 Household Interview Travel Survey – HITS – (a com-
prehensive survey about transportation patterns conducted by the Land Transport Author-
ity in 2008 [34]), the Singapore Taxi Data – STD – database (a database of taxi records
collected over the course of a week in Singapore in 2012) and the Singapore Road Network
– SRD – (a graph-based representation of Singapore’s road network).
19.3.2.1 Minimum fleet sizing
The minimum fleet size needed to ensure stability is computed by applying equa-
tion (19.1), which was derived within the distributed approach. The first step is to process
the HITS, STD, and SRD data sources to estimate the arrival rate Ȝ, the average origin-
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