Table 4.

Summary of the studies investigating the waiting time and trip costs of SAVs

Simulation study	Results
Burns, Jordan, and Scarborough (2012) [17]	The simulation model for Ann Arbor, Michigan, USA, to achieve a customer waiting time of two minutes or lower shows high-cost reduction from 21 $ to 2 $ (90% reduction) per day due to reduction in the ownership cost, operating expenses, parking fees and value of time
Burns, Jordan, and Scarborough (2012) [17]	Two simulation models for Babcock Ranch, Florida, USA, and Manhattan, New York, USA, showed radically low trip cost with a waiting time less than two minutes. In Manhattan, results show high-cost reduction from 7.8 $ per trip (using the traditional yellow taxi) to 0.8 $ per trip (88% reduction) due to the reduction in the ownership cost, operating expenses, and central coordination. For the Babcock Ranch case, the mobility service cost would be less than 3$ per day per person or 1$ per trip
International Transport Forum [16]	The simulation model for Lisbon, Portugal, shows that AVs can provide an average waiting time of 3.7 min
Zhang, et al. (2015) [19]	A simulation model for the City of Atlanta, USA, shows that AVs can provide an average waiting time of 0.12 min
Azevedo L, et al. (2016) [32]	The simulation model for Singapore shows that AVs can provide an average waiting time of 3 min
Bischoff, and Maciejewski (2016) [20]	The simulation model for Berlin, Germany shows that AVs can provide an average waiting time of 2.5 min and up to 5 min during the peaks
Hörl, Erath, and Axhausen (2016) [33]	The simulation model for the City of Sioux Falls, USA, shows that AVs can provide an average waiting time of 5 min during the off-peak and an average waiting time of 10 to 15 min during the peak periods
Moreno, et al. (2018) [22]	The simulation model for greater Munich metropolitan area, Germany shows an average waiting time of 5 min with 95% of the waiting time is lower than 10 min