| > given that computers already are used to match passengers and route cars, I'm not clear on why we would expect a drastic increase It depends on what the denominator is. If it's only the time between when a driver/auto-car "accepts" the ride and the end of the ride, there's little reason [1] to expect a drastic increase. However, if the denominator is the total time the driver is "on duty", which is, I believe, what is generally used to calculate rideshare drivers' effective hourly compensation, then my original point stands. That is, an auto-car can be "on duty" even while just sitting in storage. The current algorithm also doesn't tell rideshare drivers where to be while on duty, only routing them once a ride is requested. In the auto-car scenario, the computer has complete control, so a predictive algorithm could increase utilization, even if the denominator is time-in-motion. Whether any increase would be drastic is debatable, but there's opportunity for something. [1] Currently, the computer routing algorithm has an incentive to optimize for time at the expense of distance (since it's the driver who bears the expense of the unbilled distance, AFAIK). In the case of an auto-car, that perverse incentive would be absent, but I don't expect the difference to be huge. |
If you consider the robot to "work" for more hours than a human, that's great, but due to the lower average revenue, it needs to be cheaper in order to be competitive with humans. There's no way to move your self-driving car to the opposite side of the world for the night.
"Whether any increase would be drastic is debatable, but there's opportunity for something."
If self-driving cars are cheaper, it seems like that would lead to more of them driving longer hours than humans, which would lower the utilization rather than increase it.