> 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.
I'm struck by how many times you refer to rather blunt instruments (e.g. "scheduling drivers in advance" / "blocks of time in which they need to drive" / "quotas to meet"). There is a much wider canvas of tools available to guide and shape behavior, as I would guess you understand. (You seem to use language like an economist or business analyst, so you likely know about various incentive structures.)
Given how cutting-edge and innovative the ride-sharing companies claim to be, I don't think they can also authentically claim that they cannot find smart, incentive-based ways to work within slight adjustments to the business and regulatory environment. Market-making and matching algorithms are powerful, interesting, and applicable here, in a wide variety of regulatory configurations. In my view, the "ride-sharing" companies (after all, the driver is "sharing" her car with the passenger... right!) should leverage their people and infrastructure as a competitive advantage and, well, innovate.
> Just think about it - what happens when everyone has a self driving car? All of those cars that are otherwise parked are now on the road. Traffic. Same thing that happened pre-COVID with "ridesharing".
Explain this logic? To my understanding, ride sharing increased traffic because it increased demand for car travel. They did this by being a better choice than other modes of transit. But I believe the idea of ride sharing itself took 2-3 would-be individual riders, and put them in one car. The flip side was that the car would sometimes be empty, driving to its next customer, but that wasn't nearly as common as having multiple customers at once.
But holding constant demand for car travel, I don't see the logic for why FSD ride sharing would increase traffic. It would even free up an extra seat to add an additional passenger. And in theory, it would be able to drive more efficiently if it was an entire network of FSD vehicles.
> If you are willing to share the ride, you can have the same car serve you and someone else who shares a similar route and is willing to travel 10 minutes early (or late - can be incentivize by a discount).
What does the self-driving car change in that case? Wouldn't that be just the same as ordinary carpooling that we can do today?
> Ride-sharing is not an area that can be monopolized
It is very close. There are massive network effects and it will be hard to unseat an incumbent.
The more riders there are, the more revenue is available for the service provider (eg drivers). More riders means average waiting time is lower because more vehicles will be around. More riders means less downtime between riders since the next rider will be closer. More time in revenue service lets you spread fixed costs over more riders, reducing the fixed costs per ride (economies of scale).
This all means an incumbent will provide better service especially as reduced waiting times, and will be able to provide that service more cheaply due to greater utilisation. Their drivers/owners will also make more money due to greater utilisation. Newcomers will end up providing worse service (longer waiting times etc) and cost more!
> It’s well established that Uber and lyft INCREASE the amount of cars
>> Our findings provide evidence that after entering an urban area, ride-sharing services such as Uber significantly decrease traffic congestion time, congestion costs, and excessive fuel consumption. To further assess the robustness of the main results, we perform additional analyses including the use of alternative measures, instrumental variables, placebo tests, heterogeneous effects, and a relative time model with more granular data. We discuss a few plausible mechanisms to explain our findings as well as their implications for the platform-based sharing economy.
> Drives back empty to pick up parent to drive to work.
This implies there are no other people for which a rideshare would be suitable in either direction at any given time. This seems, like an unreasonable way to predict how mass AV would work. Again, demand is asymmetrical, which means there are opportunities for aggregation and overlap, when talking about mass adoption to the point that "the majority of cars on the road will have zero occupants".
There's the issue with demand for AV availability (ie traffic), which can never be reduced to zero. This would incentivize better judgement than "I'll stagger the school and work times"
Logically, why would AVs be 1-4 occupant vehicles? Once you have AV, you have AV vans and buses, similar to airport shuttling.
However, the reason for that is that the majority of people keep roughly the same hours. And the current system at least has the advantage that cars are generally near their owners rather than driving additional miles to get to centralized locations. Given various assumptions, utilization could certainly be increased--though we already have the ZipCar and taxi options--but I'm unconvinced that the pay-per-use economics are going to be as compelling as you think.
Ridesharing currently requires human drivers which limits them to where the drivers want to be and limits them to how many drivers are in your area.
When human drivers are not required you have neither of these problems. It will be significantly easier for ridesharing companies to provide availability in non-dense areas.
Hell, they'll be able to control where these vehicles go by algorithm, so it will simply be a matter of adding more vehicles to their fleet and the algorithm will take care of minimizing response time for everyone.
Presumably the car is going to pick someone up. That means it isn't parked being useless. If this starts working at scale, supply and demand will pull ridesharing costs down in a way that reduces ownership.
The longer the rider is willing to wait, the more the service can optimize the allocation of drivers. (Less distance driven without a passenger, more distance driven with multiple passengers in a car pool, more direct carpools.) This is a real elimination of waste whose value can be split between the service and the passenger, although it's not clear whether the size of that value is really at the 10-20% level.
Physics says if I don’t have my own car I’ll have to wait for one. The rideshare could leave a ton in the parking lot but that negates all the projected cost savings from higher utilization.
>I know it's only an anecdote, but myself (and many other colleagues) in Boston switched to periodically using rideshares to commute due to the decline in public transit quality.
I've been riding the T in Boston for the better part of 20 years now. My usage of rideshares is more related to convenience and timing than any change in MBTA service. A lot of trips I do require going into/out of the core city to make a lateral move, or to coordinate multiple modes with different headways, e.g. 5 minutes for subway, 10 minutes for one bus, 25 for the next. The result is that time in motion is similar to the rideshare, but I spend an equal amount of time waiting for a connection.
I'd really be curious to see the effect on trips that already have a direct or reasonably efficient multi-step itinerary, e.g. Davis to Charles/MGH, Sullivan to Harvard, etc.
>> there's no network effect or immediacy required
If there's enough "shared trips"[1] , there's a network effect.And actually the lack of immediacy makes more shared trips combinations possible.That's on the customers side.
And on the drivers side - more drivers you'll get, more competition, but also more trucks that are spread through space and time - which would help in finding an optimal driver - with the shortest distance to the next job , and with the end point of that job closer to "home".
> It's not a simple push-button order
Why ?
[1]Or even close to share trips - drive from A to B , and from somewhere near-B to C .Also shared trips could be less-than-truckload and that's probably why they aim at small/medium business first.
> Think about it this way: if you already own a car, why would you use ridesharing?
To avoid traffic, to avoid parking problems, to avoid driving drunk, to avoid leaving your car unattended in a dubious neighborhood, to avoid parking costs, etc...
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.
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