Antonio Loro is an urban planner who focuses on the planning implications of emerging road vehicle automation technologies. He has conducted research with TransLink and Metrolinx on the potential impacts of automated vehicles, and is currently with the Ministry of Transportation of Ontario. This article was written by Antonio Loro in his personal capacity. The views expressed in this article are the author's own and do not necessarily represent the views of the previously mentioned organizations.
As efforts to develop automated vehicles continue to speed forward, researchers have begun to explore how driverless taxis in particular could play a prominent role in the future mix of urban transportation options. Some of these early findings raise the provocative argument that driverless taxis (or self-driving or fully-automated taxis, if you prefer) could hugely reduce or even eliminate the need for buses and trains. However, careful interpretation of this research reveals that vehicles with high passenger capacities (bus and rail transit, in other words) could be superseded by lower-capacity vehicles only where there is plenty of road space to spare. Where road lanes are in shorter supply, buses and trains – which could themselves get a huge productivity boost from automation – will continue to be indispensable for moving large volumes of passengers. In such cases, driverless taxis, especially share taxis, will be ideally suited to complement higher-capacity transit, generally by focusing on areas with a surplus of road space. And in the near-term, even before such advanced automation is perfected, automated buses could start improving mobility for large numbers of urban travelers.
Researchers have begun to explore future scenarios where vehicle automation technologies have advanced to a level that enables taxis to drive without human intervention through the full network of urban roads. Recently, the ITF (International Transport Forum), a think tank within the OECD, modeled a number of scenarios to examine how these driverless taxis, once they are commonplace, could serve urban travelers on a typical weekday in Lisbon. Among the outputs of their model, one is particularly attention-grabbing: even in a scenario where 8% of trips go by foot or bike, none go by transit, and the remaining 92% go by driverless taxis that serve single passengers, ITF researchers say that the number of taxis needed would be less than a quarter of the number of cars currently in use in the Portuguese capital. Such a reduction in the size of the overall fleet of cars in the city would greatly diminish parking demand. Unsurprisingly, though, in order to serve so many trips, the fleet of taxis would be used very intensively, and the total vehicle kilometres traveled (VKT) in the city would more than double.
Remarkably, the ITF team say that despite the upsurge in VKT in this scenario, travel times would be slowed very little. Underpinning this result is a noteworthy assumption in the model: currently, according to the ITF researchers, less than 40% of available capacity on Lisbon’s roads is in use during peak periods. (The authors caution that their figures are underestimates, as they do not account for bus travel, which makes up 13% of VKT in Lisbon.) The upshot is that even in a scenario where new taxi trips and empty taxis driving to their next passengers cause VKT to double, the ITF model’s outputs suggest that there should be road capacity to spare.
The model shows the previously very lightly used local roads absorbing much of the new VKT. Meanwhile, the most heavily traveled category of roads (“local traffic distributor roads”) rises from 43% to 69% of road capacity used. That 69% doesn’t necessarily mean traffic will be flowing smoothly, however. These percentages refer to the average capacity in use on different categories of roads – even if there is a low percentage of capacity in use on a given category of road, there could nevertheless be congestion focused at some locations on those roads. Interestingly, according to the TomTom Traffic Index, congestion may already be an issue in Lisbon, as travel times during peak periods are currently 45% to 70% longer than they would be under free-flow conditions.
The crucial implication to highlight here is that single-passenger driverless taxis could supplant buses and trains – but only where the roads have the capacity to absorb potentially huge increases in traffic without becoming congested.
A 2014 study by the Singapore-MIT Alliance for Research and Technology (SMART) arrived at results similar to those in the ITF study, though the SMART team modeled travel speeds in a much simpler way. The SMART researchers examined a scenario with single-passenger driverless taxis (or car-share vehicles) serving all trips in Singapore. They concluded that a fleet sized at one-third of the total number of passenger vehicles currently in use in the city could serve all trips while keeping peak period waiting times for the average passenger under 15 minutes. Such a dramatic result follows from a simplifying assumption in the model: the SMART team first estimated the current average speed that taxis drive at – including both the denser and the more dispersed areas of the city – and then assumed that future driverless taxis would drive at that particular speed, regardless of their location in the road network.
Less optimistically, the taxis would be bogged down in congestion of their own making. Currently in Singapore, 63% of peak period trips go via public transit. Shifting all of those trips to taxis would generate an abundance of new VKT – including VKT produced by empty taxis moving to their next passengers. Currently, peak period travel times on Singapore’s roads are 50% to 80% longer than they would be under free-flow conditions, according to TomTom; a massive increase in VKT wouldn’t help much.
A more recent article from the SMART team includes a brief exploration of the congestion effects of empty taxis repositioning themselves in a very simple road network. They find that their algorithm generally results in the empty vehicles traveling mainly on less busy roads, though the repositioning process could cause heavy congestion in networks where there is already congestion. This preliminary analysis suggests that much of the traffic produced by driverless taxi repositioning could be focused on the roads that are least congested to begin with. This suggestion lines up with the results of the ITF team. Interestingly, the ITF’s model of Lisbon has the biggest increases in traffic showing up on local streets in particular – which could unfortunately make them less attractive places to live, the authors caution. Such streets serve purposes other than being conduits for cars – they may be quiet routes for walking and cycling, or safe places for children to play, or inviting public spaces, for example – so injecting new car traffic could produce impacts other than congestion that are worth considering. Furthermore, while the SMART team suggests that much of the traffic created by repositioning per se might be focused on the roads that were previously least congested, the taxis that are actually carrying passengers could of course be the more important source of congestion – especially when large mode shifts, such as those seen in the scenarios described above, inevitably produce large VKT increases.
The ability of automated vehicles to use roads more efficiently could mitigate congestion resulting from increased VKT – with some caveats. Combining automation and V2V (vehicle-to-vehicle communication) technologies would enable vehicles to drive safely with short following gaps. However, the large potential capacity increases resulting from this “platooning”, where vehicles are grouped into closely-packed files, would mainly materialize on freeways, where traffic flows are less turbulent than on city streets. Automation with V2V could also boost flows through intersections by coordinating the movements of vehicles far more efficiently than traffic signals. These improvements would be constrained, though, wherever intersections are shared with entities not equipped with the requisite tech – not just cars, but pedestrians and cyclists as well. More radically, vehicles themselves could be smaller, thus occupying less road space, if the crash avoidance capabilities of automation eliminate the need for bulky, crashworthy construction. However, such a revolution in vehicle design would not be able to take over the streets until automation technologies are advanced enough and adopted widely enough to guarantee occupant safety.
The capacity limits of roads could be less of an impediment for multi-passenger driverless taxis. Hypothetically, if 8% of trips in Lisbon were taken on foot or bike and the remaining 92% were served by driverless taxis carrying multiple passengers (most commonly three to five, but as many as eight), the ITF team estimates peak period VKT would rise by 25%. It’s a substantial increase, but much smaller than the 103% jump in the single-passenger taxi scenario discussed above. Not surprisingly, providing public transit service would further mitigate VKT. In a scenario where 22% of trips go by subway, 8% go by foot or bike, and the remaining 70% go by driverless share taxi, the model estimates peak period VKT would rise by a relatively modest 9%.
Taxis serving multiple rather than single passengers would also mean an even smaller taxi fleet would suffice. Fewer cars in the city, kept busy serving dozens of trips a day, would drastically cut the need for parking. Sidewalks and bike lanes would be among the potential uses for the freed-up land. If taxi passengers share rides, and if 22% of trips go by public transit, the ITF figures that close to 95% of all parking spaces in Lisbon could be made redundant. (This outcome depends on traffic still flowing smoothly despite a 9% increase in VKT – if traffic is slowed, a larger taxi fleet would be needed to effectively serve all trips, so more parking would be needed during periods of low demand).
The discussion above just scratches the surface of the ITF and SMART studies – it’s definitely worth reading the original articles to explore their insights and to understand how the models were constructed. Higher-fidelity models that build on the ITF and SMART efforts will improve our estimates of the potential for driverless taxis to serve urban trips; however, even without complex models, it is clear that automation won’t eliminate the need for buses and trains when large numbers of people have to move through limited space. This is one of the straightforward geometric arguments that Jarrett has made before in this blog: larger vehicles fit more people into a given length and width of right-of-way than convoys of small vehicles can carry. (To illustrate, a freeway lane might have a capacity as high as 2400 cars per hour, while Bogotá’s TransMilenio bus rapid transit system has a capacity of 45,000 people per hour per direction.)
Of course, it’s a contentious question when we will attain the holy grail of automation technology sufficiently sophisticated to enable taxis or other vehicles to drive on any road in any conditions. It may appear further in the future than some suppose; nevertheless, even before this “Level 5” technology (as defined by the Society of Automotive Engineers) is mature, there will be vehicles capable of fully automated operation under more restricted conditions. There already are – such “Level 4” vehicles are currently capable of driving themselves at low speeds when segregated from challenging traffic environments. Beginning in the near-term, these kinds of low-speed automated vehicles, perhaps looking something like Google’s famously cute prototype, could carry passengers in settings like retirement communities. They could also circulate through networks of low-speed roads in suburban neighbourhoods or business parks to provide “first and last mile” access to and from transit routes.
Both in the near-term and the long-term, one of the most effective ways to reap the mobility benefits of automation will be to apply it to buses. Even with current technology, driverless operation would be feasible for buses running on busways with adequate exclusion of other vehicles and potential hazards. And even for buses on streets with mixed traffic, some of the technical challenges of achieving full automation are eased. For example, since bus routes run along only a small subset of the larger urban road network, the challenges of building and maintaining exquisitely detailed, meticulously annotated, continually updated maps would be substantially reduced – this would be advantageous for mapping-reliant approaches to automated driving, such as Google’s approach. The drop in labour costs from automation would enable dramatically increased frequencies, and the precision of automated control could also improve reliability. Because of the imminent potential to significantly improve mobility for large numbers of travelers, buses are a key priority for the application of automation.
With Level 5 automation, driverless taxis would become feasible. But rather than usurping the place of buses, they could play a complementary role. Level 5 would of course expand the domain of driverless buses, enabling them to provide service on any road; but it’s simply because buses can move numerous passengers in little road space that they will remain indispensable. Buses could ply heavily traveled corridors; driverless taxis could operate in less dense areas and during periods of lighter travel, whether serving complete trips or feeding into bus and train networks.
Interestingly, since low labour costs for driverless buses would enable higher frequencies, smaller vehicles would suffice to provide effective service in some areas. At a certain point, it could be difficult to distinguish in appearance between a small driverless bus and a driverless share taxi. However, their functions could be distinct: for example, driverless buses could provide frequent, predictable service on fixed routes according to schedules or headways, and driverless share taxis could provide on-demand, flexible route service. On the other hand, because diverting routes to pick up and drop off multiple passengers at numerous origins and destinations would cut into the travel time and cost benefits of taxi travel, some driverless share taxis could end up providing service with more fixed routing.
The take-home message from all this is that it’s critical to strategically deploy vehicle automation technologies according to their strengths and weaknesses. At some point, Level 5 automation will be achieved and driverless taxis will become feasible – but it’s important to think beyond just driverless taxis. To create the best possible mix of urban transportation options, it’s essential to consider the advantages and disadvantages of a range of potential automated vehicles and services – including buses, driverless taxis, and low-speed vehicles. Even more important, there's no need to wait for a Level 5 world with fully-fledged driverless taxis to appear before reaping the benefits of automation – and transit agencies have an opportunity to take the lead.
[My thoughts on this piece are at the top of the comments! — Jarrett]
Photo: Rotterdam driverless bus prototype, 2getthere.eu
Jarrett Walker here, sharing some thoughts I had while working with Antonio on this piece.
All models are radical simplifications that require many facets of an issue to be declared unimportant. Frequently these simplifications reflect the ideology of the modeler or sponsor. This is understandable. Models are still interesting so long as we read them as exploring the consequences of a set of assumptions, not as predictions about a complex and nonlinear future. Modelers themselves have the saying: “All models are wrong, but some are useful.”
Had I written this piece myself, I could easily have reported exactly the same information to support a different angle, namely: “Look at what bizarre assumptions modelers will make in their desperation to give the appearance that driverless taxis can replace transit!”
Antonio notes most of these cautions, but let’s just ponder one of the biggest for a minute. The models he discusses claim to discover substantial underutilized road capacity in major cities at peak hours. Effectively they find there to be roads that could carry more vehicles, even on the peak, than they are currently carrying.
What is all this mysterious surplus road space that these models are finding in dense cities? Some of it is local streets where children play. Much of it may be the same road space that cities all over the world reclaiming from the car, both to serve active modes and to create a public realm that makes their cities livable, humane, and equitable.
To presume that all this space is potentially available to vehicles, as these models do, constitutes a sweeping rejection of the entire urbanist agenda. Instead it imagines cities where all streets are rivers of traffic from curb to curb. This is a fairly dramatic and controversial assumption to be concealing inside a model, behind what looks like objective analysis.
Having said this, I agree with Antonio’s conclusion. Driverless taxis, if they ever exist, will certainly change the nature of urban space in ways that are good for both livability and mobility, especially by reducing parking. They will replace low-ridership transit but not high-ridership transit, because the latter will always be a better use of scarce urban space. And yes, by the time this technology is ready, buses, too, will be driverless and therefore vastly more abundant than in today’s labor-constrained model.
But for good reasons you’ll never hear me describe model outcomes as though they were facts, unless their assumptions (which is to say, facets assumed to be unimportant) are very clearly in view. This is not a disagreement with Antonio’s work, just a difference of focus and experience.
Interesting article and interesting take on it by yourself, Jarrett. Curiously absent from much of the debate on “driverless” technology is the knock-on effect on landuse. You hint at it through the assertion that street are more than just a conduit for vehicles; they are the most commonly encountered public places. Imagine if every steet – main street, side street, or back street – was maxed out with moving vehicles. Yuck! Moreover, the impact that this technology could have on land use would be similar to that during the advent of the private car and relatively sudden provision of a huge amount of road space in the mid twentieth century.
The car allowed for huge economic decentralisation, initially of wealthy people then subsequently for businesses and essential services. Will this happen again with driverless cars? I don’t see why not. The road infrastructure is there waiting to facilitate this mass re-suburbanisation. Unlike with the fixed routing of mass transit and rail transit in particular, driverless taxis will have no fixed routing. There will be no particular node for land use intensification. The idea of mini-downtowns emerging around transport hubs will diminish as all land will be more or less fair game.
Of course, planning controls can prevent unsustainable development patterns. These controls, however, have a habit of being implemented a little too late in many instances. What will be important is that governments must realise that driverless technology will be at best a supplement to established mass transit options – heavy rail, LRT, BRT, and of course the humble bus. Not to mention cycling and walking. The danger is that driverless taxis could be seen as an excuse not to invest in these traditional transport options. Sound familiar? Before we know it we could live in a world that is just as car dependent as the late twentieth century in the western world. Just replace gas-guzzlers with greenwashed driverless EVs. The landuse and social implications will be more or less the same.
One of the oft-touted benefits of driverless cars is that they will make more efficient use of roadspace by being able to operate in tighter configurations than can human-operated vehicles, not being constrained by the “two-second rule”.
Of course, while that may well apply to highways–where the two-second rule is many car-lengths–that would not apply to urban traffic, in which there aren’t gaps of that size.
One possible advantage an all-driverless fleet might have (in an urban environment) would be platoon operation at traffic lights–rather than the current practice of the first car in a pack starting at a green light, then the second once the first has made space, then the third–with the pack expanding and collapsing like an accordian, instead the pack can move in formation, as though the cars were all mechanically coupled together like a train. If that were doable, it would improve throughput somewhat, but I doubt it would add much to the overall capacity of urban streets.
“Some of it is local streets where children play.”
I hear this line a lot. Children playing in streets, how romantic it sounds.
During the day, most children are in school, and not playing in the street. In any case, there are public parks for this purpose.
Good article, Antonio. To build on Jarrett’s point regarding the shift of traffic to under-used streets: increasingly, progressive municipalities are actively discouraging through-traffic on residential streets — be it via speed bumps, bulge-outs, inconsistent one-way patterns, or other means. This seems to be common in residential urban areas with regular street grids, such as the Plateau in Montreal, or New Westminster in Vancouver. On these grids, traffic efficiency could be increased substantially if they were fully open to traffic, but, thankfully, that may be less and less likely to happen as cities increasingly think about liveability.
What about putting extra traffic on the winding streets and cul-de-sacs of the suburbs? Yes, there is excess(ive) free road space there, but the networks are so circuitous that it’s hard to imagine that any efficiency gains could be made. And of course, no self-respecting suburbanite would ever allow more traffic, or faster traffic, on their streets.
The question, then, is what kind of under-used streets would accept extra traffic and increase efficiency in doing so? It seems that the best candidates would be grid-based streets with low residential and commercial density — that is, with low political opposition to heavier traffic. By definition, these are not streets that have a lot of destinations for people; thus, traffic could only use them for a portion of their trips before having to merge back into busier streets. And if the new traffic always has to take busy streets at some point in their trips, would there actually be any efficiency gain overall?
The line about filling out residential streets with more (autonomous cars) is a very scary prospect and shows just how out of touch a lot of transportation modellers (and thus the models they produce) are.
Literally the next article I read after this one is this: http://nextcity.org/daily/entry/san-jose-street-event-new-york-central-park-closed-to-cars
The 2014 SMART study this article references is Fascinating and terrifying.
It spends several paragraphs on “Hidden cost of mobility”, which they define solely in terms of time spent by motor vehicle operators on activities required for vehicle ownership and zero paragraphs, sentences or words on the “hidden cost of mobility” for people outside the vehicle.
They suggest that the average american spends 175 hours a year walking to and from their vehicle, and then include this in a monetization calculation which justifies robot cars. The implication is that walking is a “cost”
In general they seem in thrall to US DoT definitions of mobility, focussed on Level of Service above all else.
There’s another way to get more capacity for cars without even having to resort to dumping them on quiet residential streets: don’t ever stop for pedestrians, and don’t have any pedestrian phases in the traffic signal system. Cars waste lots of time waiting for pedestrians to cross before they turn. If we could just get rid of those pesky pedestrians, there’s all kinds of free road capacity just waiting to be unlocked, especially in the most congested places like Manhattan.
Which just goes to show the limitations of models and the need to check assumptions that go into them.
What in this article changes if you delete the word “driverless”?
For the consumer driverless versus driven taxis make very little difference. Driverless taxis would be cheaper than current taxis? Perhaps? By how much? Maybe not? What would cause current car owners or transit users to switch, en masse?
While driverless vehicles may never replace driverless buses, perhaps they would eliminate paratransit driverless vans. I envision paratransit riders entering a pod at home or any location. A driverless vehicle would arrive and place this pod on its platform and take off. There would be rare occasions where the rider needs assistance to enter and leave the pods. I also envision that most people would no longer buy vehicles and simply call a car to get around. Package delivery drones would help reduce vehicle trips too, and yes eventually drones large enough to carry people.
Because nothing says “urban fabric” like a streetscape bereft of people below and a buzzing sky blackened by drones above.
Until we learn to take account externalities, we are never going to solve our transportation woes. The problems we have with cars are not technological, they are a failure to account for externalities, from public space, to noise, to safety, and of course pollution.
@ P:
Children certainly do play in streets, as I did on a daily basis with my friends in the US midwest in the 1990s. Parks are neither universally available to kids nor do they necessarily provide the same amenities (paved space for street hockey, rollerskating/blading, room to ride a bike etc). Whether kids typically play in the street only between certain hours is mostly irrelevant to the question. I suppose a hypothetical driverless taxi network that was designed only to use this “excess” capacity during school hours might be more palatable for residents, but the hours of say 9am to 4pm are likely to be fairly low-demand anyway and thus not require commandeering residential streets as alternative routes.
Anyway, on the whole, Jarrett’s point about the tradeoff hidden within the model is perfectly sound.
Platoons of cars doesn’t sound ideal from an urban permeability perspective. Bikes are often required to move out from a bike lane, and you’d be relying on driverless vehicles giving way (ok, same issue with drivers, but with shorter gaps, you’ve got to trust the robot to let you in).
Similarly, at least in Australia, it is legal to cross mid-block, more than 20m from a signalised crossing. It allows a freedom of movement that would be impossible with a ceaseless river of cars – literally uncrossable without a ‘bridge’, lights, zebra crossing, whatever. The kids may not play in the street, but they definitely cross it.
I haven’t read the reports yet, but my first thought was that of course there’s lots of extra peak-hour road capacity in many cities – outbound, away from downtown, the same place there’s generally extra transit capacity. Driverless taxis would replace downtown parking by sending vehicles back out to residential areas to make another trip, adding more miles in the currently less-used outbound direction. The extent to which this is good or bad for congestion, public space, and urban livability probably depends on the land use, road, and parking configuration of a particular urban area.
Driverless taxis would be most efficient in a city with an everywhere-to-everywhere land use pattern, which generally means cities that were designed for the automobile to begin with (and also some areas within larger cities, like midtown Manhattan). The effects and efficiencies of driverless vehicles would be very different in sprawling Houston or Phoenix or LA than in older, denser cities like Boston and DC that heavily rely on radial rail systems to manage large, single-direction flows of people in the peak hours. The results of a model that fits one type of city really can’t be extrapolated and applied to cities with different land use and travel patterns, since roadway capacity and patterns of congestion are so different. I’m not familiar enough with Lisbon or Singapore to be able to tell what the model results might mean for driverless vehicles in the US cities in which I live and work.
Even if they would otherwise consume downtown parking spaces, I can’t imagine driverless cars deadheading somewhere else is good for the environment.
Agree with Chad here on the driver or driverless point.
Taxis also provide a fairly low entry job market for drivers (especially with UBER and Co…)…or are they all going to convert to car mechanics and IT technicians to run the driverless taxis…or road workers to offset the increase in wear and tear.
Drastically reduce, either physically or through pricing, the supply of private car parking downtown and you would be halfway there to a more efficient use of taxi fleets, driverless or not
OK there’s a lot to unpack here. It’s amazing to me that this is framed in engineering terms, when the key issues are human factors.
First, in what universe will everyone decide to forego private ownership of their personal mobility? That’s a huge leap – yet discussions on driverless cars make that leap effortlessly. We are imagining and discussing a proposal that would be similar to all housing being suddenly socialized. It would be great, we could save a ton of money now wasted on financing property transfers and lower the cost of housing dramatically – but for some reason that’s not reasonable while giving up cars to private monopolies seems perfectly reasonable. And that assumption is critical to the rest of the discussion. Will we use these taxis to go hiking on the weekend?
(The end of private ownership is probably the only way to prevent people from driving their car to work and sending it home to their garage to avoid parking charges, or from having their car circle the block while they shop downtown for a half hour.)
Then we have to assume we’ll put up with control systems that subject riders to tiny headways and ram through intersections that no longer need signals because our smart cars can plan to navigate through gaps in opposing traffic in real time. And for pedestrians, not only do we need to expect and accept arterial volumes on local streets, but also non-stop platoons on shopping streets that become impossible to cross. (Frustrated pedestrians will quickly learn how to spook the auto-pilot into stopping on very short order – riders will need to adapt to that too.)
I’m not saying there aren’t benefits to automation that we don’t need to talk about or plan for, but I do feel we stink at managing this sort of disruption and we lose sight of the human factors that become someone’s externality. Providers of these services will have every incentive to promote their rider’s benefits over others in the right of way unless there is very strong government regulation. My preference would be to limit automation to highways and protected (or semi-protected) rights of way like busways. Or remembering that SkyTrain has already delivered this for transit in a very smart way.
Another big factor missing from the discussion is, even if driver-less car technology becomes widely available, the vast array of sensors to power it is not, and never will be free.
Even if the cost manages to decline from a few hundred thousand dollars to just a few thousand dollars, most people will still just buy the manually-driven car like they do today to save that few thousand dollars. Even today, people still drive cars with manual transmission, and manual transmission cars are noticeably cheaper than the identical make/model/year with automatic transmission.
Of course, the cost of the driverless technology will get passed down to the consumers of roboxtaxi services. The premise that a ride in a robotaxi will be significantly cheaper than a ride in a human-driven taxi (e.g. that the driverless technology is significantly cheaper than a human driver) is NOT a given.
It should also be noted that driverless cars and robotaxis will be popular long before driverless buses are allowed to see the light of day. The bus drivers’ union present in virtually every municipal transit system will make sure of that.
“Frustrated pedestrians will quickly learn how to spook the auto-pilot into stopping on very short order”
You can be sure they will. And unless they’re going to kill the young, the old, the disabled and the oblivious in huge numbers, automated vehicles will need to make emergency stops upon detecting pedestrians in front of them. I find it difficult to see how automated vehicles can operate efficiently in a congested environment with a lot of pedestrians without the draconian enforcement of jaywalking laws.
Very interesting article, thanks for sharing. I’m not sure that driverless technology is ready to apply to public transportation yet.
Anyway we are going in this direction, for sure
“I find it difficult to see how automated vehicles can operate efficiently in a congested environment with a lot of pedestrians without the draconian enforcement of jaywalking laws.”
I think that issue could be solved by the use of elevated or underground pedways. But that’s an enormous infrastructural investment in itself and it comes with costs to street life and local businesses. So if the driverless taxi model is assuming such an investment, that needs to be out in front.
Reading this was almost as good as being there! Thank you. Interesting topic. Nice thought of driverless taxi, buses and all. But at this point, I think its not time to apply this thought. Whatever but this technology thought is amazing. Great Post
Thank-you for this article. Based on a model of a shared fleet of autonomous cars in Canberra (around 380,000 people, very low density, very good road network), there is a very promising case for a shared fleet in which cars are concurrently shared (at least in peak hours), and thought of as mini-buses or shared taxis that take you door-to-door, 24×7, on demand. As the Lisbon study also showed, vehicle miles per passenger mile are higher than private passenger cars unless sharing is “enforced” by tariff incentives and policy (such as making the fleet smaller than required for “no waiting”, and deeming typical waits for a car of 2 minutes are OK in peak periods). Under such circumstances, vehicle miles per passenger mile do drop under the 100% private passenger car comparison.
However, it is impractical for them to drop below mass-transit comparisons, and hence it is hard to see how even a highly shared fleet of autonomous vehicles could replace high-speed and high-capacity mass transit in dense cities with already congested roads, at least without yet-to-be-proven improvements in lane and intersection utilisation (and even then, the improvements would need to be astounding).
But somewhat mitigating the high vehicle miles per passenger mile of a shared fleet of autonomous vehicles is the observation that in the circumstances in which the dead-running is the highest – asymmetrical AM and PM peak commuter flows when cars need to reposition constantly from drain to source – the roads they are using to reposition are otherwise under utilised (almost by definition – people don’t want to be travelling in that direction, which is why the repositioning vehicles are clocking up empty miles).
Canberra model: http://www.projectcomputing.com/resources/cacs/
Nice Blog
Also view at car transport
Obviously, the price of the driverless technology can get passed lower towards the consumers of robo taxi services. The idea that the ride inside a robo taxi is going to be considerably less expensive than a trip inside a human-driven taxi (e.g. the driverless technologies are considerably less expensive than an individual driver) isn’t a given.