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Self-Driving Cars Will Transform the Human Environment

Compared to most of our capital investments, automobiles are underutilized assets. They are mainly active during peak hours and rarely for more than 10 percent of the day?in fact, most are used for less than one hour a day.1

Much of their capacity is also underused since cars typically display low levels of occupancy in each trip?often with only one occupant. Households put up with such levels of inefficiency in order to gain the benefits of comfortable, door-to-door, on-demand travel.

But soon the convergence of shared transport services - such as car sharing and ride-sharing - with self-driving vehicle technology is likely to eliminate this one-hundred-year-old inefficiency.

Until recently, shared transport services have been largely informal and ad-hoc, including household car-sharing and carpooling; but starting in the 1980s, new models of cooperative and commercial car sharing emerged.

These forms of car sharing allowed individuals to subscribe to shared fleets whose vehicles they could reserve, access, and use only when they need them. Pricing for these services is typically calculated on a per-hour or per-kilometer basis.

The business model, situated somewhere between traditional car-rental services and taxis, has proven popular in many urban areas since they allow individuals to have access to cars without necessarily owning one.

With the arrival of ubiquitous Internet access and dedicated app-based services, car-sharing has quickly grown in popularity and sophistication, and numerous successful services have been deployed around the world.

At the same time, there has been a development in terms of technological sophistication with ride-sharing services?especially for app-based on-demand services. These can take the form of taxi-like services or peer-to-peer real-time ride sharing.

As with app-based car sharing, these forms of ride-sharing have proven to be tremendously popular, and pioneering companies in this field have generated billions of dollars in market capitalization.

All of these services currently require a driver. So it seems interesting to examine what might be the next step in these services evolution, namely, their integration with self-driving technology. In order to quantify the economic implications of shared self-driving vehicles on a regional basis, the International Transport Forum at the OECD sponsored extensive investigations by several researchers.

They simulated the comprehensive impacts of the deployment of shared and self-driving vehicle fleets in five representative contexts:2

- First, a fleet of Shared Autonomous Vehicles as well as traditional automobiles in a city the size of Austin, Texas.

- Second, a scenario involving the complete removal of the entire private automobile vehicle fleet in Singapore, and its replacement with a shared self-driving fleet.

- A third study modeled the implementation of a fleet of autonomous taxis in New Jersey, based on origin-destination trips derived from travel surveys. These trips approximate the real trips made by people in New Jersey every day.

- A fourth study looked at the potential impact that the sharing of rides in automated taxis could have on taxi fleet operations in New York City. It did so by looking at the detailed origin, destination, and timing of every single taxi trip taken in the city over the course of a year and investigating which of these trips could have been shared, because riders were traveling from roughly the same areas to roughly the same destinations at approximately the same time.

- The fifth study examined a shared, self-driving, and centrally dispatched fleet of vehicles in three different environments: a mid-sized U.S. city (Ann Arbor, Michigan), a low-density suburban development (Babcock Ranch, Florida), and a large and densely-populated urban context (Manhattan, New York). It used travel survey-based data on average trip distances, trip-making rates (such as trips per hour), and travel speeds to help characterize travel in the regions studied. A combination of queuing, network, and simulation models was used to calculate travel patterns and vehicle requirements.

Building on this work, the OECD researchers conducted a full-scale simulation for Lisbon, Portugal, a city with approximately 565,000 inhabitants who take about 1.2 million trips each day in an area of 85 square kilometers.3

This study identified changes that might result from the large-scale adoption of a shared and self-driving fleet of vehicles. It explored two different self-driving vehicle concepts:

- TaxiBots, which are self-driving cars that can be shared simultaneously by several passengers.

- AutoVots, which pick-up and drop-off single passengers sequentially.

The report looked at the impacts on car fleet size, volume of travel, and parking requirements over two different time scales: a 24-hour average and for peak hours only.

The findings of this comprehensive simulation study were nothing short of amazing.4

1. Nearly the same mobility can be delivered with 10 percent of the cars. TaxiBots, combined with high-capacity public transportation, could remove nine out of every ten cars in a mid-sized European city. And nearly 80 percent of cars could be removed using the single-passenger AutoVots without high-capacity public transportation.

2. The overall volume of car travel is likely to increase in the world of automated, shared cars. A TaxiBot system with high-capacity public transportation is forecast to result in 6 percent more car-kilometers traveled than today, because these services would have to replace not only those provided by private cars and traditional taxis but also all those provided by buses. An AutoVot system in the absence of high-capacity public transportation would nearly double car-kilometers traveled. This is due to repositioning and servicing trips that would otherwise have been carried out by public transportation.

3. Impacts on congestion depend on system configuration. A TaxiBot system in combination with high-capacity public transportation uses 65 percent fewer vehicles during peak hours. An AutoVot system without public transportation would still remove 23 percent of the cars used today at peak hours. However, overall vehicle-kilometers traveled during peak periods would increase in comparison to today. For the TaxiBot with high-capacity public transportation scenario, this increase is relatively low, just 9 percent. For the AutoVot car sharing without high capacity public transportation scenario, the increase is significant, at 103 percent. While the former remains manageable, the latter would not be, given existing roadways.

4. Reduced parking needs will free up significant public and private space currently used for garages. In all cases examined, self-driving fleets completely remove the need for on-street parking. This is a significant amount of space, equivalent to 210 football fields or nearly 20 percent of the curb-to-curb street space in a mid-sized city like Lisbon. Additionally, up to 80 percent of off-street parking could be removed, generating new opportunities for alternative uses of this valuable space.

5. Ride-sharing with TaxiBots replaces more vehicles than car-sharing with AutoVots. An AutoVot fleet requires more vehicles than a TaxiBot system to provide the same level of mobility. AutoVots also require considerably more repositioning travel to deliver that mobility.

6. The size of the self-driving fleet needed is influenced by the availability of public transportation. Around 18 percent more TaxiBots and 26 percent more AutoVots are needed in scenarios without high-capacity public transportation, compared to scenarios where shared self-driving vehicles are deployed alongside high-capacity public transportation. Without public transportation, 5,000 additional cars are required for the TaxiBot system and another 12,000 in the AutoVot system. Car-kilometers traveled would increase by 13 percent and 24 percent, respectively.

Laws, infrastructure, and technology are likely to make limited deployment of self-driving vehicles a reality within five years. Research indicates that the economic implications of the ensuing transition will be enormous.

Given this trend, we offer the following forecasts for your consideration.

First, managing the transition to shared autonomous vehicles will be challenging.

If only 50 percent of car travel is carried out by shared self-driving vehicles and the remainder by traditional cars, total vehicle travel will increase between 30 percent and 90 percent. This holds true irrespective of the availability of high-capacity public transportation. Looking only at traffic during peak hours, the overall number of cars required increases in all but one scenario studied, namely TaxiBots with high-capacity public transportation.

Second, self-driving vehicles will change public transportation as we currently know it.

For small and medium-sized cities, it is conceivable that a shared fleet of self-driving vehicles could completely eliminate the need for traditional public transportation.

Third, the impact of self-driving shared fleets on urban mobility will be enormous, and it will be shaped by policy choices as well as deployment options.

Transportation policies can influence the type and size of the fleet, the mix between public transportation and shared vehicles, and ultimately, the amount of car travel, congestion, and emissions in the city.

Fourth, active management will be needed to lock in the benefits of freed space.

Shared vehicle fleets will free up significant amounts of space in a city. Prior experience indicates that this space must be proactively managed in order to ensure these benefits are fully reaped. Management strategies can include restricting access to this space by allocating it to specified commercial or recreational uses, such as delivery bays, bicycle tracks, or enlarged footpaths. Freed-up space in off-street parking could be used for urban logistics purposes, like distribution centers.

Fifth, improvements in road safety are almost certain, but environmental benefits will depend on vehicle technology.

The deployment of large-scale self-driving vehicle fleets will likely reduce both the number of crashes and crash severity, despite increases in overall levels of car travel. Environmental impacts remain tied to per kilometer emissions and thus will be dependent on the adoption of more fuel-efficient and less polluting technologies. TaxiBots and AutoVots are in use 12 hours and travel nearly 200 kilometers per day, compared to 50 minutes and 30 kilometers for privately owned cars. More intense use means shorter vehicle lifecycles and, thus, quicker adoption of new, cleaner technologies across the car fleet.

Sixth, new vehicle types and business models will be required to optimize the value created by this new transportation paradigm.

A drastic reduction in the number of cars needed would significantly impact car manufacturer business models. New services will develop under these conditions, but it is unclear who will manage them and how they will be monetized. The role of authorities, both regulatory and fiscal, will be important in guiding developments or potentially maintaining market barriers. Innovative maintenance programs could be a part of the monetization package developed for these services.

Seventh, public transportation, taxi operations, and urban transportation governance will have to adapt.

Shared self-driving car fleets will directly compete with urban taxi and public transportation services. Such fleets might become a new form of low-capacity, high-quality public transportation. This is likely to cause significant labor issues. However, there is no reason why the current public transportation operators or taxi companies could not take an active role in delivering these new services. Obviously, governance of transportation services, including concession rules and arrangements, will have to adapt.

Eighth, mixing fleets of shared self-driving vehicles and privately owned cars will not deliver the same benefits as a full TaxiBot/AutoVot fleet?but it still remains attractive and is likely to be the dominant configuration well see over the next two decades.

In all fleet-mixing scenarios examined by the OECD, overall vehicle travel was forecast to be higher. Also, vehicle numbers were projected to increase in three out of four peak hour scenarios. Improved traffic flow of automated cars would mitigate congestion up to a point. However, the public policy case for self-driving fleets alone may be difficult to make based solely on space and congestion benefits, due to the increase in overall travel volumes. Nonetheless, even in mixed scenarios, shared self-driving fleets could be a highly cost-effective alternative to traditional forms of public transportation, if the impacts of additional travel are mitigated. The deployment of shared self-driving automobile fleets may be easier in limited areas like business parks, campuses, planned communities, and islands, as well as in cities with low previous car ownership levels.

References
1. iBid.

http://gizmodo.com/uber-could-one-day-be-replaced-by-taxibots-1700520608