Self-Driving Vehicles, Robo-Taxis, and the Urban Mobility Revolution

Self-Driving Vehicles, Robo-Taxis, and the Urban Mobility Revolution

          
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Self-Driving Vehicles, Robo-Taxis, and the Urban Mobility Revolution

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    Impact Scenarios: How Big Will the Revolution Be?

    No one can predict the future, of course. But autonomous technology is advancing fast. Its impact is far-reaching, complex, and multifaceted. It affects many parties, including consumers, the auto and technology industries, governments, policymakers, and urban planners. Some looking into the future is necessary, if only to begin to understand and plan for the ways in which things could pan out and for the ramifications for urban life and policy.

    As part of our research into the impact of SDVs, BCG and the Forum developed four scenarios for what the city of the future might look like. (See Exhibit 11.) These are clearly what-if hypotheses. Our goal was not so much to predict what the future will hold as to provide some insight into the possible impact of different mobility models on cities and their inhabitants. We hope that the possibilities that we project will help inform relevant aspects of policymaking as leaders design the mobility models of the future. (Please see the video that describes the city scenarios.)

    exhibit
    Scenario One: The Premium Car That Drives Itself

    Imagine a city similar to any major city of today. It’s 2030, and people are still using cars, taxis, bikes, motorbikes, and their feet, as well as public transportation to get around. The city is busy, congestion is commonplace, and a lot of space is dedicated to both on- and off-street parking. In addition to using traditional transportation, a growing number of people now own premium-segment SDVs. Indeed, every fourth new vehicle sold in the city is a self-driving car; price is a barrier to wider adoption. One in ten SDVs is shared privately, meaning, for example, that a family that in the past used two vehicles now has only one, which, because of its self-driving capabilities, fulfills all of the family’s mobility needs. Other automotive technologies have advanced as well. Every third private vehicle sold is now electric. The share of new sales of electric vehicles (EVs) used as taxis is even higher. The city offers incentives that encourage the use of battery-powered vehicles as taxis, and some 90% of the new taxis sold every year are electric.

    Although the changes to urban life in this scenario are more limited than in the others, there are some measureable benefits from SDVs, alternative power trains, and sharing. (See Exhibit 12.) Because a small percentage of the new SDVs are shared, there is a very small (about 1%) reduction in the number of vehicles in the city. Emissions drop by 9%, thanks to a higher share of zero-emission EVs. In addition, those SDVs that have internal-combustion engines are assumed to consume 20% less fuel than traditional cars with drivers. We assume SDVs will realize that fuel efficiency by maintaining continuous speeds, taking better routes, and accelerating and braking more smoothly. Even though there are slightly fewer vehicles in the system, there is no gain of parking space that can be repurposed, because the space that is freed up is now used for EV charging stations.

    exhibit

    The most positive benefit for the city is a reduction in accidents. Automated driving, especially in combination with connectivity, has the potential to significantly reduce the number of accidents, injuries, and fatalities. Since 90% of accidents today occur because of human error, even the moderate uptake of SDVs foreseen in this scenario could lead to a drop in accidents of almost 20%.

    Scenario Two: SDVs Rule the Streets

    In this scenario, the city takes a more active role in designing its urban mobility structure. It offers incentives for use of SDVs as well as electric powertrains. Within ten years, the percentage of SDVs in use rises to almost 60% of the private-vehicle and taxi fleet total. One in ten SDVs is shared by multiple individuals, and the total number of cars in the city falls by 8%. (See Exhibit 13.)

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    The city’s more aggressive policy results in greater societal benefits. Tailpipe emissions drop by almost 25%, and space dedicated to parking can be reduced by 5%. The high share of SDVs means there are 55% fewer accidents—an improvement that leads to further, indirect savings in such areas as hospital care.

    Scenario Three: Robo-Taxis Take Over

    Now let’s suppose that the city imposes disincentives for private-car ownership (a prohibitive tax on private vehicles, for example, or even an outright ban), with the specific goal of making the self-driving taxi (and traditional public transportation) the main means of motorized transportation. The biggest change is the nearly 50% drop in the number of cars, the result of people traveling mostly in shared cars rather than in privately owned vehicles. Because most of the taxis are electric, emissions decrease drastically (by about 80%). Furthermore, there are almost 90% fewer accidents, nearly 40% of parking space is freed up, and the city can use that space for other modes of personal mobility (for example, more and bigger bicycle lanes) and freight transportation—or simply for public leisure space. (See Exhibit 14.)

    exhibit

    There is a trade-off, however: overall vehicle distance traveled rises. This, we assume, is partly because some people switch from using large-capacity buses to low-occupancy SDVs, owing to increased convenience and the better end-to-end service that SDVs can provide. In addition, SDVs travel more distance empty than individually used cars. (Like a taxi today, a shared SDV will drive empty to pick up a passenger, drop him or her off, and drive empty to the next passenger. An SDV might also drive empty to search for a parking spot outside the city center.)

    We have assumed that the aggregate additional distance traveled could rise by as much as 50%. (Empty-vehicle taxi travel in New York City today is estimated to consume about 40% of drivers’ time.) If the shared SDV fleet in this city of the future is not electric, overall tailpipe emissions will likely increase despite SDVs’ having better fuel economy than conventional cars.

    These assumptions do not yet account for the additional vehicle miles traveled that could result from greater use of SDVs than of conventional vehicles. Improved convenience might lead plenty of people to choose SDVs over walking or biking for running errands, such as picking up their dry cleaning a few blocks away, or other purposes. Furthermore, the availability of the shared-SDV fleet might trigger demand from new segments: children, the elderly, and people with disabilities might see SDV travel as an attractive alternative to the public-transit system and more feasible than driving a car. If the price is sufficiently attractive, low-income citizens might use SDVs extensively as well.

    All in all, while fleets of shared SDVs may reduce the total number of vehicles in the city, they may not necessarily reduce the number of cars on the road at peak hours. We expect the net impact on congestion to be positive in most cities, although the changes—for example, higher throughput, fewer bottlenecks, and a reduction in traffic incidents—may come mostly from road efficiency improvements rather than the simple reduction in the number of vehicles or kilometers traveled on the city’s streets.

    Scenario Four: The Ride Sharing Revolution

    This scenario, which is the most transformative relative to the status quo, anticipates the most benefits and the biggest changes in consumer behavior and urban policy. The city could provide incentives for ride sharing as well as for using SDVs, reducing the number of traditional cars even further. Every self-driving taxi now averages two passengers instead of the 1.2 assumed in scenario three (the current average occupancy we used as the base figure for a traditional taxi). The potential payoff is enormous. In addition to the emissions reduction and the road safety improvement achieved in scenario three, the aggregate vehicle distance driven in the city falls dramatically, since almost every trip is now shared. (See Exhibit 15.) In addition to the parking space already gained, ride sharing frees up a lot of space on the streets at any given time and further lowers the number of cars needed to provide the same level of mobility to the population. Traffic efficiency improves, and there are nearly 60% fewer vehicles than today. In addition, the cost to the consumer of ride sharing mobility is likely a lot cheaper. The city of the future starts to take shape.

    exhibit
    Multiple Scenarios, Considerable Implications

    Our analysis makes it clear that the potential benefits for society are huge if SDVs are combined with ride sharing and electrification. A power train shift from internal combustion to electric engines is essential if cities want to cut tailpipe emissions, and ride sharing in urban areas is required to reduce the number of vehicles. Autonomous capabilities are the key to big improvements in road safety.

    These three factors—sharing, autonomous driving, and electrification—reinforce each other to facilitate fast adoption. Autonomous driving makes it much easier to share vehicles, and because their utilization rates are higher than those of private cars, shared vehicles are well suited to electric engines. Indeed, it will likely be easier to convince consumers to use EVs as part of a shared-mobility fleet than to get them to buy them—especially until there is widespread high-speed electric-charging infrastructure in place (which expanding use of SDVs in cities would facilitate). And, as our research suggests, consumers already expect SDVs to be electric.

    A few things are certain. One is that no single scenario will play out exactly as described above. Another is that each city will experiment with its own course, and, as a result, there will be many different trials (and probably a few errors), which will put varying demands on consumers, industry, and city leaders.