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The Future of Enhanced Mobility | The 4 ACES - Ridesharing can take growth to the next level

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Electric Vehicles
  • Connected Intelligence
  • Sustainable Growth and Tech Trends


Publication | Update:
Mar 2020

According to the Mckinsey perspective ‘The trends transforming mobility’s future’, the levels of disruption coming over the next dozen years are likely to exceed those of the previous 50 or more.

In this context, four trends - ACES: autonomous driving, connectivity, the electrification of vehicles, and shared mobility, are identified.

According to Jim Resnick of Wired the millennial generation are more comfortable with car-sharing programs and have shown a willingness to share or rent their own vehicles.

But the economics of ridesharing will be tough for providers and users alike. For consumers who drive more than about 3,500 miles a year—as some 90 to 95 percent of US car owners do—using your own vehicle is still the cheaper option. But that will change, almost certainly, when autonomous vehicles (AVs) are out in force.

According to the Mckinsey perspective, the ridesharing’s market share is still comparatively small; in the United States, the largest providers together account for only about 1 percent of total vehicle miles traveled (VMT).

Globally, billion has been invested in the industry in the past seven years. In the United States, this is a billion market and growing. across ten metropolitan areas that generate 0 million or more in yearly ridesharing revenues, and compound annual growth rates are north of 150 percent.

However at present, with the driver’s cut of each fare is typically much larger than the ridesharing company’s, and ridesharing companies are called to invest hundreds of millions of dollars each year developing AVs, is a tough ride.

Among high-income urban consumers, ridesharing is increasing

Data suggest that ridesharing’s most important demographic—urban adopters—are experiencing a fundamental conceptual shift about car ownership. as vehicle ownership declines, a phenomenon that may have broader implications for car ownership in the future.

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Ridesharing cost-per-mile has been settling in at about $ 2.50 in the United States, and ridesharing companies can increase both the total number of trips users take and the average number of miles per trip by providing solutions for additional use cases—such as shopping trips, deliveries, trips with children, group nights on the town, and shared commutes, to name just a few—for core urban customers and new customers too.

Cost-effective design improvements offer a way forward. Adaptable and reconfigurable vehicle interiors make rides more comfortable and more accessible, and shopping trips and deliveries easier. Design changes are especially compelling for commuters, seniors, and families and can help put ridesharing on a trajectory toward 7 to 10 percent of VMT by 2030.  A 2 to 3 percent share of VMT would increase ridesharing revenues by almost billion.

Robo-taxis can be 30 to 50 percent cheaper than owning a car if you factor in all the different costs around insurance, parking, and so forth.

When it comes to consider the robo-taxi market, Philipp Kampshoff, a partner in McKinsey’s Houston office and coleads the McKinsey Center for Future Mobility contests: “Robo-taxis have huge potential to disrupt the industry as we know it... We’re going to see that most likely in urban environments… The robo-taxi market is going to be a very interesting and attractive market. We see that already today with the movement toward shared mobility, where this is only going to pick up as we move toward robo-taxis. The market today is already 50 billion, and it will be a lot bigger once we move to robo-taxis. So the market is going to be competitive, and it’s not that simple, at this point in time, to say who is going to be the winner going forward.

“If you come from the customer angle, there is a good question around why you choose one provider over another. One of the key reasons you would choose one provider over another is availability of cars. Once you call a shared-mobility provider, how long does it take for the robo-taxi to actually show up at your house? So the shorter that time is, the more attractive the provider is for the customer. And in order to address that, scale is going to be important. If you have a large fleet and you can provide good coverage of the network, then obviously that is going to be helpful. The other angle is the price. If two cars are at my house at the same point in time, and the cars are basically the same, then I would probably go for the one that is cheaper".

“Now if we look at the operations side of things, co-operations are going to be very important—so having the right technology in terms of autonomous vehicles, having access to different car options. Maybe you don’t want to have the same car, the same layout, of every robo-taxi. But robo-taxis are maybe more purpose-built for certain applications. So you want to have more flexibility there. All of a sudden you’re in the business of managing a fleet of thousands of cars, maybe similar to rental cars today, where you actually have to maintain and service a huge fleet of cars at any given point in time. So being lean in your operations, making sure you service your cars in the most effective way, is going to be very critical for these companies.”

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The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
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Forecasts, Data modelling and indicator normalisation

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

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