- Smartphones interact with the other pillars of the sharing economy—excess capacity and urbanism—to unleash ride-sharing in cities
- Uber has seen rapid growth starting in 2013, thanks to the proliferation of smartphones
- Smartphones help reduce the “first mile” and “last mile” problem for travelers
A pair of previous articles in The Fuse (see here and here) explained how the sharing economy rests on three major elements: Excess Capacity, Urbanism, and Smartphones. Together, these elements constitute a “sharing economy engine,” in a three-way reciprocated interaction. This three-way interaction can be seen in the following info-graphic.
However, a puzzle remains: Excess capacity and urbanism have existed, respectively, for many decades. Yet the sharing economy has only truly taken off since 2010. For example, while leading sharing companies were founded in the late 2000s (for instance, AirBNB in 2008, Uber in 2009), and while data on sharing usage is relatively sparse, a study from Uber reveals remarkable growth between 2013 and 2016. The following chart shows exponential increases in Uber driver-partners, in the United States, quoted from a labor economics study written by professional economists who were provided with proprietary Uber data.
If excess vehicle capacity and urban density long precede Uber’s exponential growth of 2013 through 2016, what has changed? Smartphones. They were introduced in 2007 with the release of the iPhone, and the adoption curve of smartphones has roughly paralleled that of Uber. The Pew Research Center, a non-profit organization based in Washington D.C., has extensively researched the rise of the sharing economy. Pew surveys reveal massive growth of smartphone adoption among U.S. adults aged 18+.
The parallel shape of the above curves—for Uber and smartphones—presents an interesting question. Have smartphones and Uber been like a “lock and key”? Have smartphones been the component which has unlocked the latent sharing potential of excess vehicle capacity and urban density?
Smartphones interact with excess vehicle capacity in urban environments to facilitate the sharing economy. In general, the key factors are information, convenience, and flexibility, in that smartphones allow personalized, customizer ride-hailing from point-to-point, avoiding the need to use traditional fixed-guideway transit. Both individual and systemic implications result from this unprecedented flexibility.
Individual decision-making: Smartphones change the consumer calculus
In terms of formal transportation literature, scholars Boris Pushkarev and Jeffrey Zupan introduced a framework for consumer decision-making on which travel mode to choose in their 1977 book Public Transportation and Land Use Policy. Pushkarev and Zupan explained that when deciding whether to walk, bike, drive, ride transit, or not take a trip at all, people look at the trade-off between many competing “prices,” some financial and some intangible. These “prices” include:
- Price in money
- Price in travel time
- Price in access time and effort
- Price in discomfort and disamenity
Pushkarev and Zupan make the point that there is always a trade-off between these factors. For instance, consumers will accept some level of discomfort if they can reach their destination time-efficiently and at considerably lower prices. But if the ride is too uncomfortable, inconvenient, or slow, consumers may not use the service no matter how low the price.
Ride-sharing with smartphones has significantly altered the calculus of consumer decision-making.
In brief, ride-sharing with smartphones has significantly altered the calculus of consumer decision-making. Particularly in the largest urban areas, where carpool options such as UberPool and LyftLine are available, in which customers share rides, price in money has been held steady, in that price per ride approximates what customers were already paying for fixed-route transit. But all of the other “prices” are dramatically reduced. Customers may now hail a ride from their exact location, to their exact destination (reduces access time and effort, and reduces travel time), and ride in a private vehicle instead of a bus or train (reduces discomfort and disamenity).
Systemic context: Smartphones address “first-mile,” “last-mile,” and “double density”
In areas of dense population and high excess vehicle capacity, where ride-sharing blossomed once smartphones were adopted, a key factor has been the ability of smartphones to address classic problems of urban transportation in a fundamentally new manner. The access constraints traditionally known as “first-mile,” “last-mile,” and “double density” have been directly targeted by point-to-point, handheld ride-hailing. With smartphones, consumers no longer need to waste time walking to and from transit stations. Thus, shared travel has a much less strict requirement for density at both trip origin and trip destination.
Traditionally, transit has moved along fixed routes, which require both trip origin and trip destination to be within 20 minutes walking of a transit stop. Fixed-guideway transit services have often been unable to travel the “first mile” or “last mile,” meaning that users have needed to be situated in such a way that they can travel the first and last mile by themselves. In turn, this requires “double density” in terms of requiring high enough population and land-use density at both origin and destination, to make it worthwhile for transit services to place a frequent stop and for riders to be able to use transit to make their trips.
Smartphones can help reduce “first-mile” and “last-mile” issues.
In contrast, smartphones break through this limitation by allowing riders to hail a vehicle from wherever they are, and by enabling the driver to take the rider to a customized and very precise destination. It has never before been so. The “first-mile” and “last-mile” problems are eliminated; riders gain unprecedented convenience, as well as unprecedented visibility into trip times. While the “double-density” requirement still remains to some extent, in that a certain minimum amount of density is still needed to make ride-sharing profitable for drivers in a given area, the problem is significantly lessened by the circumvention of the “first-mile” and “last-mile” problem.
The new barriers: How low can ride-sharing prices go? How reliable is ride-sharing?
The new problem, rather, is cost: Is it worth it for consumers to hail a point-to-point ride if it may cost as much as $15 each way for certain longer rides (especially from city-to-suburb or suburb-to-suburb)? Or should consumers simply retain their vehicles and continue paying for gasoline, insurance, licensing, etc., and fold the cost of individual trips into the overall cost of owning and operating the vehicle?
A related problem is reliability: Can ride-share drivers be counted on to show up on-time, according to the app’s stated arrival time? How dependable is it? If a rider wants to go from one low-density place to another low-density place, and the driver doesn’t show up, then how likely is it that another driver will be able to show up in-time to meet the rider’s specific travel need for that particular trip?
Ride-sharing expansion to suburbs?
A key question is whether ways will be found to expand ride-sharing beyond crowded cities.
Nevertheless, such issues only highlight the future challenges and future potential for expanding ride-sharing. The key question is whether ways will be found to expand ride-sharing beyond crowded cities, where it may mostly replace transit trips and possibly increase VMT, to low-density suburbs where higher vehicle-occupancy per private vehicle trip—due to systemic adoption of ride-sharing—could hypothetically lead to a major reduction in VMT, if paradigmatic adoption of ride-sharing in suburbs were somehow feasible. Because suburbs contain over half of the American population, the question of suburban ride-sharing adoption is an important trend to monitor, for those interested in reducing VMT and fuel consumption.