For decades, many governments have tried to develop solutions to address traffic congestion, yet commute times continue to lengthen in most of America's urban centers. It’s time for a new approach.
New business models inspired by the sharing economy and disruptive technologies are ushering in an exciting new age in transportation: the era of smart mobility. The arrival of on-demand ride services like Uber and Lyft, real-time ridesharing services such as Carma and Zimride, carsharing programs such as Zipcar and car2go, bike sharing programs, and thousands of miles of new urban bike lanes are all changing how people get around.
Commuters no longer need to own a car to have one at their disposal. They don’t have to pre-arrange carpools to share a ride with others headed in the same direction. They needn’t wait for a ride home when it’s pouring down rain and there’s not an empty cab in sight.
For their part, automakers increasingly see themselves as both product manufacturers and mobility services companies. In addition to developing next-generation connected and autonomous vehicles that will improve traffic flows and safety, automakers are investing in a wide swath of new mobility services—everything from carsharing and rental services to multimodal trip-planning apps.
There’s no question that consumers have been the primary beneficiaries of new mobility services. The question facing urban planners is how today’s expanded mobility ecosystem can help advance public policy goals such as encouraging higher productivity and reducing congestion, while bringing related benefits such as fewer traffic accidents, better air quality, and a smaller urban footprint for parking.
Can alternative transportation modes help metropolitan areas reduce traffic congestion without spending tens of billions of dollars on new roads, tunnels, and light rail? And if so, what are the most promising strategies? Which approaches work best in which cities? How can automakers and transportation officials work together to address changing mobility needs?
These are just a few of the questions our analysis attempts to answer.
This study takes a data-driven look at what metropolitan areas can gain from expanded mobility ecosystems. We compare alternative approaches from ridesharing to biking, and explore how governments can focus scarce investment dollars on areas where they can do the most good.
The need for answers to America’s traffic gridlock problem becomes more acute each year. In much of the nation, traffic congestion has increased to alarming levels, with associated costs estimated at $121 billion, equivalent to slightly more than 1 percent of all annual US personal consumption.1
The average American spends about 34 hours every year sitting in traffic. That’s a whopping 5.5 billion hours for all commuters.2 The economic opportunity cost is staggering: $330 million daily, or about $124 billion every year. If nothing changes, this cost could grow to $186 billion by 2030.3
And that’s just the cost to individuals. Every mile we drive costs governments 7.5 cents, and at almost 3 trillion vehicle-miles traveled per year, those miles add up.4 If you include the cost of congestion, air pollution, or even lost property value near roadways, the total estimated external cost of driving runs between 27 cents and 55 cents per mile.5
For decades, governments have tried in vain to develop solutions to address congestion. High-occupancy vehicle (HOV) lanes and costly public transportation networks may have slowed the growth of congestion, but commute times continue to lengthen in America’s urban centers. Estimates suggest that only 15 percent in congestion savings can be achieved even with widespread deployment of such conventional measures to all major freeways.6
Clearly, a new approach is needed.
Helsinki, Finland has announced an audacious goal: By 2025, the city plans to make it unnecessary for any city resident to own a private car. The goal is an on-demand mobility system that would allow customers to choose among public and private transport providers and assemble the fastest or cheapest way of getting anywhere they need to go at any time.
“The city’s role is to enable that market to emerge,” explains Sonja Heikkilä, a transportation engineer with the city.7
Bus routes would be dynamic, changing based on current demand at any moment. From planning to payment, every element of the system would be accessible through mobile devices.8
Citizens could arrange a personalized travel experience irrespective of location. Wherever they are in the city, they could access a variety of options with their phone: a rideshare, an on-demand bus, an automated car, special transport for children, or traditional public transit. Residents could purchase “mobility packages” from private operators that would give them a host of options depending on weather, time of day, and demand.
Today, our congested transportation system is designed around infrastructure and vehicles: roads, bridges, subways, and buses. Helsinki’s 2025 vision points to a very different future model, one designed around individual mobility—moving each traveler from point A to point B as quickly and efficiently as possible. US cities also are beginning to reimagine their transportation ecosystems around this concept.
A transportation system designed around individual mobility would prominently feature four modes of alternative mobility (as well as more traditional modes such as buses):
See appendix A for a more detailed definition and description of each mode.
These forms of transportation have seen different levels of popularity in recent years. Ridesharing, as mentioned above, has been in decline for decades. On-demand ride services, on the other hand, have seen rapid growth since their launch several years ago.
Individual, corporate, and government incentives line up well for some of these modes, and poorly for others. Where the incentives align, we see faster growth (figure 1). Ridesharing, for example, suffers from a lack of both individual incentives to participate and private sector incentives for technological innovation. Carsharing’s growth has been more rapid due in part to automobile manufacturers’ entry into the carsharing business and relatively enthusiastic support from municipalities.
We now take a more detailed look at the current and future role that each of the four transport modes can play in addressing America’s traffic congestion problems.
To understand which cities—and even neighborhoods—stand to gain the most from better congestion reduction strategies, we examined a variety of data, most prominently commuter behavior at the census-tract level. Using data from the Census Bureau’s American Community Survey (ACS) and Census Transportation Planning Products (CTPP), we estimated the number of people who could reasonably rideshare or bike to work.11 We then tallied up how many vehicle miles traveled (VMT) and congestion-cost dollars would be saved if all of these commuters used alternative transportation.12
For carsharing, we used a slightly different approach. Using ACS data, we estimated the population in each tract matching the target demographics of carsharers.13 With these numbers, we estimated the number of neighborhoods nationwide with strong carsharing potential.14
On-demand ride services, the newest of the alternative transportation modes we studied, also offers the least available data.15 Our estimates for this mode are thus less detailed.
For each mode, we compare current usage rates with estimates representing our model’s maximum possible usage.
A more detailed description of our methodology for capturing the existing and potential reach of alternative mobility approaches can be found in appendix B.
For decades after World War II, the carpool to work was a daily ritual for millions of Americans, mostly suburban (and then, mostly male). Through the 1960s and into the mid-1970s, one in five workers carpooled to their jobs.16
How times have changed.
Most suburban—and even many urban—households now have at least two cars. More often than not, both parents drive into work by themselves, in separate cars. Today, fewer than one in ten commuters nationwide shares a ride to work.17 Fully 77 percent of Americans drive to work alone.
And, in contrast to the suburban business commuters of days past, carpooling today is often associated with lower-income workers with limited resources.18 Many of today’s carpoolers do so out of economic necessity rather than choice.
But the news on the ridesharing front isn’t all bad. Despite the 30-year decline in carpooling rates, several factors—new technologies enabling real-time ride matching, changing attitudes toward car ownership, the growth of the sharing economy, and an increasing number of managed lanes that provide incentives for carpooling—offer significant opportunities to revive ridesharing.
We analyzed ridesharing rates in 171 metro areas across the United States and identified some important factors that will help determine ridesharing’s future.
The beauty of ridesharing lies in the fact that it taps into an abundant yet underutilized resource: the empty seats in cars. Every day millions of Americans drive to work by themselves, in parallel with neighbors who very often are driving to similar locations. These empty seats in cars represent a huge source of waste in our transportation system—but potentially also a huge opportunity for improvement.
What is the potential impact from reducing this waste? To model potential rideshare savings in cities, we treat transportation choices as a function of fuel costs, congestion patterns, attitudes, and assembly costs. (See appendix B for a detailed description of our methodology.)19 We imagine a world in which assembly costs for ridesharing approach zero and societal attitudes toward ridesharing are more supportive. This scenario allows us to calculate the personal and societal benefits that could accrue if all commuters who could reasonably rideshare to work did so.
A detailed examination of our methodology can be found in appendix B, but here is the Cliff’s notes version. We used geospatial analysis of demographic data to calculate the number of likely ridematch pairs within each census tract who live within one mile of one another, leave for work at the same time in the morning, and travel to the same workplace tract. To account for commuters who engage in “trip chaining”—stopping along the way to and from work to carry out other business—we reduced this number by 16 percent.20 We then calculated the reduction in vehicle miles traveled (VMT) and fuel savings if the pair chooses to rideshare.
Figure 2 shows ridesharing’s economic potential, nationwide and for the 10 largest cities by projected number of new carpoolers.
We estimate that almost 19 million commuters in US metro areas could switch from driving to ridesharing if current barriers to ridesharing were eliminated, resulting in a 27 percent overall modal share.21 This switch would have enormous societal benefits: We project maximum potential savings from increased ridesharing at $30.3 billion annually. These savings would accrue from several sources: $15.8 billion in direct annual savings to new carpoolers due to reduced vehicle upkeep, $11.6 billion in indirect savings from lowered congestion costs, and $1.8 billion in reduced annual road infrastructure costs. Furthermore, traffic-related accidents could fall by 22,915 annually (yielding $847 million annual savings), while carbon dioxide emissions would fall by 9.1 million metric tons annually—yielding societal savings of $338 million.
To be clear, our estimates represent a best-case scenario that may take years to be fully realized. Our point is to show the vast, and currently mostly unrealized, potential of this mode of transportation. Our results further reveal some general trends indicating where ridesharing could be most effective.
An important finding of our study is that “ring” neighborhoods could become ridesharing hotspots. Neighborhoods with high ridesharing potential, according to our analysis, are usually distributed in a ring 10 to 15 miles outside each city’s urban core. These neighborhoods tend to have higher concentrations of commuters traveling each day to similar workplace destinations, both in the city center and in office parks and edge cities throughout the metro area. (Here’s a surprising fact about tomorrow’s ridesharing: It’s not only commutes from the suburbs to the city center which offer the best opportunities for increasing ridesharing. Many commuters who live in tracts that can be hotspots for ridesharing do not in fact work in a city center.22)
A classic example is Indianapolis, where neighborhoods with the highest numbers of potential new ridesharers are concentrated about 10 to 12 miles from the city center, in suburbs such as Carmel, Fishers, Greenwood, and Brownsburg (figure 3). The map in figure 3 shows census tracts with higher projected levels of ridesharers as darker blue. The bulls-eye pattern of carpool potential shows clearly here because there are relatively few physical boundaries near the city and no contiguous metro areas to complicate the pattern.
Our analysis demonstrates the enormous economic potential of ridesharing—$30.3 billion in annual savings if ridesharing were embraced by its maximum potential user base, or about a fifth of US commuters. So how do we get there? Experience teaches that it won’t be easy. The following strategies, however, could help make progress.
These strategies are relatively cheap compared to infrastructure, and are likely to offer significant returns on investment for state and local transportation officials.
To explore further our projections for ridesharing’s potential and current rideshareing policy and infrastructure, we invite you to view our interactive map.
Any American who visits European cities such as Amsterdam, Copenhagen, and Stockholm cannot help but marvel at the thousands of bike commuters streaming past them on the morning commute, in all types of weather. In these cities, up to half of all commuters bike to work each day, more than those who drive or take public transport.40
The story is very different in the United States, of course. Only 0.6 percent of commuters currently bike to work in the urban areas we study here. The good news is that bike commuting in America is growing by about 7.5 percent annually.41
Even so, given such low participation rates, it’s unsurprising that biking is far from the top of the list of transportation planners’ congestion reduction strategies. After all, most communities lack good biking infrastructure, and US commutes tend to be longer than those in other nations, which can discourage bike commuters.42 Our pervasive car culture also makes persuading Americans to give up the comfort of their cars daunting.
That said, the number of potential commuters in America’s metropolitan areas who could bike to work with relative ease is much higher than one might expect. A recent MIT analysis of several large cities, including Washington, DC, Philadelphia, and San Francisco, indicates that biking would be the fastest way to reach large portions of each city’s areas during rush hour.43
To estimate the potential economic returns of increased bike commuting, we created a geospatial model based on the assumption that anyone who works five or fewer miles from home could reasonably commute by bike, at least sometimes, given improved infrastructure, better commuter benefits, and sufficient societal encouragement. We chose five miles because that distance comprises 75 percent of all bike trips from the most recent nationally representative survey of commuting patterns.44 We reduced our projections to account for well-known determinants of bike commuting frequency such as trip-chaining, weather, and climate (for details of our modeling techniques, see appendix B).
Based on these assumptions, we estimate that a little less than a quarter of current commuters (28.3 million out of 108.4 million) could switch to bike commuting as one of their main modes of commuting if barriers to biking were substantially reduced. To be sure, an increase of this magnitude won’t happen anytime soon in America, but even much smaller increases would have large impacts on traffic congestion and health and wellness.
According to our analysis, the economic potential from increased bicycle commuting is almost as high as that from increased ridesharing. The potential annual VMT reduction from new bikers (13.1 billion VMT) would amount to almost 1/2 of 1 percent of all vehicle miles driven last year (2,950,402 million), according to the Federal Highway Administration. (see figure 4).
We recognize that few, if any, bike commuters will bike to work every day of the year. In fact, hours of daylight, weather, and climate will keep many from cycling as far or as often. We therefore apply a conservative annual frequency factor of 96 days per year to arrive at our predicted total mobility savings from biking of $27.6 billion.
As with our projections of savings from increased rideshare, projected bike commuting savings will come from several sources. New bike commuters would reap direct benefits of $7.7 billion through fuel savings and reduced vehicle ownership costs. Taking more cars off the road would benefit commuters nationwide, who stand to reap $17.1 billion in indirect savings due to reduced congestion-related fuel and time wastage. Cities stand to gain $2.6 billion annually in indirect savings based on lower road construction costs, reduced accidents, and lower carbon dioxide emissions (see appendix B for details of savings calculations).
Figure 4 shows the potential savings from increased bike commuting for the 10 largest metro areas in terms of number of potential new bike commuters.
It’s important to again underscore that these figures represent an idealized future state, a theoretical ceiling we could reach with improved biking infrastructure, technological changes, and societal forces that promote bike commuting. The barriers to bike commuting are substantially different from those affecting other alternate forms of transportation. We’ve already mentioned distance, climate, weather, and trip-chaining. Other factors that potential bike commuters often cite as keeping them out of the saddle include perceived and actual safety considerations, lack of dedicated bike lanes and infrastructure, fear of traffic, lack of daylight, and inconvenience.45
The map in figure 5 shows bike commuting potential in Fairfax County, VA, located just outside of Washington DC; darker shades indicate areas with greater potential.
The areas with higher concentrations of potential bike commuters cluster around suburban “edge cities” containing commercial centers such as Reston, Tysons Corner, Herndon, Manassas, and Woodbridge. The identity of some of the “hot spots” may be counterintuitive, particularly Tysons Corner, which used to be a national symbol of car-friendly and congested development. But these areas are typical of what we found in our nationwide study, and “bikeability” now forms a major part of Tysons Corner’s long-term development plan.46
Medium-density suburban neighborhoods located one to three miles away from thriving commercial developments offer surprisingly good opportunities for increasing bike ridership. Further down the I-267 Dulles Tollway is Reston Town Center, another car-friendly suburb that has begun planning for 13 bikeshare stations to sustain its economic growth and attract younger residents.47
Bike commuting’s potential value is not evenly distributed inside each metro area. The greatest potential benefits are likely to be in core urban centers and, perhaps surprisingly, in suburban neighborhoods near smaller commercial centers (see sidebar, “Bike commuting potential in Fairfax County, VA”).
Our research suggests nine ways in which cities can align incentives to accelerate the growth of bike commuting.
Carsharing programs are changing how urbanites across the country get around. These services give consumers all the benefits of automobile ownership without the attendant high fixed costs (including purchase, insurance, maintenance, and parking costs).
In its most basic form, carsharing is car rental by the hour. Providers include commercial entities such as car2go, owned by Daimler Benz; DriveNow, owned by BMW Inc; and Zipcar, owned by Avis. They also include private individuals who participate in peer-to-peer (P2P) carsharing programs, renting their personal vehicles for use (e.g., through avenues such as Getaround, RelayRides). These P2P programs can serve less dense and lower-income areas than their commercial counterparts, which require a certain level of population density and a certain demographic profile to be commercially viable.
The expansion of carsharing is visibly changing the transportation landscape in urban areas. While these programs have existed since the '90s, they have grown impressively in recent years, achieving a sizeable consumer base and prompting major automakers to acquire carsharing companies.
Last year, commercial carsharing membership in the United States rose by 34 percent to more than 1.3 million members, up from less than a million in the previous year. The nation’s commercial carshare fleet grew to more than 19,000 vehicles in 2014, an increase of more than 2,300 vehicles from 2013.71
The growing popularity of carsharing should come as no surprise, since autos are unlikely to be replaced anytime in the foreseeable future as the “personal vehicle” of choice. As Chris Borroni-Bird, co-author of Reinventing the Automobile: Personal Urban Mobility for the 21st Century, explains, “No other means of transportation offers the same valued combination of safety, comfort, convenience, utility, and choice of route and schedule.”72 More than 75 percent of US consumers still see the personal car as their preferred mode of transport, although this preference is lower among younger consumers.73 For Generation Y consumers, in particular, affordability and high operational and maintenance costs are enough to dissuade many from owning a vehicle, making carsharing programs an attractive alternative.74
Carsharing’s steady growth has been accompanied by a corresponding increase in studies of the phenomenon. The trends that will determine carsharing’s future thus are becoming clearer. Here are some highlights.
Carsharing services are a niche transportation option for certain demographic groups. Today’s typical carsharing participants live in urban neighborhoods with medium to high household densities and have relatively high education levels; a large proportion of them rent their homes. For carsharing services to be commercially viable, a new carshare “pod”—that is a fixed parking area for one or more carshare vehicles—needs a minimum of households from the target demographic within a half-mile.75
Carshare members eventually reduce the number of cars they own. While many members are already carless when they join carsharing programs, research shows that overall participants eventually reduce their average vehicle ownership from 0.47 to 0.24 vehicles per household, with one-car households that become carless constituting most of this shift.76 This shift typically takes place over several years.
Carsharing services are leading more Americans to forego vehicle purchases. As more Americans come to view carsharing as a viable alternative, they will forego the purchase of a vehicle. One analysis found that carsharing services led Americans to forego the purchase of 500,000 new or used cars between 2006 and the end of 2013.77
The congestion-relief potential of carsharing rises with the number of carsharing services. Studies show that carsharing significantly reduces the number of cars on the road. According to one estimate, each carsharing vehicle reduces the need for 9 to 13 private automobiles.78 At the same time, the average number of vehicle-miles traveled by carsharing members is also reduced, with estimates of the reduction ranging from 26.9 to 32.9 percent.79
The evolution and growing ubiquity of carsharing services should fuel continued growth. Previously, carsharing was limited to neighborhoods within half a mile of an available parking lot. This is no longer the case. Business models have evolved to include both point-to-point and round-trip systems, while parking options have expanded to include both on-street and dedicated spaces in an increasing number of new developments, increasing the flexibility and convenience of carsharing. As carsharing networks become denser and more ubiquitous, their attractiveness to vehicle-holding households will increase.
Changing consumer preferences will facilitate the growth of carsharing services. A recent global Deloitte survey of consumer attitudes and preferences revealed that, among 23,000 consumers in 19 countries (in both developed and developing markets), an average of about 50 percent of respondents did not consider personal cars as their preferred mode of transportation.80 The study showed that the views of younger Americans are often in line with those of their peers overseas. In the United States, just 64 percent of Generation Y consumers view the personal car as a preferred mode of transport.81 This shift in consumer preferences will further broaden the appeal of carsharing.
As with ridesharing and bicycle commuting, we modeled the maximum potential benefits of carsharing (see appendix B for details). Our method was simple. We identified neighborhoods nationwide where carsharing is likely to be feasible, using established criteria for where carsharing works and where it doesn’t.82 (We relaxed those criteria slightly to account for ongoing improvements to carsharing’s business models and efficiency that are increasing its reach.) Then we calculated the likely potential carsharing members in each of those neighborhoods, and estimated how many of their cars they would shed and how many fewer miles they would drive daily and annually once they join a carshare program.
In the United States, we estimate that carsharing could reduce nationwide vehicle ownership by nearly 2.1 million, or slightly more than 1 percent of the total number of vehicles in the United States in 2013, according to the Census Bureau. Academic research suggests that new carshare members would reduce their daily travel by 1.87 vehicle miles each over time, allowing us to calculate savings from congestion reduction, carbon emissions, and safety improvements.83
We project the potential annual savings from carsharing to reach a ceiling of $4.3 billion annually. These savings would come from different sources. Drivers who become carshare members would eventually save $1.4 billion in direct vehicle maintenance and upkeep costs as they reduce their own driving. Commuters nationwide would benefit from reduced congestion, avoiding $185 million worth of wasted fuel and $2.2 billion in time delay.
We project cities would save $366 million in annual deferred road construction costs, $77 million in accident avoidance, and $36 million in savings from almost 1 million metric tons of reduced carbon dioxide emissions.
As with real-time ridesharing and bike commuting, not all cities would benefit equally from carsharing. Figure 6 displays our estimates by metro area.
Figure 6 shows that the largest, most densely populated cities have the most to gain from increased carsharing. The New York City metro area could reduce its vehicle population by almost 3 percent if carsharing were fully implemented, and could potentially see carsharing membership as high as 13.2 percent of all commuters. VMT reductions from these new carsharers could lead to $1.4 billion in annual savings to New York City and its commuters, including $127 million in deferred annual road construction costs. Chicago, San Jose, San Francisco, Oakland, Washington, DC, Baltimore, and Boston are not far behind in their carsharing potential.
Figure 7 shows neighborhoods of the New York metropolitan area where carsharing is feasible, and where the predicted vehicle reductions from carsharing would be concentrated.
As figure 7 indicates, carsharing offers high potential benefits for cities such as Jersey City and Union City. Some New York-area cities are already moving in this direction. Hoboken rolled out its Corner Cars municipal carshare program in 2010 in partnership with Hertz, and saw immediate reductions in car ownership among members.84 White Plains, NY saw its first three Zipcar pods go live in 2012.85
Small college towns are fertile ground for vehicle reductions as well. Moscow, ID is a typical example of a college town with a high potential vehicle reduction from carsharing. For small towns in rural areas, carsharing’s most important benefit may be its role in attracting and retaining a young, educated workforce. A recent study of what makes communities desirable for Millennial workers found that 31 percent wanted a combination of trains, light rail, buses, carpooling, carsharing, ridesharing, bicycling, bike sharing, and walking as their primary ways of getting around. Between 23–39 percent of respondents, depending on where they lived, said they wanted their primary method of transportation in the future to be something other a personal car.86
On-demand ride services (also called ridesourcing or ride-hailing services) like Uber, Lyft, and Sidecar are creating new business models and reshaping transportation markets by allowing private individuals to sell rides to eager customers.
Many issues concerning on-demand transportation are being widely debated today, from their potentially disruptive impact on taxicab companies to their impact on reducing drunk driving. Because our focus here is on congestion and economic benefits, we focus more narrowly on traffic reduction.
The market for on-demand rides is relatively new and evolving rapidly. Substantive studies of it are rare. We have nevertheless identified some general trends likely to affect the future paths of these service providers.
Uber’s and Lyft’s ridership rose rapidly during the past two years. One study sifted through Uber and Lyft transaction records to find 25 percent monthly growth in ridership at both firms at the beginning of 2013.96 That growth declined to a still-impressive 10 percent monthly rate by the beginning of 2014, however, and most analysts seem to agree that on-demand services face significant new headwinds as competition stiffens, markets become saturated, and calls for regulation increase.97
The US Census Bureau does not distinguish on-demand ride services from other transportation modes when it collects statistics about commuting patterns.98 The Bureau of Labor Statistics lumps Uber and Lyft drivers together with taxi drivers in its national surveys of employment and wages.99 Uber, however, has recently signaled a new openness to releasing trip data.100 As data from on-demand providers and government increase, we’ll get a better sense of how these services fit into the broader mobility ecosystem.
The release of several years of complete data on New York City cab rides offers the possibility that, in the near future, we will be able to calculate nationwide potential economic benefits of on-demand car services to the extent that such services substitute shared rides for some taxi trips.
Further data will be needed, however. On-demand ride service providers recently began piloting programs that allow customers traveling similar routes to link up and share their ride.101 Uber estimates that such pooled services could remove up to a million vehicles from New York City streets, although the company has not specified its methodology.102
A recent study of New York City cab trips found that cumulative trip length could be cut by 30 percent with little inconvenience if passengers were willing to share their trip with another passenger traveling the same way.103 Another study found the average length of a trip in San Francisco in 2008 was just 4.2 kilometers.104 Yet another study counted taxi rides in New York City and found that passengers logged 3.4 million trips per week, while a separate dataset recorded 173 million trips in the city between January and December 2013, with an average distance of 8.3 miles.105
Such findings allow us to estimate that, if on-demand ride service providers could facilitate trip sharing for 30 percent of New York City’s trips, the total number of trips would be reduced by almost 52 million a year, leading to a rough estimate of 431.2 million VMT eliminated. A reduction of that magnitude implies congestion savings to commuters of $495 million annually with 14 million hours in delay saved, and infrastructure savings to New York City of $959 million on road construction over 25 years. We further estimate a 139 thousand-metric-ton annual reduction in carbon dioxide emissions and 350 fewer annual traffic accidents.
It’s worth noting that this estimate does not take into account the potential congestion reductions that would come from lower car ownership due to increased mobility provided by on-demand ride services. We await empirical studies of the magnitude of this effect.
Innovations within the transportation sector are being driven by the growing recognition that cars, once synonymous with freedom and mobility, have become victims of their own success. Today, traffic congestion limits and undermines mobility in metro areas across America and the world, imposing huge costs on individuals and society as a whole.
The basic problem confronting transportation planners is that adding new infrastructure to relieve congestion is a notoriously slow and costly process. This doesn’t mean that new roads, bridges, and tunnels aren’t needed in America—they are. However, given environmental issues, land acquisition, permits, eminent domain issues, and construction, such projects can take years, if not decades, to go from conception to delivery.
The arrival and increasing popularity of dynamic, smart mobility services offer promising new possibilities for making more efficient use of existing infrastructure. At a fraction of the cost of new roads, smart mobility ecosystems can help reduce gridlock, lower accident rates, improve air quality, and shrink the urban footprint required for parking.
A whole menu of services and transportation modes is becoming available to cities willing to use them to tackle congestion and access problems. Carsharing works best in dense urban cores. On-demand ride services are most effective in extending taxi service to underserved city areas. Ridesharing can often provide the greatest returns in a ring 10 to15 miles outside the city center. Bike commuting typically offers the greatest benefits in neighborhoods within the urban core and in clusters around suburban commercial centers.
We’ve shown how transportation agencies and governments can encourage each of these options. Cultivating and expanding smart mobility ecosystems will require us to rethink our transportation investments, shifting our focus from simply maximizing vehicle throughput to moving users as efficiently as possible through any of a variety of modes.
Increasing ridesharing, bike commuting, and carsharing will result in substantial economic savings for commuters as well as cities. Commuters would realize direct savings from lower vehicle operating costs, and they would also benefit from reduced time delays and fuel wastage. Cities would save on the infrastructure costs associated with constructing roads to handle increases in congestion. Cities would also benefit from fewer road accidents and lower carbon emissions. Our general methodology for calculating different types of savings is shown in figure 8 and described in detail below.
Our calculations of alternative mobility savings use two different units to represent metro areas: Combined Statistical Areas (CSAs) and Metropolitan Statistical Areas (MSAs), both defined by the Office of Management and Budget in 2013.114 CSAs are the units most often used by transportation planners to capture regional transportation phenomena. To the list of 166 (excluding Puerto Rico) CSAs delineated by the OMB, we added the five largest additional prominent metropolitan statistical areas (MSAs) that were not classified as CSAs: Phoenix-Mesa, AZ; Richmond, VA; San Antonio, TX; San Diego, CA; and Tampa, FL. The result is our list of 171 metro areas, which we use to calculate national totals for urban and suburban commuters and which cover approximately 77 percent of the 2013 total population of the United States.
Metropolitan Statistical Areas (MSAs) are generally slightly smaller than CSAs and are useful for comparing metro areas to one another. We analyzed the top 99 largest MSAs by 2013 population to derive city rankings.
We use GIS tools to aggregate commuting and other demographic information from the census tracts within each CSA or MSA. We then calculate the projected new members and expected reduction in vehicle miles traveled (VMT) for each of the alternative modes of transportation as described below.
To calculate potential new ridesharers and projected VMT savings from ridesharing, we use the 2006–2010 Census Transportation Planning Products (CTPP), which reports commuter flows from all census tracts to every other. We also use the 2012 American Community Survey (ACS) five-year estimates—specifically, the questions from Tables B08301 and B08302 on transportation mode and departure time for the journey to work. We combine these two sources of data to estimate the number of pairs of commuters in the tract that are travelling to the same other tract, leave for work within 30 minutes of one another, and live one mile or less from one another.115 The one-mile trip deviation constraint and the 30-minute constraint on joint time of departure for work follow Amey (2010).116
To calculate potential new bike commuters and projected VMT savings, we also use the 2006–2010 CTPP and the ACS 2012 five-year estimates—specifically, the questions from table B08301 on transportation mode.
We calculate potential new carshare members and projected VMT reductions from increased carsharing using a three-stage process. First, we identify census tracts nationwide where carsharing is feasible. Then, we estimate the potential maximum number of new carshare members in those neighborhoods. Finally, we estimate the likely reduction in VMT for the new carshare members.
Below, we describe how we calculate each component of the mobility savings in figure 8 from the projected VMT reductions.
Car operating costs saved. Commuters who drive their personal vehicles less can expect savings in expenses like maintenance, gas, oil changes, parking, and toll charges. To calculate personal savings to commuters who switch to ridesharing, bike commuting, or carsharing, we use the Internal Revenue Service 2015 reimbursement schedule for the average cost of operating a personal vehicle for a work commute: 56 cents per vehicle-mile.
Time delay saved. If many commuters switch to alternative modes of transportation, then overall congestion is expected to decrease and all commuters will save time. We estimate these time savings by fitting a regression model to the city-level congestion results provided by the Texas Transportation Institute’s (TTI’s) 2012 urban mobility report. We model TTI’s reported time delay per commuter (Y) as a function of VMT per commuter and dummies for CSA or MSA population size group—categorized as small (< 0.5 million population), medium (0.5–1 million), large (1–3 million), and very large (> 3 million). The regression results shown in figure 9 indicate that all variables are significant and explain 63 percent variance of the dependent variable. The model accuracy of 82 percent is fair. In the model, delay rises by 1.1 hours as average VMT per commuter rises by two miles.
We assign a dollar value to the time delay saved using a national average wage of $16.79, following TTI’s methodology.122
Fuel saved. If commuters in a city waste less time idling in traffic, they will waste correspondingly less fuel. We again model the results presented in the TTI 2012 urban mobility report to estimate how much fuel would be saved if commuters wasted less time stuck in traffic. TTI city-level figures for fuel consumption per commuter in gallons (Y) is estimated by regressing it against delay per commuter in hours. The regression result shows a strong model fit with adjusted R-squared of 91 percent and model accuracy of 92 percent.
The dollar value of fuel saved is obtained using the average gas price by CSA or MSA from the Bureau of Labor Statistics Current Prices Index.123
Carbon emissions reduced. We estimate carbon dioxide emissions reductions using standard carbon emissions factors published by the World Resources Institute.124 For bike commuting and carsharing, we multiply the expected reduction in VMT by the standard WRI emissions factor for passenger vehicles, 0.38420902 kilograms of CO2 per VMT, assuming 3 percent diesel fuel vehicles. For ridesharing, we use the same WRI emissions factor but reduce it slightly to account for the additional weight of the passenger in the carpool car, which reduces its efficiency slightly. Accordingly, we use the WRI emissions factor for freight, 0.297 kilograms CO2/short ton mile, assuming an average passenger weight of .075 short tons. We also assume 10 percent extra distance for the trip overall to pick up the passenger and reduce our estimate of VMT savings accordingly. To calculate the overall societal savings from reduced carbon dioxide emissions, we use the Office of Management and Budget estimate of $37 per metric ton of CO2.125
Road accidents reduced. Taking cars off the road means reducing the number of traffic accidents. A nationwide federal study of accidents found an average of 0.8114 accidents per million vehicle-miles traveled, with the following distribution of accident types:126
A separate study by the National Safety Council found that the average economic cost of traffic accidents in 2012 was $1,410,000 for fatal crashes, $78,900 for non-fatal injury crashes, and $8,900 for property damage-only crashes.127 We apply these accident rates and savings projections to our VMT reduction projections to yield the expected reduction in traffic accidents and associated cost savings. We recognize that the dollar value of savings reported here somewhat underestimates the comprehensive value that individuals are willing to pay to preserve their life and health.
Road construction cost saved. Removing cars from the road by reducing VMT will generate savings for cities by allowing them to avoid road construction costs. A recent study by the Reason Foundation estimates the cost of providing additional road capacity to relieve severe congestion. The study predicts the travel time index will increase by 65 percent in very large cities over 25 years. To relieve severe congestion in America’s 403 urban areas, 104,000 lane-miles of capacity will be needed at a cost of $533 billion over 25 years, or $21 billion per year. Translating those estimates into infrastructure costs per hour of congestion delay yields $2.76 as the cost to address an hour of delay.128 We multiply this factor by the expected reduction in delay per commuter by the number of commuters to arrive at city-level estimates of infrastructure savings.