Shared Mobility: Current Practices and Guiding Principles

Chapter 3. Overview of Shared Mobility Services

Shared mobility is having a transformative impact on many cities by enhancing transportation accessibility, increasing multimodality, reducing vehicle ownership and vehicle miles traveled (VMT) in some cases, and providing new ways to access goods and services. Several trends are impacting the growth and mainstreaming of shared mobility, as highlighted below.

Carsharing

Table 1 in the appendix to this primer summarizes more than a dozen North American roundtrip carsharing studies. These include both third-party and operator-led evaluations. One of the most notable effects of roundtrip neighborhood carsharing is reduced vehicle ownership due to either sales or deferred purchases. Most of this shift in auto ownership is from single households becoming carless, followed by two-car households becoming one-car households. Numerous studies have examined the effect of carsharing on overall vehicle numbers and show reductions ranging from 4.6 to 20 personal vehicles per carsharing vehicle. Differences can be attributed to a range of methodological approaches (e.g., postponed purchases and sold vehicles).

The most current studies and member survey results released by U.S. and Canadian carsharing organizations show that up to 32 percent of carsharing members sold their personal vehicles, and between 25 percent and 71 percent of members avoided an auto purchase because of carsharing. A 2008 research study documented that 25 percent of members sold a vehicle and 25 percent of members postponed a vehicle purchase due to carsharing across a sample of approximately 9,500 participants (Martin & Shaheen, 2010). Of the participants, more than 80 percent had a bachelor’s degree and 54 percent had incomes exceeding $50,000 USD. Forty percent of respondents were between 18 and 30 years old, and 55 percent were between 31 and 60. See the Appendix A, Table 2 for a more comprehensive breakdown of carsharing member demographics. Variation can be attributed to a stated-intention bias, location-specific differences, and business model. Carsharing has also been shown to save its members an estimated $154 to $435 annually for U.S. members and $392 to $492 CAD for Canadian members.

Additionally, reductions in auto ownership are commonly associated with increased public transit ridership, walking, and bicycling modal shifts, as well as reduced parking demand and VMT or vehicle kilometers traveled (VKT). Twelve percent to 54 percent of carsharing participants in North America walk more often. Studies differ on whether or not carsharing increases or decreases public transit ridership. Studies of six individual locations across North American found that between 13.5 percent and 54 percent of carsharing participants take public transit more frequently. However, one study of approximately 9,500 participants across North America found a slight shift away from public transit ridership (Martin & Shaheen, 2010). (See Appendix A, Table 3.)

In the United States, the average carsharing member’s VMT/VKT is reduced by 7.6 percent to 79.8 percent. The large variation is likely attributable to differences in location, member use, and survey design. Martin and Shaheen (2010) found that VMT/VKT is reduced by 27 percent (observed impact, based on vehicles sold) to 43 percent (full impact, based on vehicles sold and postponed purchases combined) due to the before-and-after mean driving distance. Along with reduced VMT/VKT and vehicle ownership, low-emission fleets also contribute to lower greenhouse gas (GHG) emissions. This same study found a mean observed impact decline of 0.58 metric tons of GHG per year per household (impacts due to vehicles sold) and a full impact reduction of 0.84 metric tons of GHG per year per household (impacts due to sold and postponed vehicle purchases) or an average reduction of GHG emissions of 34 percent to 41 percent per year per household (Martin & Shaheen, 2010). Several global carsharing programs offer additional GHG reductions through partnerships with carbon-offset companies. Moreover, many members report an increase in environmental awareness after joining carsharing. Carsharing can also provide other beneficial societal impacts, such as the increased mobility afforded by one-way service models and access to vehicles for college students and low-income households.

Carsharing succeeds because it either provides consumers with better mobility or sufficient mobility at a reduced cost. The latter effect drives most of the emission and fuel-use reductions with travel substitutions replacing private vehicle use. Carsharing fundamentally changes the cost structure of driving from a fixed cost to a variable cost. Carsharing involves substituting "driving with driving" (i.e., a private auto with fixed costs versus a shared vehicle with variable costs), the magnitude of these changes must be measured to assess the fundamental carsharing impact. This is challenging given that we do not know who will join carsharing until after they have enrolled. Among the carsharing member population, we need to know: 1) how individuals traveled before and the modal behaviors they changed due to carsharing and 2) how individuals would have traveled in the absence of carsharing (e.g., postponed vehicle purchase). These effects are nearly impossible to measure without some form of member survey, as the best way to understand these shifts is to identify what happened.

From survey stated response data, researchers can generate an understanding of an individual’s travel lifestyle before enrollment, including miles/kilometers driven in personal vehicles, which is often challenging to gauge. In addition, the shifts due to carsharing are different for different people. Many individuals will invariably drive marginally more, and many do so as a result of carsharing. Others will drive substantially less, as they alter their engagement with the private auto to one of necessity rather than convenience. Measuring this effect through surveys is necessary because only the member can truly assess how the carsharing system has changed his/her life. For some, the system’s impact is inconsequential, and observed behavioral changes are the result of other unseen dynamics of which carsharing is merely a witness. For others, the system plays a central role in facilitating a lifestyle change that reduces aggregate fuel consumption and emissions. Although imperfect, the member survey is a key instrument for obtaining a before-and-after measure of carsharing impacts.

It is important to note that the application of data from national and regional travel surveys to the evaluation of shared mobility impacts is currently less feasible for a number of reasons. First, these surveys are generally snapshots of activity over large areas that may or may not have a robust range of shared mobility services. They generally lack longitudinal structure, which spans the period before and after a person begins using a system. Second, the subsample of people using shared mobility services within large surveys, such as the National Household Travel Survey (NHTS), is small, and the time between such surveys can be large-spanning years. People are rarely re-sampled in subsequent surveys. Because of these factors, use of national and regional surveys to evaluate the household-level change in behavior is limited.

Finally, activity data can only tell us how an individual used a particular shared mode in contrast to their total transportation behavior. For this reason, despite advances in technology that improve approaches to travel behavior measurement, surveys play (and likely will continue to play) a fundamental role in assessing causes of change and providing critical inputs to its measurement. A similar discussion is relevant to impact analyses of the other shared modes discussed in this primer.

Bikesharing

Like carsharing, bikesharing offers a number of environmental, social, and transportation-related benefits. It provides a low-carbon option for the first-and-last mile of a short-distance trip, providing a link for trips between home and public transit and/or transit stations and the workplace that are too far to walk, as well as a many-mile alternative. Potential bikesharing benefits include: 1) increased mobility; 2) cost savings from modal shifts; 3) low implementation and operational costs (e.g., in contrast to shuttle services); 4) reduced traffic congestion; 5) reduced fuel use; 6) increased use of public transit and alternative modes (e.g., rail, buses, taxis, carsharing, ridesharing); 7) increased health benefits; 8) greater environmental awareness; and 9) economic development. The ultimate goal of public bikesharing is to expand and integrate cycling into transportation systems so that it can more readily become a daily transportation mode (for commuting, personal trips, and recreation).

Although before-and-after studies documenting public bikesharing benefits are limited, a few programs have conducted user surveys to record program impacts. Table 4 in the Appendix presents a summary of these surveys, showing trips, distance traveled, and estimated carbon dioxide (CO2) reductions. The emission-reduction estimates vary substantially across studies due to different assumptions about user behavior, trip distribution, and trip substitution. Key assumptions that influence CO2 reduction estimates pertain to public bikesharing trips that displace automobile trips.

Although casual users (typically bikesharing users with passes for seven days or less) account for the majority of bikesharing riders, very limited studies of casual users have been conducted. Many bikesharing programs do not collect and retain information on casual users after the billing process is complete. As such, collecting demographic data and understanding casual user behavior remains a key challenge. One study conducted by Virginia Tech urban planning students documented key demographics of Capital Bikeshare casual users. Between September and October 2011, they completed an intercept survey at five Capital Bikeshare kiosks. The survey found that Capital Bikeshare casual user demographics closely mirrored its annual membership, serving predominantly Caucasian riders (Borecki, et al., 2012). Seventy-eight percent of casual users and 80 percent of annual members were white, compared to just 34 percent of the district’s population in the Washington, DC 2010 census. Table 5 in Appendix A compares intercept survey data of Capital Bikeshare’s casual users with their annual membership and census data. The survey also found that women were more likely to be casual users—52 percent, compared to just 33 percent who were annual members. Age and educational attainment were fairly comparable between annual members and casual users surveyed (Borecki, et al., 2012).

In 2012, the Transportation Sustainability Research Center at the University of California, Berkeley completed a study of long-term (annual and seasonal) bikesharing members in four areas—Minneapolis-Saint Paul, Montreal, Toronto, and Washington, DC. This was followed in 2013 by a second study of annual and seasonal bikesharing members in five cities—Mexico City, Minneapolis-Saint Paul, Montreal, Salt Lake City, and Toronto (Shaheen, et al., 2014). These studies found that compared to the general population, bikesharing members tend to be wealthier, more educated, younger, more Caucasian, and more male. See Appendix A, Tables 6 and 7, which depict member demographics in the United States, Canada, and Mexico City.

At the most basic level, both studies found the availability of bikesharing increased the frequency in which a bicycle was used by annual, season, and 30-day members. Furthermore, the majority of users in Canada and Mexico use bikesharing at least one to three times per week. Across the cities, 50 percent of members also drive less frequently due to bikesharing (see Appendix A, Figure 1). The results of both studies show an interesting split across cities. Respondents in small/medium-sized cities were more likely to use bikesharing in conjunction with public transit. In larger cities, both studies showed that bikesharing caused respondents to ride public transportation less. Importantly, the patterns are not a reflection of the different countries involved in the studies. Rather, there is an emerging distinction of impact arising from the type of cities in which bikesharing is deployed (i.e., larger, dense cities versus smaller, less dense cities). For instance, users employ bikesharing more for commuting purposes in larger cities and more for recreational purposes in smaller cities.

Both Minneapolis-Saint Paul, MN and Salt Lake City, UT are smaller cities with more limited light rail in contrast to the denser networks in Montreal and Toronto. Mexico City is similarly dense. Respondents in both Minneapolis-Saint Paul and Salt Lake City did not experience any change in bus use. In total, 67 percent of respondents in Minneapolis-Saint Paul and 87 percent of respondents in Salt Lake City indicated that bikesharing had no impact on their bus use. In terms of reduced bus use, 18 percent of respondents in Minneapolis-Saint Paul reported using the bus less often, while only 4 percent in Salt Lake City reported a similar change. In Minneapolis-Saint Paul, 16 percent noted increasing bus use, and 8 percent reported increasing bus use in Salt Lake City. Salt Lake City is the only city to report a net increase in bus use as a result of bikesharing. In Minneapolis-Saint Paul and Salt Lake City, bikesharing is reported to have increased rail use. In Montreal and Toronto, by contrast, 7 percent to 8 percent increased rail use, while 50 percent to 60 percent decreased rail use. In Mexico City, more people are decreasing rail use (17 percent) than increasing it (13 percent), but the difference is less. The primary reasons for this shift away from rail in Montreal, Toronto, and Mexico City are that bikesharing enables users to get to their destination more quickly and can be less expensive. Twenty-five percent, 48 percent, and 28 percent of respondents in these respective cities reported using rail less because bikesharing offered a lower cost and quicker transportation alternative. Forty percent of Salt Lake City respondents stated they took the train less because bikesharing was faster, and 50 percent of Minneapolis-St. Paul respondents said they used bikesharing because they wanted exercise. 

Finally, in addition to studies that have demonstrated reduced CO2 emissions and a modal shift toward bicycle use, evaluations indicate an increased public awareness of bikesharing as a viable transportation mode. A 2008 study found that 89 percent of Vélib’ bikesharing users said the program made it easier to travel through Paris (Vélib’, 2012). Fifty-nine percent of Nice Ride Minnesota bikesharing users said that they liked the convenience of bikesharing most about their program (SurveyGizmo, 2010). In 2011, Denver BCycle achieved a 30-percent increase in riders and a 97-percent increase in the number of rides taken over the previous year (Denver BCycle, 2011). These studies suggest that public bikesharing programs have a positive impact on bicycling as a transportation mode.

Ridesharing

At present, there are few published studies on the impacts of ridesharing (carpooling and vanpooling). Empirical evidence indicates that ridesharing can provide transportation, infrastructure, and environmental benefits, although the exact magnitude of these impacts is not well understood. Individually, ridesharing participants benefit from shared travel costs, travel-time savings from high occupancy vehicle lanes, reduced commute stress, and often preferential parking and other incentives.

Ridesourcing

Recent innovations in technology are enabling on-demand ridematching services and ridesourcing (also known as TNCs or ride-hailing) services where drivers and passengers can link-up using smartphone applications. In many cases, passengers can compensate drivers for fuel, parking, and other trip expenditures through these applications, including the driver’s time with ridesourcing applications. Public policy continues to evolve as on-demand ride services, such as uberX and Lyft, gain popularity. 

At present, there are few published studies on the impacts of on-demand ride services. A recent study of ridesourcing in the San Francisco Bay area found that survey respondents were generally younger than the overall population, although this may be influenced by the sampling method. Respondents were relatively well educated. Eighty-four percent of customers had a bachelor’s degree or higher, and survey respondents matched the income profile of San Franciscans fairly closely, with the exception that households making less than $30,000 were underrepresented as illustrated in Table 8 in Appendix A (Rayle et al., 2016).

The trip survey found that uberX provided the majority (53 percent) of rides, while other Uber services (black car, SUV) represented another 8 percent. Lyft provided 30 percent of trips, and the remainder were other services, which is consistent with anecdotal information on the market share of each service. Of all responses, 67 percent were social/leisure (bar, restaurant, concert, visit friends/family). Only 16 percent were work, 4 percent were to or from the airport, and 10 percent were to another destination (e.g., doctor’s appointment, volunteer). Forty-seven percent of trips began somewhere other than home or work (e.g., a restaurant, bar, gym), and 40 percent were home-based. Thirty-nine percent and 24 percent of survey respondents in the Bay Area stated they would have taken a taxi or a bus, respectively, if the uberX or Lyft were unavailable (see Table 9). Four percent of respondents named a specific public transit station as their origin or destination, and almost half (48 percent of trips) occurred on Friday or Saturday. Ridesourcing trips with a destination in San Francisco averaged 3.1 miles (4.9 km) in length compared to taxi trips averaging 3.7 miles (5.9 km). Finally, the study found that wait times tended to be substantially shorter than taxi hail and dispatch wait times. This study did not examine e-Hail taxi services, as they were not widely deployed at the time of the survey. There has been a dramatic rise in the city since then. As of October 2014, 80 percent of San Francisco taxis (1,450 taxis) were reportedly using the e-Hail app Flywheel, which brought taxi wait times closely in line with those of ridesourcing (Sachin Kansal, unpublished data). See Table 10 in Appendix A for more information on this comparison of ridesourcing and taxi trips travel times.

As noted earlier, the research on shared mobility impacts is somewhat limited and still evolving. Several modes have yet to be examined. Both emerging and existing services require further investigation, particularly at the city and regional level.

References

Borecki, N., Buck, D., Chung, P., Happ, P., Kushner, N., Maher, T., Buehler, R. (2012). Virginia TechCapital Bikeshare Study. Blacksburg: Virginia Tech.

Denver BCycle. (2011). 2011 Season Results. https://denver.bcycle.com/docs/librariesprovider34/default-document-library/annual-reports/dbs-2011-annual-report.pdf?sfvrsn=2

Martin, E., & Shaheen, S. (2010). Greenhouse Gas Emission Impacts of Carsharing in North America. San Jose: Mineta Transportation Institute.
Rayle, L., Dai, D., Chan, N., Cervero, R., and Shaheen, S. (2016). "Just A Better Taxi? A Survey-Based Comparison of Taxis, Transit, and Ridesourcing Services in San Francisco,"Transport Policy, Volume 45, pp. 168-178.

Shaheen, S., Martin, E., Chan, N., Cohen, A., and Pogodzinski, M. (2014). Public Bikesharing in North America During A Period of Rapid Expansion: Understanding Business Models, Industry Trends and User Impacts. San Jose: Mineta Transportation Institute.

SurveyGizmo. (2010). Nice Ride Subscriber Survey: Survey Report. https://appv3.sgizmo.com/reportsview/?key=102593-416326-6d13ea0276ea0822c9f59f4411b 6c779

Velib’. (2012, December 20). Un service qui plait plus que jamais!. Retrieved from JCDecaux: http://www.jcdecaux.com/fr/Presse/Communiques/2012/Ve-lib-un-service-qui-plai-t-plus-que-jamais-!