Earlier this year, we wrote about how economic, environmental, and social forces have quickly given rise to shared mobility – the shared use of a vehicle, bicycle, or other low-speed travel mode. The role of shared mobility in the broader landscape of urban mobility has become a frequent topic of discussion. Major shared modes—such as bikesharing, carsharing, ridesouring (e.g., Lyft/Uber), and ridesharing (carpooling and vanpooling)—are changing how people travel and are having a transformative effect on cities around the world.
A number of social, environmental, and behavioral impacts have been attributed to shared mobility, and an increasing body of empirical evidence supports many of these relationships—although more research is needed. The various effects can be grouped into four categories:
1) Travel behavior,
3) Land use, and
In recent years, climate action planning has further raised awareness among local governments of shared mobility as a transportation strategy, along with its potential impacts—both positive and negative—on the transportation network. Here’s what’s been documented on four of the shared modes:
With carsharing, individuals gain the benefits of private-vehicle use without the costs and responsibilities of ownership. Individuals typically access vehicles by joining an organization that maintains a fleet of cars and light trucks deployed in lots located within neighborhoods and at public transit stations, employment centers, and colleges and universities. Typically, the carsharing operator provides gasoline, parking, and maintenance. Generally, participants pay a fee each time they use a vehicle. Carsharing can include roundtrip services (where a vehicle is returned to its origin) and one-way services (where a vehicle is returned to a different location).
A number of academic and industry studies of shared mobility have documented the impacts of carsharing. These studies have indicated that carsharing can lead to:
• Sold vehicles or foregone vehicle purchases;
• Increased use of some alternative transportation modes (e.g., walking, biking);
• Reduced vehicle miles/kilometers traveled (VMT/VKT);
• Increased access and mobility for formerly carless households;
• Reduced fuel consumption and greenhouse gas (GHG) emissions; and
• Greater environmental awareness.
One North American multi-operator study of roundtrip carsharing found that each roundtrip carsharing vehicle results in 9 to 13 vehicles on average taken off the road—including sold and postponed auto purchases. The study also found that roundtrip carsharing had a neutral to negative impact on public transit ridership. The study found that for every 5 members that used rail less, 4 used rail more, and for every 10 members that took the bus less, almost 9 took it more. The study also indicated a positive impact on non-motorized modes and carpooling, with more roundtrip carsharing members increasing walking, biking, and carpooling use than decreasing it. Finally, the study found that respondents reduced their average annual GHG emissions per household by 0.58 metric tons for the observed impact (based on vehicles sold) and 0.84 metric tons for the full impact (based on vehicles sold and postponed purchases combined). This is the equivalent of a 34 to 41 percent decline on average in GHG emissions per household.
Similarly, a five-city study, of car2go in North America representing a free-floating one-way carsharing model found that each free-floating one-way carsharing vehicle resulted in the removal of 7 to 11 vehicles on average from the road, including vehicles sold and foregone and postponed vehicle purchases. However, the number of vehicles shed or suppressed varied considerably by metropolitan region. Correspondingly, reductions in GHG emissions ranged from four (Calgary) to 18 percent (Washington, DC) on average. In four of the five cities surveyed, a majority of respondents stated that one-way carsharing had no impact on their public transit use. While the study found a slight overall decline in public transit use, carsharing members exhibited an increase in use of active modes, such as walking. Location-specific variations—including urban density, public transit service and availability, socio-demographics, and cultural norms—contribute to these modal shifts, and they are likely to result in impacts differences by location. For those respondents who used public transit less, the primary reason was that one-way carsharing is faster. Respondents that used public transit more reported that this was due to accessing carsharing as for first-and-last-mile connectivity to transit.
With bikesharing, individuals gain access to bicycles on an “as-needed” basis without the costs and responsibilities of bike ownership. Since information technology-based bikesharing emerged ten years ago, three models have taken form: 1) station-based bikesharing; 2) dockless (or free-floating bikesharing); and 3) hybrid bikesharing systems. In station-based bikesharing systems, users access bicycles via unattended kiosks offering one-way service (i.e., bicycles can be returned to any kiosk). In a dockless bikesharing system, users may check out a bicycle and return it to any location within a predefined geographic region. In a hybrid bikesharing system, users can check out a bicycle from a kiosk and end their trip either returning it to a kiosk or a non-kiosk location or users can pick up any dockless bicycle and either return it to a kiosk or any non-kiosk location. Bikesharing provides a variety of pickup and drop-off locations, enabling an on-demand and very low-emission form of mobility. The majority of bikesharing operators cover the costs of bicycle maintenance, storage, and parking.
Early documented impacts of bikesharing include increased mobility, reduced GHG emissions, decreased automobile use, economic development, and health benefits. Research has shown that public bikesharing typically reduces driving and taxi use, while increasing cycling in most cities. One study found that half of all bikesharing members report reducing their personal automobile use.
Bikesharing has been shown to complement, compete, or both with public transit depending on the context and local factors; nevertheless, more research is needed. One geospatial study of bikesharing mapped modal shifts and found that shifts away from public transportation were most prominent in urban environments within high-density urban cores. Shifts toward public transportation in response to bikesharing tended to be more prevalent in lower-density regions on the urban periphery. This early study of North American bikesharing indicates that public bikesharing may serve as a first-and-last-mile connector in smaller metropolitan regions with lower densities and less robust public transit networks. The findings also suggest that in larger metropolitan regions with higher densities and more robust public transit networks, public bikesharing may offer faster, cheaper, and more direct connections compared to short distance transit trips. In addition, public bikesharing may be more complementary to public transportation in small and medium metropolitan regions and more substitutive in larger metropolitan areas, perhaps providing relief to crowded public transit lines during peak periods.
Ridesourcing services (also known as transportation network companies or TNCs) provide prearranged and on-demand transportation services for compensation, which connect drivers of personal vehicles with passengers. Smartphone applications are used for booking, ratings (for both drivers and passengers), and electronic payment. Studies on the impacts of ridesourcing (also referred to as Transportation Network Companies or TNCs) are more limited, particularly the effects of these innovative services on core transportation modes (e.g., taxis, public transportation). The impacts of ridesourcing on vehicle trips, vehicle occupancy, VMT, GHG emissions, and other transportation modes have not been extensively studied. Emerging studies of ridesourcing suggest these services are either replacing a trip previously made with another mode or they are making an entirely new trip they otherwise would not have (i.e., induced demand). While one study concluded that ridesourcing substitutes more automobile trips than public transit trips, multiple other studies suggest that ridesourcing can compete with public transit and active modes (cycling and walking). The table below shows survey results regarding what mode of transportation respondents would have used had ridesourcing not been available.
Ridesourcing Modal Shift Impacts by Mode
|Rayle et al.
San Francisco, CA
Denver and Boulder, CO
|Clewlow and Mishra*
Seven U.S. Cities**
Two Phases (2014-2016)
|Bike or Walk||9%||12%||23%|
|Would not have made trip||8%||12%||22%|
|Other / Other ridesourcing||10%||7%||–|
*The impacts in this study were weighted by usage and aggregated across 7 cities.
**Cities in study include: Austin, Boston, Chicago, Los Angeles, San Francisco, Seattle and Washington, DC.
These studies found that ridesourcing is drawing users from private vehicles (7-39%), taxis (1-36%), active modes (9-23%) and public transportation (15-30%), although the impacts vary by region. Rayle et al. (2016) asked respondents if they still would have made the trip had ridesourcing services not been available and, if so, how they would have traveled. 92 percent replied they still would have made the trip, suggesting that ridesourcing has an eight percent induced travel effect.
A few studies have examined the VMT and trip-making impacts of ridesourcing services. Schaller 2017 found that ridesourcing services contributed to a 3.5% increase in citywide VMT and a 7% increase in VMT in Manhattan, western Queens, and western Brooklyn in 2016, however this study uses various sources as proxies to estimate modal shift from other transportation modes like public transit and driving. In Denver, Henao 2017 found that ridesourcing takes more vehicle trips to move fewer people. The study noted that it takes an average of 100 vehicle miles to transport a passenger 60.8 miles (or nearly 40% out of service miles). Finally, in San Francisco, SFCTA (2017) found that approximately 20% of total ridesourcing VMT are out-of-service miles. These studies suggest that there may be an increase in VMT in these cities due to ridesourcing services, although the exact magnitude is still unknown and likely varies based on local characteristics, such as density, land use, and the built environment.
Ridesharing facilitates formal or informal shared rides between drivers and passengers with similar origin-destination pairings. Vanpooling consists of 7 to 15 passengers who share the cost of the van and operating expenses and may split the responsibility of driving. Despite its long history (over 70 years in North America), ridesharing has relatively few quantitative analyses documenting its impacts. Thus, the magnitude of ridesharing’s costs and traveler benefits is unclear. It is frequently referred to as the invisible mode—as it is difficult to track and document. Ridesharing participants experience cost savings attributable to shared travel costs, travel-time savings through use of high occupancy vehicle (HOV) lanes, and potentially lower commute stress as a result of shared driving responsibilities. One study of casual carpooling in Houston, Texas found that casual carpoolers between the ages of 25 and 34 were more likely to make commute trips (96 percent) versus non-commute trips (80 percent), and they were more likely to be single or married without children. In contrast, HOV lane users tended to belong to larger households, where over 60 percent of carpools (also known as fampools) comprise family members. Another study of casual carpooling in the San Francisco Bay Area estimated a total reduction of 450,000 to 900,000 gallons of gasoline per year. The majority of this savings was attributable to ridesharing’s congestion reduction impact on traffic. A more recent study of Bay Area casual carpooling revealed that motivations for participation among 503 respondents included: convenience, time savings, and monetary savings, while environmental and community-based motivations ranked low. This study found that 75 percent of casual carpool users were previous public transit users, and over 10 percent formerly drove alone. Despite the uncertain magnitude of impacts, ridesharing participants experience cost savings due to shared travel costs, travel-time savings through the use of HOV lanes and possibly reduced commute stress due to shared driving responsibilities.
Policymakers and decisionmakers should be aware of the potential positive and negative impacts of shared mobility on local communities. Understanding the impacts of shared mobility can enable policymakers to leverage positive environmental outcomes, while simultaneously taming unintended or negative impacts. To understand the impacts of shared mobility on local and regional communities, local governments should consider requiring mobility operators to share data and report key impact metrics and develop and administer studies to measure the travel, social, and economic impacts of shared mobility on local communities.
This article was co-authored with Adam Cohen.
Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.