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21st Century Operations Using 21st Century Technologies

Analysis of Travel Choices and Scenarios for Sharing Rides
Final Report

Chapter 2. Sharing Rides in an Environment of On-Demand Ridehailing

Overall Context

Over the past decade, transportation network companies (TNC) like Uber™ and Lyft™ have replaced, supplemented, and disrupted traditional modes of transportation with their on-demand ridehailing services. In 2014, these TNCs introduced dynamic ridesharing products such as UberPool™ and Lyft Shared™ (formerly known as Lyft Line™).

While most TNCs primarily offer private ridehailing service in which a driver is paired with a single rider (or party of riders), many companies have also offered ridesharing services that pair multiple riders (or parties) on shared, overlapping trips with the same driver. While the terms "ridehailing" and "ridesharing" are often used interchangeably, this research holds that the terms have distinct meanings and this document uses them accordingly. Ridehailing refers to all TNC services. Ridesharing applies only to hailed rides where riders elect a product that may pair them with one or more other travelers en route. Ridesharing refers to all trips during which a user's product choice indicates a willingness to share, even if they are not paired with another party. This report refers to the balance of ridehailing trips for which users do not opt into a potential multi-party trip as private ridehailing.

Increasing ridesharing relative to private ridehailing is of interest to policymakers to promote more efficient use of the transportation network. It is critical to understand which riders are adopting these services and in what contexts. While transportation researchers have analyzed ridehailing behavior in general, the literature describing ridesharing trips and users or the effect of trip cost and travel time on a rider's choice between a shared or private TNC trip is much more limited. To strengthen the understanding of this specific segment, the research reported on for this study provides (1) a descriptive analysis of ridesharing trips and users in 15 American cities, and (2) a scenario-based analysis of the effect of differences in relative trip price and travel time between private and shared TNC trips on vehicle miles traveled (VMT) in an urban and metropolitan context. The survey data described in this chapter informs a broader study of shared ride scenarios, presented in chapter 3. As described more fully in the approach section, this research differs from other survey efforts, as this directly involved a major TNC which provided administrative detail and reached ridehailing service users within one to two days of a recent trip. Because of this survey targeting and additional data, detailed context and personalized questions were possible.

Ridehailing Literature

There is a growing body of literature on TNC use and users in American transportation markets. This literature can be divided into two categories: those that describe ridehailing users broadly and those that characterize ridesharing users more specifically.

Ridehailing Users

Most ridehailing literature does not distinguish between private and shared products. Typically, this product detail is not available in the data sources (usually surveys) utilized by researchers, either because the data were collected for another purpose unconcerned with this distinction or these newer products were not offered at the time of collection.

The 2017 National Household Travel Survey (NHTS)—the first to ask respondents about TNC use—is one important source used by scholars to characterize ridehailing users. Using this data, Schaller found that most TNC users are located in nine large, densely populated metropolitan areas and predominantly affluent, well-educated, and young.1 Conway, Salon, and King used the same data to identify correlations between higher ridehailing use and greater transit/ nonmotorized travel, higher residential density, and lower vehicle ownership.2 One important limitation of using the NHTS to study ridehailing use is that trip locations are sampled at the metropolitan level (i.e., core-based statistical area), so it is not possible to distinguish trips that occur in the urban core versus more suburban locations. Geographic coverage areas for Lyft and Uber are typically defined broadly and are treated as such in this study (e.g., the Chicago TNC market is not just the City of Chicago, but several surrounding counties as well). TNC trips are more prevalent in city centers than in outlying areas of a metropolitan region, meaning that decisions made at the city-level to encourage more shared trips will impact a larger proportion of vehicles in a city than is implied by the results reported here.

Beyond the 2017 NHTS, other studies have used online, in-vehicle, or on-street intercept surveys to characterize TNC users according to age, income, education, vehicle ownership, trip purpose, and gender. Henao found a correlation between ridehailing and certain trip types, namely social and airport trips (among TNC users who are also frequent drivers) and work and school trips (among TNC users who are not frequent drivers).3 Rayle et al. and Schaller found that non-car owners are overrepresented among TNC users. 4, 5

Studies in the U.S. show TNC users are younger, better educated, higher-income individuals than the average American. Clewlow and Mishra found younger, college-educated, affluent Americans have adopted ridehailing more quickly than older, less educated, lower income populations.6 Young and Farber's examination of household travel survey data characterized ridehailing as a "wealthy younger generation phenomenon."7 Kooti et al. described the average active TNC user as "an individual in his or her mid-20s with an above-average income."8 The authors found older riders use ridehailing services less frequently but take longer rides; higher-income riders take more rides and are more likely to use more expensive ridehailing products.

Studies differ in their assessment of whether ridehailing users are predominantly male or female. In a meta-analysis, Moody and Zhao provide a complete summary of travel survey studies focused on the sociodemographics of ridehailing patrons in the U.S. and Singapore.8 Their extensive literature review considers the impact of age, income, education, gender, and car ownership on the number of times a month individuals use ridehailing services. The authors confirm use is generally higher among younger, more educated, and more affluent individuals.

Beyond descriptive research, studies have estimated predictive models with similar parameters and implications. Dias et al. used household travel surveys from metropolitan Seattle to create a model estimating adoption and frequency of ridehailing and carsharing use.10 Alemi et al. surveyed over 2,000 ridehailing users in California about their use of TNCs to create a model of ridehailing use.11 The model developed by Dias et al. found higher vehicle ownership and greater residential density to be correlated with ridehailing/carpooling use, while Alemi et al. found that increased land use diversity, and centrality are associated with higher adoption of these services. Alemi et al. found that use of other newer transportation services (bikesharing, carsharing) and technology (online shopping, social media) were predictors of TNC use. Both papers found adoption was positively correlated with income, education, and employment status and negatively correlated with age and presence of children.

Ridehailing Versus Ridesharing

Despite the growing body of ridehailing literature relying on travel surveys and mode choice models, very few studies distinguish between types of ridehailing service. This report identifies four noteworthy exceptions.

Amirkiaee and Evangelopoulos considered users' motives in ridesharing (i.e., cost, time, anxiety, trust, and reciprocity), but did not report demographics of respondents.12 Additionally, their research analyzed respondents' intention to use ridesharing, but did not collect data on actual observed trips.

Sarriera et al. analyzed a survey of TNC users that asked about recent use of UberPool™ and Lyft Line™.13 They found that younger, unmarried, non-car-owning individuals were more likely to have used ridesharing options; income and gender did not have a significant effect on ridesharing opt-in; and the majority of ridesharing trips were for leisure, rather than commuting or airport access. Respondents' most common motivations for ridesharing were cost savings and speed and comfort compared to transit and walking. The top deterrents to sharing were lack of privacy, uncertainty about travel time, and fear of being paired with an unpleasant passenger.

Moody, Middleton, and Zhao found TNC users who are younger and employed are more likely to have used ridesharing (they analyzed the same survey data as Sarriera et al.).14 All other sociodemographic characteristics were not significantly predictive. For the subset of respondents who had used a ridesharing service, students, respondents with graduate degrees, and those who were unmarried reported the highest percentage of their ridehailing trips being shared.

Liu et al. created a mode choice model that incorporated ridehailing and ridesharing alongside traditional modes by collecting stated preference data in New York City.15 The model estimates the effects of travel time, trip cost, and other factors on mode choice, but does not incorporate other characteristics of the traveler or trip. This is the only mode choice survey analysis that included both shared and private TNC trips in the United States at the time of this writing.

Two recent studies have considered the role of pricing and travel time in the choice between private and shared TNC trips. First, Alonso-González et al. used a stated preference experiment to analyze individual willingness to share by comparing preferences towards individual and pooled rides in the Netherlands.16 Considering price, time, and privacy, the study found that up to 85 percent of users were willing to share rides with one to two extra passengers in cases without a time penalty and with a 3-euro price difference between private and shared rides. However, the study also identified a class of travelers—29 percent of their sample—that resist sharing, have a high value of time (VOT), and strongly prefer individual rides. Second, Hou et al. used the anonymized TNC trip, vehicle, and driver data from the City of Chicago to estimate customers' willingness to share.17 Controlling for factors like trip purpose, population density, distance, and travel time, the authors found that the price difference between private and shared trips had a relatively small effect on willingness to share; a 10 percent increase in the difference leads to 0.82 percent increase in sharing.

The goal of this research is to add to this limited literature on shared ridehailing choices in the United States by reporting on new surveys linked to actual TNC trips, both shared and private.

Survey Approach and Data Preparation

The TNC's survey of its users collected several types of data, including preferences for trip alternatives and data on observed trips (which the TNC appended to the survey results). The survey also asked respondents to provide information about their trip purpose, personal characteristics (e.g., annual income, age, gender), and travel behavior (e.g., car ownership, frequency of transit use) that were used in the discrete choice modeling and market segment analysis described later in this chapter.

Trip Alternative Questions

The data collection process combined data on observed trips and stated preference responses to trip choice questions. Survey respondents were divided into two branches: those who used private ridehailing for their most recent ride and those who used ridesharing. In each branch, respondents reviewed combinations of shared ride prices and travel times relative to a private ridehailing trip. For recent private trip takers, the comparison trip was their observed trip. For shared trip takers, the comparison trip approximated the private trip they were offered when they selected a shared ride, based on price differentials and travel times. Prices of hypothetical trips ranged from 15 to 150 percent of the observed trip and travel times ranged from 70 percent to 155 percent of the observed trip. Ridesharing times were presented as a range, reflecting how shared trips are displayed in the smartphone app.

The survey presented respondents with combinations of price, time, and mode (private or shared), referred to collectively as a "card." Each question asked users to select one card from a trio of cards, as shown in table 2 and table 3. For private TNC users, each set of options compared the observed trip cost and time to various levels of price differentials and time penalties for hypothetical shared trips. The price differentials were intended to represent a typical shared discount (35 percent of the observed private price) and two higher discounts (50 percent and 75 percent).18 The various time penalties also include a typical time penalty alongside higher and lower options.

For shared TNC users, each set of options compared the approximated private trip cost and travel time to various levels of cost and time that were either more or less attractive than the observed shared ride. The comparison levels were set to represent the typical case (a private TNC trip at 150 percent the price of a shared ride with travel time 70 percent as long), and the objective of the survey was to understand at what level of service these known users would stop choosing the shared product. Table 2 shows examples of how the cards were presented as choice questions. Private TNC users were asked 13 questions and shared ride users 16 questions.

Table 3 displays the price and time levels presented for shared and private TNC users. For shared TNCs, alternative times were presented as a range as is typical within the app. Card 1 served as the comparison private trip for most choice sets. Time penalty caps were enforced to ensure the shared TNC times presented were realistic.

Table 2. Alternative options examples. (Source: Federal Highway Administration)
Variable Example 1: Private Transportation Network Company (TNC) Users Example 2: Shared TNC Users
Option 1 (Observed) Option 2 Option 3 Option 1 (Observed) Option 2 Option 3
Price $25 $16 $13 $16 $22 $24
Time 30 min 35-39 min 44-47 min 39 min 32-34 min 27 min
Mode Private Shared TNC Shared TNC Shared TNC Shared TNC Private
Table 3. Characteristics of cards presented to private and shared transportation network company users in trip alternative analysis. (Source: Federal Highway Administration)
Private Transportation Network Company (TNC) Users Shared TNC Users
Card Price Relative to Observed Time Relative to Observed Mode Card Price Relative to Observed Time Relative to Observed Mode
1 100% 100% Private 1 150% 70% Private
2 65% 100% Shared 2 100% 100% Shared
3 50% 100% Shared 3 100% 81-91% Shared
4 15% 100% Shared 4 100% 91-102% Shared
5 65% 115-130% Shared 5 120% 100% Shared
6 50% 115-130% Shared 6 120% 81-91% Shared
7 15% 115-130% Shared 7 120% 91-102% Shared
8 65% 125-150% Shared 8 135% 100% Shared
9 50% 125-150% Shared 9 135% 81-91% Shared
10 15% 125-150% Shared 10 135% 91-102% Shared
11 65% 145-165% Shared 11 120% 70% Private
12 50% 145-165% Shared   No Value No Value No Value
13 15% 145-165% Shared   No Value No Value No Value

Appended Data on Observed Trips

In addition to the TNC users' anonymized survey responses, the TNC provided the research team with the following data on respondent behavior for each survey response:

  • The frequency which the respondent opted into the shared ride option between July and November 2018 relative to all users in the market.
  • The frequency which the respondent used the TNC's services between July and November 2018 relative to all users in the market.
  • The length of the user's most recent trip relative to all trips in the market.
  • The share of a user's TNC trips for which the user opts in to shared rides, classified according to three levels: frequent (greater than 50 percent of trips), sometimes (10 to 50 percent), and rarely (less than 10 percent).
  • The price charged for the user's most recent trip.
  • The travel time of the user's most recent trip.

The first three items were provided as the quantile in which the data fell for the distribution of that measure in each of the 15 markets. The TNC partner appended the following variables from the Environmental Protection Agency's (EPA's) Smart Location Database to describe the built environment surrounding the pick-up and drop-off locations of each survey response:

  • Gross residential density (housing units/acre) on unprotected land.
  • Gross retail and entertainment employment density (jobs/acre) on unprotected land.
  • Gross office and industrial employment density (jobs/acre) on unprotected land.

The TNC did not attach the exact value for these variables to each survey response, but rather categorized quantiles for each variable: less than 50 percent, 50 to 80 percent, 80 to 90 percent, 90 to 95 percent, and greater than 95 percent. These quantiles refer to the relative density of locations within a given market, rather than relative density at a national level.

Sample Cleaning

The complete dataset included 5,373 unique survey responses. To ensure validity and applicability of the data collected for the research questions, the research team, in conducting its analysis, removed invalid or inapplicable responses and weighted the remaining responses to the surveyed population. The following filters were used to limit records used for further analysis:

  • Trip distance and price must be greater than 0 (to eliminate trips that were not completed).
  • The group size reported by the trip's driver must be 1 or 2 passengers (shared options are available only to groups of 1 or 2 passengers, so data about larger traveler parties were not applicable to this study).
  • The respondent must have taken at least one trip with the TNC in the respective survey market between July and November 2018 or must have indicated that he/she was a visitor (otherwise no data were available to weight based on frequency of opting in to sharing and the data were unusable).

This filter resulted in the removal of 625 responses, for a sample size of 4,748. In addition to the filters above, responses were tested for logical consistency in preferences between the ridehailing options presented. The checks identified users who chose an option that was strictly inferior to another in terms of both travel time (i.e., slower) and cost (i.e., more expensive). To ensure users completed the survey with attention and thoughtfulness, responses failing two or more checks were removed.

Screening responses with these filters resulted in removing an additional 383 responses (for a total of 1,008) and produced a final dataset of 4,365 responses. While this process resulted in the removal of a high number of screened responses (approximately 18.8 percent), this level is similar to other online surveys of TNC users.19

Data Weighting

To produce generalizable findings, survey samples must match the characteristics of the population surveyed. For this reason, the research team applied data weighting techniques to the survey sample to make it more representative of the population of TNC users across the markets studied. The team weighted responses in a two-step process: within TNC markets and between TNC markets.

Within each of the 15 markets studied, responses were weighted to match five characteristics: users' sharing opt-in rate, users' number of TNC trips taken, trip distance, trip day of the week, and trip time of day. This approach required the appended quantile information described above as well as data on the share of trips within each market by day of the week and time of day and appended day-of-week and time-of-day information for the survey anchor trip. The team applied an iterative proportional fitting model to the survey data distributions to calculate weights that matched distributions of these five characteristics in the survey responses to the population distributions of each market as closely as possible. The team trimmed weights to prevent the outsized influence of specific responses. After weighting within each market, the team used data from the TNC on the number of active users within each market to weight responses between markets.

Weighted data on respondents and their trips was then used to conduct the market segmentation analysis described in the following section. Before turning to that analysis and results, a final data preparation step for the choice analysis warrants description.

Discrete Choice Model Development

In the discrete choice analysis, the unit of analysis is not the observed trip, but each choice of a private or shared TNC ride presented in the trip alternative portion of the survey. Because each respondent answered many triplet questions and each question represented two preferences (the selected card over two unselected cards), the 4,365 valid responses produced 110,320 choice observations.

Because of the structure of the choice question triplets, each observation represented one of three choice types: private over shared, shared over private, or one shared option over another. The last choice type was discarded from analysis (although later revisited) because it did not represent the choice between sharing and not sharing, but rather preferences for time and cost in shared TNC rides (additionally, some shared options were strictly preferable to others in terms of both time and cost). The result was 79,667 usable observations. These choice observations were associated with the full data for each respondent and weighted appropriately.

The research team used the dataset of 79,667 choice observations to build several discrete choice models. In each model, the dependent variable is the binary shared–private product choice. The mode used for the observed trip on which the choice survey was based was not used as a dependent or predictor variable in these models.

As a first step in the discrete choice analysis, the research team created a series of univariate models that estimated correlations between trip and user characteristics (i.e., predictor variables) and the choice of mode (private or shared) for each observation. These univariate models considered the following predictor variables:

  • Shared TNC cost savings per trip
  • Shared TNC travel time penalties per trip
  • Shared TNC cost savings per mile
  • Shared TNC travel time penalties per mile
  • Level of shared TNC cost savings (see table 1)
  • Level of shared TNC travel time penalty (see table 1)
  • Trip distance
  • Group size
  • Availability of express option
  • Day of week
  • Time of day
  • Trip purpose
  • Origin type
  • Destination type
  • Market (i.e., city)
  • Visitor status
  • Whether trip was paid for by an employer
  • Land use characteristics of origin
  • Land use characteristics of destination
  • Annual income
  • Age
  • Gender
  • Car ownership
  • Transit use
  • Bicycle use
  • Household size and composition

Trip distance, cost savings, and travel time penalties were treated as continuous variables. Cost savings and travel time penalties (per trip and per mile) are particularly important predictor variables in this project. These variables were calculated as the cost and time difference between the preferred and the rejected travel alternative (in all cases, one alternative was a private TNC trip and the other a shared TNC trip). Because travel times were presented as a range for shared TNC trips, the travel time penalties were calculated based on the midpoint of the range presented. Although the uncertainty implied in the range may have affected each respondent's decision, the design of the survey did not allow for consideration of this uncertainty, so midpoints were used for the analysis instead. It is possible, though, that users focused on the longest time presented for each trip option, in which case using that time instead of the midpoint would make sense, but we lack evidence of this and thus chose to use the midpoint values.

The team treated all other predictor variables as discrete variables or multiple binary variables in the various univariate logit models. Annual income, for example, was classified according to five levels (e.g., less than $50,000, $50,000 to $75,000, etc.). Household composition, on the other hand, was classified as a set of binary examples (e.g., one child, no children, many children).

Because the dependent variable in each of these models is a binary outcome variable (whether a user selected the shared option for the hypothetical choice), the team used logistic regression for the discrete choice models of the logit form. Logit models estimate the log-odds of an outcome as a linear combination of predictor variables that can have positive or negative effects on the probability of sharing. The logit model is specified by a set of coefficients and the ability to calculate standard errors and associated p-values for each predictor variable. These values can indicate whether the variable has a significant effect on the dependent variable. The coefficients describe the change in the log-odds outcome for a one unit increase in the predictor variable. Using exponentiated coefficients, it is possible to observe the impact of each of the above predictor variables on the probability of sharing.

The research team tested various combinations of predictor variables in a series of multivariate models. Initial models were constructed based on predictor variables shown to have significant effects in the univariate models and refined in order to identify the combinations of variables that best predict choice, measured using the Akaike information criterion, a measure of logit model fit. The team explored nine logit models in depth as listed below:

  • Model 1: Shared TNC cost savings and travel time penalties.
  • Model 2: Shared TNC cost savings per mile and travel time penalties per mile.
  • Model 3: Shared TNC cost savings and travel time penalties and market/city indicators.
  • Model 4: Shared TNC cost savings and travel time penalties and market/city characteristics (e.g., population density, employment density).
  • Model 5: All categorical variables found significant in univariate discrete choice models.
  • Model 6: All categorical variables found significant in model 5.
  • Model 7: Shared TNC cost savings and travel time penalties and all variables in model 5.
  • Model 8: Shared TNC cost savings per mile and time penalties per mile and all variables in model 5.
  • Model 9: Shared TNC percent of observed cost and time (categories from table 3) and all variables in model 5.

Furthermore, the research team divided the data into subsets in various ways to calculate these effects according to population segments of interest from a policy perspective. For each segment, the models above were estimated for the specific population of interest.

These segments include:

  • Annual income (less than $50,000, greater than $50,000).
  • Relative office and industrial employment density for the market at the origin and destination (less than 90th percentile of the metropolitan area, greater than 90th percentile).
  • Regional centrality index by transit, relative to automobile centrality at the origin and destination (less than 90th percentile of the metropolitan area, greater than 90th percentile).20
  • Trips to and from airports or other intermodal hubs.

Calculating coefficients for each of these segments allowed the research team to observe how population-level characteristics affect willingness to use a shared TNC and to identify the market segments where an intervention (for example, via a targeted incentive) would have the greatest effect.

Passenger Occupancy in Private Rides

While this chapter describes differences between private (i.e., ridehailing) and shared (i.e., ridesharing) rides, it is worth noting that private rides often have a passenger occupancy of greater than 1. Some ridehailing vehicles, for example, have the capacity for up to 6 passengers in one private party. Shared ride parties, meanwhile, are restricted to either a single passenger (most common) or a single passenger and 1 companion (which typically requires payment of a small additional fee that reduces the price difference between private and shared rides).

The survey data gathered by the TNC allowed for analysis of passenger occupancy in private rides. Doing so requires a slightly different dataset than the one used in the rest of the analysis described in this chapter. Specifically, the data were re-filtered to include only private trips and to include party sizes greater than two riders (which had been previously omitted to account for the fact that such parties are not eligible for ridesharing). This re-filtering produced a new dataset with 3,518 observations.

In the survey, respondents described their party size as "On my own," "1 other person," or "2 or more other people." Table 4 summarizes party size according to these three levels. Because the level "2 or more other people" includes parties of 3 or more, it is not possible to calculate average occupancy precisely. However, knowing that all users traveling with "2 or more other people" represents party sizes of at least three, then the average occupancy of a private ride (not including the driver of course) is at least 1.462. If, as is more likely, it is assumed that parties greater than 2 have an average occupancy of 3.1, then the average occupancy increases to 1.475.

Table 4. Share of adjusted survey data at three occupancy levels for private ridehailing trips (n = 3,518). (Source: Federal Highway Administration)
Party Size Share of Observed Trips (Weighted)
"On my own" 64.2%
"1 other person" 21.5%
"2 or more other people" 13.0%

This estimate is compatible with findings from other research. Henao and Marshall found, for example, an average occupancy of 1.37 using data collected from 416 ridehailing rides (private and shared).21 While this estimate is somewhat lower than the occupancy presented above, Henao and Marshall include shared rides in their survey, which counterintuitively lowers the average occupancy per party-trip by restricting party size to 2.

This average occupancy makes even private ridehailing rides more favorable from an occupancy perspective than personal vehicle trips, but it is also worth noting that these estimates do not account for the miles that a driver spends without a passenger between rides (i.e., deadheading miles). Balding et al. estimate the share of TNC VMT without a passenger to be 42 percent. Using this estimate, factoring the miles in which the passenger occupancy of a TNC vehicle is zero into occupancy results, leads to an effective occupancy for private ridehailing trips of approximately 0.855 (assuming that parties greater than 2 have an average occupancy of 3.1).22 For comparison, Henao and Marshall used an estimate of 40.8 percent deadheading miles, which reduced their average vehicle occupancy from approximately 1.4 to approximately 0.8.23

Limitations of Approach

Using our broad sample of 4,365 ridehailing users living in areas where on-demand ridehailing is available, this research shows (1) which market segments in our weighted sample opted in to shared and private modes more or less frequently, and (2) how differences in price and travel time may affect ridesharing behavior. Before presenting these findings in greater detail, several limitations to the methodological approach warrant attention.

First, the data presented here are only a snapshot in time of a TNC user base that is growing and changing rapidly, with a 37 percent increase from 2016 to 2017 in passengers transported.24 As this user base evolves and TNCs alter their services, this analysis will need to be updated to reflect the point-in-time reality of travel behavior. Longitudinal/panel research would support an understanding of how sharing behavior changes over time.

Second, regarding the scenario evaluation and the supporting discrete choice analysis, results are limited by trip alternative questions asked in the TNC's survey. That is, this study does not have data to evaluate the effects of price and time differences that exceed those presented in table 3 (i.e., maximum shared/private TNC price differential of 75 percent). It is not possible, for example, to analyze the effects of free shared TNC trips on the rate at which people choose to use shared TNCs. Also, because cards were generated based on set levels of discount relative to the observed trip, data are not granular enough to truly calculate non-linear effects that might exist even within the range tested.

Third, the survey results do not address interactions across all modes. For that reason, this study can estimate how price and time affect a user's choice between a private and shared TNC ride, but it cannot estimate how price and time affect a user's choice between TNCs, transit, driving, carpooling, walking, bicycling, or any other mode. This research provides previously unavailable detail on shared versus private TNC rides but does not analyze how changes to shared or private rides might affect, for example, transit use. Similarly, the study has no data to consider how TNC characteristics affect a user's decision to take a trip in the first place. Naturally, TNC users face many travel decisions beyond whether to share a TNC ride. Nonetheless, the results presented below focus narrowly on this one decision by assuming, for the sake of analysis, that travelers make decisions between personal cars, TNCs, transit, and active transportation before making decisions about shared versus private for-hire vehicles. If price and time differentials change between private and shared ridehailing in such a way that average ridehailing trip costs and travel times do not change significantly, it is likely that the overall TNC mode share will not change significantly either.

Finally, although this anchored stated preference approach provides unique insights into choices to share, caution is still recommended when relying on reported preferences (even though "anchored") rather than observational or experimental data. There is no guarantee that respondents' reported choices would have matched their real-world choices had each of the options been available; the TNC's survey did not fully replicate customers' in-app decision-making process. Furthermore, the survey relied on respondents' memories of the observed trip and some customers may have forgotten any number of factors that influenced their actual decision, such as weather, peer pressure, or the urgency of their trip, despite the trip having taken place only within the previous 24 to 48 hours.

With these limitations in mind, the results presented in the following two sections seek to shed light on how travelers chose to take shared or private TNC trips.

Analysis of Market Segments

The weighted survey data supported the division of respondents into market segments with different propensities to choose ridesharing products. Dividing up market segments allows identification of segments with greater opportunity for mode shift. The research team sought to (1) identify, using personal characteristics, meaningful segments of people who share or show a willingness to share at different frequencies across their total trips, and (2) identify, using trip and built environment characteristics, meaningful segments of trips that are shared or where there is a willingness to share at different frequencies.

The research team selected the following segments for their policy relevance and observed impact on behavior:

  • Personal characteristics:
    • Age: under 25 years old, 25 to 45 years old, over 45 years old.
    • Annual income: less than $50,000; $50,000 to $99,999; $100,000 to $149,999; greater than $150,000.
    • Weekly transit use: 0 days, 1 to 2 days, 3 or more days.
    • Household car ownership: 0 cars, 1 car, 2 or more cars.
    • Gender: male, female, or prefer not to respond.
  • Trip characteristics:
    • Whether the rider or the rider's employer paid for the observed trip.
    • Size of the traveling party: 1 or 2.
    • Day of the week: weekdays (Monday through Friday) and weekends (Saturday and Sunday).
    • Time of trip start: morning (6:00 to 9:59 a.m.), midday (10:00 a.m. to 2:59 p.m.), late afternoon and evening (3:00 to 7:59 p.m.), and night (8:00 p.m. to 5:59 a.m.).
    • Trip distance: short (less than 5 miles), medium (5 to 15 miles), or long distance (greater than 15 miles).
    • Origin: home, work, personal business, entertainment, or airport/train/bus.
    • Destination: same categories as origin.
  • Built environment characteristics for the beginning and end of each trip (city specific):
    • Relative residential density.
    • Relative retail/entertainment density.
    • Relative office/industrial employment density.

Regarding the origin and destination sub-bullets under "Trip characteristics" above, data were derived from questions asking respondents to describe their origin and destination according to one of the following types: "Your home or current residence"; "A workplace, worksite, professional meeting, or school"; "An entertainment, recreation, or social venue"; "Another location for personal business"; or "An airport, inter-city bus terminal, or train station."

The research team divided respondent data into the respective segments. For each segment, the team summed the weights for respondents at each opt-in level (for personal characteristics) or for private/shared trips (for trip characteristics and built environment characteristics). These sums were divided by the total of respondents in each category. The resulting shares are plotted as histograms in the following sections. The research team used the Pearson's Chi-squared test for the absolute counts of the segmented populations to test whether the distributions of each population (users who frequently, sometimes, or rarely share; shared and private trips) were statistically different. All segments listed above were significant at the 95 percent confidence level except for trip distance.

Frequency of Sharing by User Characteristic

The administrative data for this research contains three levels of long-term sharing behavior among users: rarely (less than 10 percent of trips), sometimes (10 to 50 percent of trips), or frequent (greater than 50 percent of trips). These frequency levels are based on all trips that a user took through the ridehailing app between July and November 2018. Those who rarely share account for 30 percent of the weighted data; those who sometimes share, 45 percent; and those who frequently share, 25 percent. The research addresses five dimensions through which statistically significant and interesting differences in sharing can be identified: gender, vehicle ownership, transit use, annual income, and age.

Figure 1 summarizes the respondents' frequency of opting in to ridesharing according to these five user characteristics. Females, representing 58.5 percent of the weighted survey responses, represent about 66 percent of users that choose shared products more than 50 percent of the time, making them far more likely to be "frequent" or "sometimes" sharers than the men in the sample.

Frequent sharers are most likely to be from zero-car households than from households with one vehicle or multiple vehicles. Respondents who share less than 10 percent of the time are most likely to have two or more vehicles. Riders who share more often also tend to use transit more. Over half of riders who frequently share also use transit more than three times a week. For riders that rarely share, over 50 percent of them responded that they use transit zero times per week.

A significantly larger proportion of frequently-sharing respondents comes from households with under $50,000 of annual income. Sometimes- and rarely-sharing respondents are much more likely to have annual incomes over $100,000 per year. Users with annual income between $50,000 and $100,000 are more equally divided among those who rarely share, those who sometimes share, and those who frequently share.

Riders who share frequently in the sample tend to be younger. The majority of all users, including from all three sharing groups, are 25 to 45 years old. The proportion of frequent sharers under 25 is nearly twice the proportion of rarely sharing users under 25.

Distribution of Shared and Private Trips by Trip Characteristic

Just as respondents' characteristics affect respondents' willingness to share across trips, details of their surveyed trip were expected to influence their choice for the specific trip surveyed. Shared trips accounted for 28 percent of the weighted sample, while private trips accounted for 72 percent. While many trip characteristics were found to be statistically significant, trip distance was not found to have a significant effect. Figure 2 summarizes the distribution of observed trips according to the product used: shared or private.

The weighted sample demonstrated a greater proportion of shared trips in several market segments. Examples include solo trips, weekend trips, trips from work, trips home, and trips to entertainment or personal business.

Conversely, the study observed a greater proportion of private trips in several market segments. Examples include trips by parties of 2, weekday trips, morning trips, short distance trips (less than 5 miles), trips to work, and trips to or from intermodal travel nodes (i.e., airports, train stations, and bus terminals). The study also observed less sharing for trips paid for by an employer and found that respondents taking employer-paid trips are much less price sensitive than those who pay for their own travel.

Distribution of Shared and Private Trips by Built Environment Characteristic

Three built environment characteristics measure different forms of density: residential, office/industrial employment, and retail/entertainment employment. As noted previously, these density measures were calculated individually for the 15 markets, so that the 10th percentile zone in New York is much denser than the 10th percentile zone in Nashville or Portland. These density measures were studied for both trip origins and destinations to understand if there were specific markets, such as trips to work, for which sharing was more common. Of the six distributions, four are statistically significant; end-of-trip residential density and beginning-of-trip retail/entertainment density are not. Like figure 2, figure 3 summarizes the distribution of observed trips according to the product used and the built environment of trip origins and destinations.

The densest areas (90th to 100th percentile of the metropolitan area) account for disproportionately large shares of trip origins and destinations across all three measures, for shared and private trips. The skewing is especially high toward employment with around 45 percent of all trips coming from the densest 10 percent of retail/entertainment and office/industrial zones. About 30 percent of trips end or start in the densest 10 percent of zones by residential population. These findings support the conclusion that TNC use is greatest in the densest parts of metropolitan areas, but many trips connect dense employment areas to less dense residential areas (likely outside urban cores).

In the weighted sample, a greater proportion of shared trips is observed for trips starting or ending in the least dense office/industrial and retail/entertainment employment areas (0 to 50th percentile). This finding may be correlated to trip length; longer trips are more likely to be shared and trips traveling through less dense areas are likely to be longer. It could also be related to the types of employment and travelers in these areas if they are lower-income jobs or less expensive retail and entertainment establishments. These interactions could be studied further in future work.

In the weighted sample, a greater proportion of private trips started in the least dense residential areas (0 to 50th percentile) and the densest residential areas (95th to 100th percentile) than in the intermediate density categories. These differences are statistically much greater than random, showing that there are multiple factors of sharing propensity at work for residential travel, and probably other area types as well. Differences in built environment characteristics capture a variety of factors, such as supply and demand factors related to trip density that could affect the efficiency and pricing of sharing as well as interactions with the type of travelers and purposes associated with traveling to these areas. These area density quantiles provide initial insights into how place type may affect sharing and opportunities to increase sharing, but additional work would be necessary to understand in greater detail what really leads to higher use of sharing and to untangle the different factors at play.

Figure 1 is a compilation of five bar graphs.

Figure 1. Graph. Distribution of observed frequency of sharing according to user characteristic. Bars of the same frequency level sum to 100 in each panel (n = 4,365).
(Source: Federal Highway Administration)

Figure 1 is a compilation of five bar graphs. Each bar graph shows the distribution of the observed frequency of sharing according to a user characteristic. Each bar graph includes a set of characteristics, such as 'male' and 'female' under 'gender.' For each characteristic, three bars are presented for three frequencies of opting in to sharing: rarely, sometimes, and frequently. Under 'gender,' 'rarely share' is the tallest bar for men, followed by 'sometimes share' and 'frequently share.' The opposite ordering applies to women. Under 'household cars owned,' users with zero cars are most likely to be in the 'frequently share' group, while users with 2 or more cars are most likely to be in the 'rarely share' group. Users with 1 car are evenly split among the three groups. Under 'weekly transit use in days,' users who never use transit are most likely to fall in the 'rarely share', while users who use transit 3 or more days per week are most likely to fall in the 'frequently share' group. Users who are use transit once or twice per week are evenly split among the three sharing levels. Under 'annual household income', users with incomes under $50,000 per year are most likely to be frequent sharers, while users with incomes over $100,000 are most likely to 'rarely share.' Between $50,000 and $100,000 in income, users are mostly like to fall in the 'sometimes share' group. Under 'age,' users under 25 years old are most likely to be frequent sharers, while those over 45 are most likely to fall under 'rarely share.'
Figure 2 is a compilation of seven bar graphs.

Figure 2. Graph. Distribution of shared and private trips according to trip characteristics (n = 4,365).
(Source: Federal Highway Administration)

Figure 2 is a compilation of seven bar graphs. Each bar graph shows the distribution of the selected mode as a percentage of riders by category of trip characteristics. The two mode options in this context are shared and private rides. Each bar graph includes a set of trip characteristics, such as 'weekend' and 'weekday' under 'day of week.' For each characteristic, two bars are presented the two modes. Under 'day of week,' there are more private trips than shared trips in the dataset on weekdays. The opposite is true for weekends. Under 'who paid for the trip,' nearly all employer-paid trips are private, while self-paid trips are more likely to fall to be shared. Under trip distance, shared trips are overrepresented in the long-distance category, while private trips are overexpressed in the short-distance category. Under 'travelling party,' shared trips are overrepresented in the category 'party of 1', while private trips are overrepresented in the category 'party of 2.'' Under 'time of day', shared trips are overrepresented at night, while private trips are overrepresented in the morning, while midday and evening trips are more evenly divided. Under 'origin,' the trends are les obvious, although private trips seem to be overrepresented among personal business and airport trips. Finally, under 'destination,' shared trips are overrepresented among trips that start at home, while private trips are overrepresented among those that start at work or at the airport.
Figure 3 is a compilation of six bar graphs.

Figure 3. Graph. Distribution of shared and private trips according to the built environment characteristics. Categories on the x-axis represent percentiles (n = 4,365).
(Source: Federal Highway Administration)

Figure 3 is a compilation of six bar graphs. Each bar graph shows the distribution of the selected mode as a percentage of riders by category of built environment characteristics at the trip origin and destination. The two mode options in this context are shared and private rides. Each bar graph includes five percentiles: 50th, 80th, 90th, 95th, and 100th. For each percentile, two bars are presented the two modes. Under 'residential density at the origin,' there are more private trips than shared trips among those trips starting at the very densest parts of a city. The same is under 'retail density at the origin', while the opposite is true under 'office density at the origin.' As for trip destinations, private trips are overrepresented among the densest destinations in terms of retail and office jobs, while the trends for residential density at the destination are less clear.

Analysis of Price and Travel Time Effects on Choice to Share

The discrete choice models developed as described in the Survey Approach and Data Preparation section enabled the research team to evaluate how shared product use would change in scenarios where respondents experienced different price and travel time options for shared trips relative to private trips. The scenarios studied were changes in (1) the dollar per mile relative cost differences between shared and private TNC trips, and (2) the minute per mile travel time relative differences between shared and private TNC trips.

To evaluate the effect of these relative differences (by population segment), the research team extracted coefficients from the discrete choice models to estimate the impacts of a change in price or travel time on the probability of sharing. Exponentiating the coefficients results in an estimate of the effect of an additional dollar of cost or minute of travel time on the probability that a user selects a shared TNC product over a private TNC product for a specific trip. The analysis used coefficients from model 8 for cost and travel time savings per mile. When analyzing market segments, coefficients corresponding to the market segment were removed from the model because the segment variables had uniform values.

The discrete choice modeling approach can be used to explore changes in mode choice that might occur as a result of changes in the relative travel time and price of TNCs. These changes are not intended to represent specific policy mechanisms since there might be any number of ways for relative cost and travel time scenarios to come about, especially considering local context factors for travel. As such, these results do not test the impact of specific policies, but rather model the mode choice implications that could result from potential policy outcomes, particularly relative price difference increases between private-party and shared TNC trips.

Effect of Price on Sharing

The effect of price can be understood in two manners based on survey data and based on administrative data, both provided by the TNC. The first is by describing the responses riders reported or were observed to choices presented directly (i.e., descriptive analysis), and the second is by using the discrete choice models to ask how TNC users would respond to options generalized from the choice sets presented if they were made available.

Relying on descriptive analysis, figure 4 shows the share of private TNC users that switched from private to shared TNC trips at each of the three levels of price differences offered in the study. These three alternatives represent shared trips with identical travel times to the observed private trip. The figure shows that holding travel time "constant" (i.e., shared and private alternatives have the same estimated travel time), higher discounts for shared rides correspond to greater shares of the population willing to use sharing, indicating some amount of price sensitivity. This relationship presents a roughly linear pattern; increasing the price differential from 35 percent to 50 percent (an average $2.24 additional discount) increases the user's willingness to share by 7.5 percent, while increasing the price differential from 50 percent to 75 percent (an average $3.44 additional discount) increases the user's willing to share by 11.0 percent. The increase in sharing per dollar price differential between these tiers is quite similar: 3.3 and 3.2 percentage points per dollar. Figure 4 also shows that over 30 percent of users rejected a shared trip that cost 75 percent less than the observed private trip, even when the presented travel time is identical, reflecting the fact that unwillingness to share is not only related to price and time.

Figure 4 presents this summary for all TNC users (solid orange) and for TNC users with reported annual income under $50,000 (dotted orange) and over $100,000 (striped orange). These segments were chosen in order to represent a more and less price-sensitive group, respectively, of the TNC user population. As expected, a greater share of lower-income users chose the shared option at each of the three price differentials. The opposite is true for higher-income users.

Figure 4 is a bar graph demonstrating the share of private transportation network company users that switched.

Figure 4. Graph. Share of private transportation network company users that switched to shared travel. Users in dataset that switched from private to shared travel at three levels of price differences offered (with average price differential shown in parentheses). These three alternatives represented shared trips with identical travel times to the observed private trip.
(Source: Federal Highway Administration)

Figure 4 is a bar graph demonstrating the share of private transportation network company users that switched from private to shared travel at three levels of discount. The results are divided into three income levels: high-income, low-income, and all users. For high-income users, 41.9% switch from private to shared at a 35% discount (or an average discount of $5.24). 49.3% switch at a 50% (or an average discount of $7.48). 58.6% switch at a 75% discount (or an average discount of $10.92). For low-income users, 55.8% switch at a 35% discount, 60.8% switch at a 50% discount, and 73.4% switch at a 75% discount (representing the tallest bar in the graph). Consider all respondents in the sample, 48.5% switched at a 35% discount, 56.0% switched at a 50% discount, and 67.0% switched at a 75% discount.

Market Segmentations and Price Sensitivity

As noted in the Survey Approach and Data Preparation section, the research team broke down the data into various subsets for its analysis to calculate price and time effects according to population segments of interest from a policy perspective. Segments are relevant to policy discussions if it is possible to design a policy that would affect only that segment through a realistic mechanism such as geographic cordons or an income verification procedure. These segments included annual income (less than $50,000 and greater than $50,000), relative office and industrial employment density for the market at the origin and destination, regional centrality index by transit at the origin and destination, and trips to and from airports or other intermodal hubs.

Table 5 presents exponentiated coefficients of model 8 for the market segments analyzed below (as well as control variables representing market segments not analyzed in depth). Table 6 presents the initial sharing rates for these same segments. The dependent variable in this model is the probability that a respondent opted into the shared ride option (regardless of whether the resulting ride was actually shared). All variables presented in Table 5 were significant with p-values less than 0.05, except where noted. These coefficients differ slightly in the segmented models as those models are derived from different (segmented) populations with different characteristics. Coefficients greater than 1 indicate that a unit change in that variable (most are binary, indicating a true or false case) would increase the probability of sharing. Coefficients less than 1 indicate the opposite. The coefficients in Table 5 indicate, for example, that the model predicts that users with annual incomes under $50,000 are 49.7 percent more likely to select a shared ride, "all else equal" (and the initial sharing rate confirms that lower-income users did indeed select a shared ride more frequently in the observed data). However, table 6 shows that the initial sharing rate for users with annual incomes under $50,000 is not exactly 1.497 multiplied by 29.9 percent because "all else" is not equal; the users in this category also have different trip purposes, origins, destinations, trip lengths, and other trip characteristics that distinguish them from the average user. Like riders with annual income under $50,000, the model also found that riders whose origin was a relatively dense office district were more likely to share. The opposite was true for trips ending in dense office districts and trips starting in competitive transit districts.

Table 5. Exponentiated coefficients of Model 8. (Source: Federal Highway Administration)
Variable Type Variable Coefficient
Market Segment
(see figure 5 and figure 7)
Annual Income: Under $50,000 1.497
Annual Income: Over $100,000 0.667
Dense Office District (Begin Only) 1.111
Dense Office District (End Only) 0.953
Competitive Transit (Begin Only) 0.859
Competitive Transit (End Only) Not Significant
To/From Airport 0.949
Price and Time Shared Cost Savings ($/mile) 1.086
Shared Time Penalty (min/mile) 0.666
Control Variables Age: Under 25 years old 1.470
Age: Over 65 years old 0.759
Transit Use: 1 or more days/week 1.316
Household Car Ownership: Owns car 1.031
Gender: Male 0.924
Visitor 1.138
Employer Paid for Trip 0.464
Size of Traveling Party: 1 1.208
Trip Start Time: Morning 0.812
Trip Start Time: Evening 0.934
Trip Distance (miles) 1.009
Home-based Work 1.211
Home-based Social 1.071

Table 5 shows that Model 8 also presents price and time differences between shared and private rides as a linear explanatory variable for sharing. As noted in the descriptive analysis in the previous section, increasing price differentials for shared rides correlates to a roughly linear increase in users' probability of sharing. Applying the market segmentations from table 5 to the discrete choice model, figure 5 presents the coefficient of this price difference for various population segments. The segments presented are not exclusive of one another, so that a trip could be made by a rider with annual income below $50,000, starting in a dense office district and ending in a transit competitive area. The two triplets of location segments are exclusive within their characteristics (as shown by use of three bars of the same color).

Figure 5 is a bar graph with ten bars.

Figure 5. Graph. Effect of $1 per mile relative price difference on a user's percent probability of sharing rides.
(Source: Federal Highway Administration)

Figure 5 is a bar graph with ten bars. Each bar represents the effect of a $1 per mile relative price difference on a user's percent probability of sharing rides. For all users, this value is 8.6%. For users with income under $50,000 per year, this value is 15.3%. For users with income over $100,000 per year, this value is 3.6%. For trips that start in dense office districts, this value is 22.4%. For trips that end in dense office districts, this value is 8.9%. For trips that start and end in dense office districts, this value is 1.6%. For trips that start in areas with competitive transit, this value is 4.7%. For trips that end in areas with competitive transit, this value is 20.1%. For trips that begin and end in areas with competitive transit, this value is 1.2%. For trips to and from the airport, this value is 2.9%.

The values in figure 5 can be interpreted as the effect of an incremental increase in the price differential between shared and private TNC trips on an individual's probability of opting into the sharing option.  Figure 5 presents this effect on a per-mile basis to normalize price and time considerations by trip length. For example, a $4/trip discount on a $6, 1-mile ride would likely have a much greater influence than a $4/trip discount on a $25, 10-mile ride.

Table 6. Initial rate of opting in to shared rides for selected market segments. (Source: Federal Highway Administration)
Market Segment Initial Sharing Rate
Annual income Under $50,000 39.0%
Annual income Over $100,000 24.4%
Dense Office District (Begin Only) 30.4%
Dense Office District (End Only) 25.7%
Competitive Transit (Begin Only) 30.8%
Competitive Transit (End Only) 28.7%
To/From Airport 23.5%
All Trips 29.9%

The research finds that the overall effect of a $1/mile per trip greater price difference is an 8.6 percentage point increase in the probability of sharing. This indicates that, for all users, an additional $1/mile price difference increase the probability of sharing from 29.9 percent (see initial sharing rates in table 6) to 38.5 percent. The effect of increasing price differences is even greater for the following segments:

  • Riders with annual income under $50,000.
  • Trips that begin in dense office districts.25
  • Trips that end in areas with competitive transit.26

This finding is related to the coefficients observed in table 5, in which these three segments are correlated with higher probabilities of sharing. This finding suggests that for each of these segments, riders are more price sensitive and therefore choose lower cost options more often when they are made available. Both past research and economic theory are consistent with lower- income travelers exhibiting higher price sensitivity.

One explanation for differential responses to price differences at different locations is that time sensitivity differs for various trip purposes. For instance, trips to employment districts are likely more time-sensitive because employees need to arrive at the office at a fixed time, but leaving offices, workers enjoy a more flexible schedule and are less concerned about travel time. Considering trips that start in dense office districts, there is considerable overlap with trips originating at work; more than half of all trips with a work origin also begin in the densest office districts. This overlap explains why riders originating in dense office districts appear less price sensitive (i.e., more willing to accept a discount to share). The effect of each additional $1/mile price difference is similar in trips starting at work (a 19.8 percentage point increase in the probability of sharing) and trips starting in dense office districts (a 22.4 percentage point increase; see figure 5).

The higher sharing propensity for trips ending in areas with competitive transit is harder to explain, but the greater effect of price for these trips may be related to statistically significant correlations with weekly transit use (i.e., this group includes riders who use transit more frequently each week), trip length (i.e., these trips are longer), or time of day (i.e., this group includes more late-night weekend trips). Each of those segments also demonstrates greater willingness to respond to a given price for shared rides, indicating that trips ending in areas with competitive transit are taken by riders who are less time sensitive, more price sensitive, and/or more open to sharing.

Conversely, the effect of greater price differences is much less than average for these other segments:

  • Riders with annual income over $100,000.
  • Trips to or from the airport or other intermodal travel centers.
  • Trips with both their beginning and ending points in areas with competitive transit.
  • Trips with both their beginning and ending points in dense office districts.

For trips to long-distance terminals, time sensitivity is very high as riders need to catch a plane or train. Because of this, riders are less likely to choose lower cost and slower or less time-certain shared rides. Additionally, airport/train station trips are highly correlated with employer reimbursement, lowering price sensitivity (employers paid for 10.2 percent of non-airport trips versus 34.5 percent of airport trips).

For the location-based price insensitive segments, these are very short trips on average. Trips that start and end in transit competitive areas, for example, are 3.2 miles long on average, compared to 7.4 miles for trips that don't fall into this category. Trips that begin and end in dense office districts are 5.4 miles long on average, compared to 7.0 miles for trips that don't fall into this category. The effect of each additional $1/mile price difference is much higher for longer trips. For example, for trips over 5 miles, each $1/mile price difference results in a 16.5 percentage point increase in the probability of sharing, compared to 8.6 percentage points for all trips.

Because a large share of these trips stays within the region's core (the most transit-competitive portion of most cities) or other business districts, even steep per-mile discounts are not very meaningful in convincing respondents to choose a shared trip. Furthermore, the private trip cost might already be low enough that price sensitivity does not play a major role. On short trips, the schedule risk of sharing could be perceived to be a higher share of total travel time, despite the survey offering the same percentage-based time penalties.

Effect of Time on Sharing

As with price, the effect of time can be analyzed in two manners: by describing the responses riders reported or that were observed to choices presented directly and by using the discrete choice models to ask how TNC users would respond to a different set of choices if they were made available.

The descriptive analysis is shown in figure 6, which reports the share of private TNC users in the dataset that chose a shared option at each of the four levels of travel time differences and price differences offered. The rightmost column matches the values from figure 4, while the other columns add new information. Within each price differential level, lowering travel time penalties has a similar effect on increasing willingness to share. Additionally, the willingness to share increases with increasing discount, as also shown in figure 4.

Over 30 percent of users rejected a shared trip with no time penalty that is 75 percent less expensive than the observed private trip. Once even the lowest tested amount of travel time uncertainty is introduced, this number rises to more than 50 percent of respondents being unwilling to share at a 75 percent discount. This value is even higher at lower levels of discount.

The values in figure 6 may be even lower than expected at lower levels of discount and higher travel time differences, because this analysis includes only users who used a private TNC product for the survey anchor trip. Compared with the population of all TNC users, these users share less in general; 33.1 percent of shared TNC anchor trip users fell within the top quintile for opting in to shared TNC trips in their city, with just 21.3 percent of observed private TNC anchor trip users falling into that group of frequent sharers.

Figure 6

Figure 6. Graph. Share of private transportation network company users in dataset that switched from private to shared travel at each level of travel time difference and price difference offered.
(Source: Federal Highway Administration)

Figure 6 consists of 11 cells indicating the share of private transportation network company users in the dataset that switched from private to shared travel at various combinations of travel time difference and price difference offered.
Figure 7 is a bar graph with ten bars.

Figure 7. Graph. Effect of 1 minute/mile reduction in relative travel time difference on the percent probability of sharing.
(Source: Federal Highway Administration)

Figure 7 is a bar graph with ten bars. Each bar represents the effect of a 1 minute per mile travel time difference on a user's percent probability of sharing rides. For all users, this value is 33.3%. For users with income under $50,000 per year, this value is 20.5%. For users with income over $100,000 per year, this value is 43.7%. For trips that start in dense office districts, this value is 33.9%. For trips that end in dense office districts, this value is 44.6%. For trips that start and end in dense office districts, this value is 36.5%. For trips that start in areas with competitive transit, this value is 41.3%. For trips that end in areas with competitive transit, this value is 41.9%. For trips that begin and end in areas with competitive transit, this value is 35.6%. For trips to and from the airport, this value is 41.7%.

Using the linear results of the discrete choice model, figure 7 presents the effect of an incremental decrease in the travel time differential between shared and private TNC trips on an individual's probability of opting in to the sharing option for various population segments. As with price, this effect is presented on a per-mile basis to normalize for differences in trip length.

According to figure 7, the overall effect of reducing the travel time penalty for shared rides by 1 minute per mile is a 33.25 percentage point increase in probability of sharing. That is, travel time savings of 1 minute per mile increase the probability of sharing for all users from 29.9 percent to 63.2 percent (see initial sharing rates in Table 6). Although it is difficult to compare the relative effects of time and money, it is obvious the effect of a 1-minute savings per mile greatly exceeds that of a one dollar per mile savings for all segments (discussed in greater detail in the following section on Relative Effect of Price and Time).

In a dense urban environment, a minute-per-mile travel time improvement may be difficult to achieve. For instance, if average speeds were 15 miles per hour (including stoplights, etc.), each mile would take 4 minutes to travel. Improving this speed by a minute-per-mile would raise average speeds 33 percent to 20 miles per hour. In a less urban context, average speeds of 30 miles per hour would need to be increased to 60 miles per hour to achieve the 1-minute-per-mile improvement—an impossibility under almost any plausible scenario. As such, travel time differentials would be more likely to come from changes in waiting time or matching time for shared versus private rides.

Although the overall effect of a 1 minute-per-mile decrease in the shared ride travel time penalty is quite large, the effect of decreasing travel time penalties is even greater for the following segments:

  • Trips by riders with annual incomes over $100,000.
  • Trips to, from, or within dense office districts.
  • Trips to, from, or within areas with competitive transit.
  • Trips to or from the airport or other intermodal travel centers.

Of the segments chosen for reporting in figure 5 and Figure 7, only the lower-income segment exhibits lower effects of reducing time differentials between shared and private products than the average response. The lower effect shows that this segment is less time sensitive than the average survey respondent, although they would still be 20 percentage points more likely to share if the time difference was reduced 1 minute per mile.

The other segments place a higher than average value on travel time. If the travel time difference between shared and private trips was reduced by 1 minute per mile, these findings suggest these groups would be willing to change products at a rate even higher than by 33 percentage points.

Relative Effect of Price and Time

The tables and figures presented point to the finding that, in general, riders appear to place a very high value on their travel time (and presumably on travel time reliability although it was not tested directly through the choice questions). This finding is not surprising in light of the survey responses summarized in table 7, which showed that the risk of delay is the most common deterrent to using the shared TNC option. Nearly half of all private TNC users surveyed indicated that they chose a private ride because of the risk that a shared ride would take longer, while only a quarter of private TNC users indicated that they were motivated by price. Other literature has also shown time to be a more powerful motivator than price in sharing rides; a recent study of Waze™ users by Cohen et al. found that saving commute time was more effective than compensation in encouraging affinity to carpool.27

A simple example helps illustrate the relative effect of price and time changes, as shown in table 8. According to figure 5, a $1/mile per trip price difference corresponds to an 8.6 percentage point increase in the probability of sharing. A price difference of $1.16 per mile would increase the probability of sharing for general trips by 10 percentage points (from roughly 30 percent of trips to roughly 40 percent).28 With an average price per mile of $3.30, this additional price difference represents a 35.1 percent reduction in the price of shared rides.

Table 7. Reported reasons why respondents chose a private ride over a shared ride (values do not add to 100 percent because respondents could select more than one reason). (Source: Federal Highway Administration)
Reasons I chose a private ride over a shared ride Percent
There was a chance that it was going to take a lot longer and that uncertainty is too risky 49.5%
The shared option was too much slower than the private option 29.2%
The discount was not big enough 24.6%
I prefer not to share my trip with a stranger 21.7%
I didn't see the shared option in the app 6.5%
I don't understand what the shared option is 0.0%

Considering travel time, figure 7 shows that the overall effect of reducing the travel time penalty for shared rides 1 minute per mile is a 33.25 percentage point increase in probability of sharing. A travel time difference of 0.30 minutes (18 seconds) per mile would also increase the probability of sharing for general trips by 10 percentage points (again, from roughly 30 percent of trips to 40 percent). With an average trip speed of 23.8 mph (or 151 seconds per mile), this represents an 11.9 percent reduction in relative travel time for shared rides.

Table 8. Illustration of effect of price and time differences on overall level of sharing. (Source: Federal Highway Administration)
Factor Price Time
Unit of change Dollar/mile Minute/mile
Unit effect on sharing 8.6% 33.3%
Initial level of sharing 30% 30%
Desired level of sharing +10% (to 40%) +10% (to 40%)
Required change to increase sharing $1.16/mile 18 s/mile
Initial price and travel speed $3.30/mile 151 s/mile (23.8 mph)
Percent change to price and travel speed 35.10% 11.90%

According to this example, the findings of the discrete choice model imply a relative effectiveness of changes in travel time and price differences. Specifically, comparing the change necessary to increase sharing by 10 percentage points is either a $1.16 per mile price differential or 18 seconds per mile time differential increase. Comparing these values results in a ratio of $3.86 per minute, which is equivalent to $231.97 per hour. This number is not equivalent to a value-of-time measure, but rather a simple comparison of the changes in price and travel time necessary to produce equivalent changes in sharing.

This measure has several limitations, particularly the complicating factor of the lived experience of sharing a ride with a stranger possibly detracting from user satisfaction, separate from the time penalty. By comparing the effect of price differential and travel time penalties, this ratio represents the amount that respondents were willing to pay to reach their destinations more quickly (and privately). Because all price and time differentials were presented relative to the private trip as part of a shared product option, the desire for privacy (which might also have a per-mile value) is not separable from the desire for faster arrival. Because shared choices were presented with fixed uncertainty bands at the different travel time penalty levels, the effect of uncertainty and delay can also not be separated in the TNC's choice survey. These limitations are discussed more in the earlier subsection, Limitations of Approach.

To place this value ($231.97) in context, it is also useful to consider an otherwise unused element of the dataset collected: the choice of one shared ride option over other shared ride options, particularly in the case where riders preferred a shared ride with a longer travel time and a lower price to a shared ride with a faster travel time and a higher price. This data holds mode constant, as all choices are between different shared options. Thus, it is possible to compare the savings a user accepted (i.e., the rejected higher cost minus the preferred lower cost, or $3.26 on average) by the delay that they also accepted (i.e., the preferred longer travel time minus the rejected slower travel time, or 3.16 minutes on average), or vice versa. The resulting ratio represents a ceiling on the user's willingness to pay to avoid additional travel time in a shared ride because a user with a higher willingness to pay would have chosen the faster, higher cost ride. A user with a lower willingness to pay would still have selected the cheaper, longer ride. In the choice data, this ratio amounts to a ceiling of $83.37 per hour—the average minimum discount needed for users to switch to a shared ride. Like the value $231.97, this estimate also has its limitations because no discounts smaller than 35 percent were tested in the study, meaning that users may have a lower willingness to pay than the study observed. It is also possible to analyze this data by considering each of the "pairwise" choices between different shared ride options presented to survey respondents.29 Considering the average trip cost and average travel time, it is possible to calculate the implied value of time for each of these pairwise choices. Depending on which of the two options the respondent selected, this value of time is either a floor or a ceiling (i.e., one choice may offer the user the chance to pay $14 per hour to save time; if a user rejects that offer, then the user's value of time is below that threshold and vice versa).

Considering value-of-time ceilings, table 9 and table 10 show the percentage of respondents that accepted the lower-cost, longer-travel option in each pairwise choice (for respondents whose last trips were private and shared, respectively). According to Table 9, a small share of respondents (18.9 percent) have low values of time (below a ceiling of $14.24), and a large share have ceilings below $139.19 (70.1 percent). Implied values of time are generally lower in Table 10, with more than half of respondents implying a value of time under $10.62 and 91.8 percent implying a value of time under $57.82. These findings are consistent with the expectation that customers whose last TNC trip was private would on average have a higher VOT.

Because this choice data controls for mode (i.e., private or shared TNC), it enables further exploration of consumer preferences among shared options. Exploration of this data could help guide service offerings and encourage more customers to make a shared ride choice (e.g., offering service standard guarantees that limit delay as a higher-priced shared-ride product option, where those paying more for their shared rides are promised a more direct trip).

However, this dataset carries three major limitations. First, different alternatives were presented to users whose last trip was private versus shared, so the results are not precisely comparable between these two types of respondents. Second, over multiple survey questions, users were presented shared options reflecting different time and price tradeoffs, and user preferences were not always consistent. That is, sometimes respondents accepted a time delay implying a lower value of time than rejected when presented a different choice set. Third, survey questions were typically presented as a triplet that included one private trip option and two shared trip options. As a result, the stated preference of one shared trip option over another only applies to cases where a respondent chose one of the shared options (over both one other shared option and one private option). Respondents who chose a private trip in every triplet of questions, for example, would not be represented in this data at all, which would skew downward the average implied value of time.

Table 9. Implied value of time based on choice between 11 pairs of shared ride options. (Source: Federal Highway Administration)
Pair Discount (Accepted) Discount (Rejected) Travel Time Penalty (Accepted) Travel Time Penalty (Rejected) Average Value of Time (VOT) Ceiling (Implied) Percent of Responses
1 50.0% 35% 55.0% 0.0% $14.24 18.9%
2 75% 50% 55.0% 0.0% $23.73 22.9%
3 50% 35% 22.5% 0.0% $34.80 36.6%
4 75% 50% 55.0% 22.5% $40.15 40.0%
5 50% 35% 55.0% 37.5% $44.74 55.7%
6 50% 35% 37.5% 22.5% $52.20 53.2%
7 75% 50% 22.5% 0.0% $58.00 47.2%
8 75% 50% 55.0% 37.5% $74.57 59.1%
9 75% 50% 37.5% 22.5% $86.99 59.3%
10 75% 35% 22.5% 0.0% $92.79 58.6%
11 75% 35% 37.5% 22.5% $139.19 70.1%
Table 10. Implied value of time based on choice between nine pairs of shared ride options. (Source: Federal Highway Administration)
Pair Cost Mark-Up (Accepted) Cost Mark-up (Rejected) Time Penalty (Accepted) Time Penalty (Rejected) Average Value of Time (VOT) Ceiling (Implied) Percent of Responses
1 20% 35% 35% 0.0% $10.62 55.7%
2 0% 20% 35% 0.0% $14.16 57.3%
3 20% 35% 20% 0.0% $18.58 92.3%
4 20% 35% 35% 20.0% $24.78 73.2%
5 0% 20% 20% 0.0% $24.78 69.0%
6 0% 35% 35% 0.0% $24.78 80.7%
7 0% 20% 35% 20.0% $33.04 80.0%
8 0% 35% 20% 0.0% $43.36 87.7%
9 0% 35% 35% 20.0% $57.82 91.8%

Discussion: Ridesharing and Transportation Network Companies

TNC trips continue to grow as a share of U.S. travel, with other emerging mobility paradigms suggesting more on-demand ridehailing in the future. The occupancy of on-demand vehicles will thus come to have an increasing impact on our streets. A shared-ride future could alleviate congestion, reduce VMT, lead to better air quality, and improve travel times for all road users.30 A future with primarily private ridehailing, by contrast, may result in far more VMT growth than one with a larger role for sharing. For that reason, it is critical to understand who is currently choosing shared TNC services and where demographic and trip characteristics may encourage greater sharing.

Using this broad sample of 4,365 ridehailing users living in areas where dynamic ridesharing is available, the analysis in this chapter demonstrates which market segments in the weighted sample selected shared and private modes more or less frequently. Multiple studies have found TNC users in general are younger, better educated, higher income, urban, and non-car owning. This chapter also finds that many of these characteristics are associated with higher rates of sharing among TNC users. For example, younger individuals are more likely to use ridehailing and, among ridehailing users, are also more likely to share.31 The opposite is true for annual income; higher-income individuals use ridehailing more, but among ridehailing users, those with lower incomes are more likely to share. The TNC's survey results confirm the conclusions of narrower ridehailing studies that younger riders who do not own cars appear to share with greater frequency. However, where Sarriera et al. found no relationship between income or gender and sharing, the TNC survey finds more sharing among lower-income riders and women.32

This research goes beyond existing literature in determining the sharing behavior of other population segments and trip types. Notably, the TNC survey finds more sharing among passengers who use transit more frequently and for weekend trips, trips from work, trips to home, and "going out" trips (i.e., trips to entertainment or for personal business). Conversely the TNC survey finds less sharing among two-person rider parties and for employer-paid trips, morning trips, shorter trips, trips to work, and trips connecting to long-distance modes.

These findings are compatible with other research findings and common expectations on travel behavior and preferences. Higher-income travelers, for example, have a corresponding higher value of time, and thus would be expected to prefer a private mode with faster travel times, even if it is more expensive. Riders traveling to the airport or to work in the morning are also less price sensitive, likely because their arrival time is important. On the other side of the issue, the data demonstrate that riders who use transit more frequently also share more frequently, possibly at least partially because they are both more price sensitive and more comfortable sharing public space with strangers. Riders without access to a vehicle—a trait correlated with transit use—also share more frequently, likely because they have lower income or because they are frequent TNC users in general and are looking to save money.

Beyond descriptive analysis of sharing rates by type of trip and user, the results presented in this chapter explore the impact of changing the relative modal characteristics of private and shared TNC trips, particularly price and travel time. This study finds that an increase in the relative price difference of $1 per mile increases an individual's probability of sharing by over 8 percentage points. A decrease in the relative travel time of 1 minute per mile has a much larger effect. On the scale of an entire metropolitan area, such a shift could have a noticeable impact on travel behavior and VMT. With an average trip length of 5.6 miles, an additional discount of over $5 per trip or a travel time savings of over 5 minutes represents a significant change in TNC operations and thus may seem hard to come by.

One valuable aspect of these findings is that they also provide planners and policymakers with the ability to identify which types of TNC trips and TNC users are likely to be more influenced by price- and time-based changes in ridesharing characteristics. While the market segmentation results show what trip types are already likely to be shared (e.g., social trips and weekend trips), the TNC scenario results shows what trip types could beconverted to shared trips with the least change in modal characteristics. For example, while riders to airports and other intermodal centers are relatively price insensitive, riders traveling from these centers may be more influenced by price-based incentives, even though these trips are currently more likely to be private rides, creating a unique opportunity for incentivizing sharing. (In the case of airports, though, the combination of passengers arriving in private TNCs and returning in shared TNCs might just lead to many TNCs—except the shared ones—returning empty after airport drop-offs.)

The TNC's survey results also show that a sizeable portion of private TNC trips (approximately 35 percent, as shown in figure 6) will be difficult or even impossible to convert to shared rides through a price-based incentive. That is, for some trips, even a 75 percent discount—the greatest offered in the survey—is not enough to convince some riders to switch from a private to a shared ride, even when they are told the travel time would match the private option. This study's cross-tabulations show that these riders are more likely to be older, higher-income, and infrequent transit users. Price incentives also appear to be less effective for shorter trips, likely because such trips are already low-cost relative to longer trips and the overall levels of discount in dollar terms are small even if the per mile rates are not. For these price-insensitive trips and users, travel time-based policies, such as enforced delays for private TNC pick-ups or dedicated travel lanes for shared TNCs, are likely to be more effective in encouraging sharing.

This research offers important opportunities and challenges to planners and policymakers seeking to increase the occupancy of vehicles on the road and thereby optimize the use of the existing transportation network. To that end, chapter 4 uses the results from this chapter to estimate the effect of a wide range of scenarios on the use of shared rides.

Through the use of smartphone applications, stated preference surveys anchored off of real trips can likely provide more accurate results than typical stated preference surveys because they incorporate the genuine context of trips. Taking advantage of that strength, the discrete choice modeling approach used in this study explored changes in mode choice that might occur as a result of changes in the relative price of TNCs. However, despite the strength of the stated preference methodology, we note several limitations to this work and suggest possible avenues for future research.

First, our data are only a snapshot in time of a TNC user base that was growing rapidly during the study period, with a 37 percent increase from 2016 to 2017 in passengers transported. As this user base changes and TNCs alter their services, our descriptive analysis will need to be updated to reflect the point-in-time reality of travel behavior.

Further longitudinal/panel research would support an understanding of how sharing behavior changes over time. Second, the study does not attempt to explain why TNC users do or do not share in response to price, time or any number of other factors. We do not assert any causal relationships between market segments and observed sharing behavior. Although it is beyond the scope of this study to draw conclusions as to whether these characteristics explicitly cause sharing, this research points the way for further experimentation with price- and time-based ridesharing incentives in controlled environments.

1 Schaller, B. (2018). The New Automobility: Lyft, Uber, and the Future of American Cities. [ Return to Note 1 ]

2 Conway, M.W., Salon, D., and King, D.A. (2018). "Trends in Taxi Use and the Advent of Ridehailing, 1995–2017: Evidence from the U.S. National Household Travel Survey." [ Return to Note 2 ]

3 Henao, A. (2017). Impacts of Ridesourcing–Lyft and Uber–on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior. [ Return to Note 3 ]

4 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." [ Return to Note 4 ]

5 Schaller, B. (2018). [ Return to Note 5 ]

6 Clewlow, R.R. and Mishra, G.S. (2017). Disruptive Transportation: The Adoption, Utilization and Impacts of Ride-Hailing in the United States. [ Return to Note 6 ]

7 Young and Farber (2019). [ Return to Note 7 ]

8 Kooti, F., Grbovic, M., Aiello, L.M., Djuric, N., Radosavljevic, V., and Lerman, K. (2017, April). "Analyzing Uber's Ride-sharing Economy." [ Return to Note 8 ]

9 Moody, J. and Zhao, J. (2019). "Adoption of Private and Shared Ridehailing in Singapore and the U.S." [ Return to Note 9 ]

10 Dias, F.F., Lavieri, P.S., Garikapati, V.M., Astroza, S., Pendayala, R.M., and Bhat, C.R. (2017). "A Behavioral Choice Model of the Use of Car-sharing and Ride-sourcing Services." [ Return to Note 10 ]

11 Alemi, F., Circella, G., Handy, S., and Mokhtarian, P. (2017). "What Influences Travelers to Use Uber? Exploring the Factors Affecting the Adoption of On-Demand Ride Services." [ Return to Note 11 ]

12 Amirkiaee, S.Y. and Evangelopoulos, N. (2018). "Why Do People Rideshare? An Experimental Study." [ Return to Note 12 ]

13 Sarriera, J.M., Álvarez, G.E., Blynn, K., Alesbury, A., Scully, T., and Zhao, J. (2017). "To Share or Not to Share: Investigating the Social Aspects of Dynamic Ridesharing." [ Return to Note 13 ]

14 Moody, J., Middleton, S., and Zhao, J. (2019). "Rider-to-Rider Discriminatory Attitudes and Ridesharing Behavior." [ Return to Note 14 ]

15 Liu, Y., Bansal, P., Daziano, R., and Samaranayke, S. (2018). "A Framework to Integrate Mode Choice in the Design of Mobility-on-Demand Systems." [ Return to Note 15 ]

16 Alonso-González, M., Cats, O., Van Oort, N., Hoogendoorn-Lanser, S., and Hoogendoorn, S. (2019). "Willingness to Share Rides in On-demand Services for Different Market Segments." [ Return to Note 16 ]

17 Hou, Y., Garikapati, V., Weigl, D., Henao, A., Moniot, M., and Sperling, J. (2020). "Factors Influencing Willingness to Share in Ride-Hailing Trips." [ Return to Note 17 ]

18 Hou et al. found that median shared rides in Chicago cost approximately 66 percent the cost of a private ride. [ Return to Note 18 ]

19 Moody, J., Middleton, S., and Zhao, J. (2019). "Rider-to-Rider Discriminatory Attitudes and Ridesharing Behavior." [ Return to Note 19 ]

20 This value is the ratio of the EPA's Smart Location Database's Regional Transit Centrality Index (D5dei) to the Regional Automobile Centrality Index (D5cei). Both D5dei and D5cei represent a Census Block Group's modal destination score relative to the maximum score for the core-based statistical area. [ Return to Note 20 ]

21 Henao, A. and Marshall, W.E. (2018). "The Impact of Ride-Hailing on Vehicle Miles Traveled." [ Return to Note 21 ]

22 Balding, M., Whinery, T., Leshner, E., and Womeldorff, E. (2019). "Estimated TNC Share of VMT in Six US Metropolitan Regions (Revision 1)." [ Return to Note 22 ]

23 Henao, A. and Marshall, W.E. (2018). [ Return to Note 23 ]

24 Young and Farber (2019). [ Return to Note 24 ]

25 Defined as relative office and industrial employment density for the market at the origin and/or destination (greater than 90th percentile of the metropolitan area). [ Return to Note 25 ]

26 Defined according to the EPA's Smart Location Database's regional centrality index by transit, relative to automobile centrality at the origin and destination (less than 90th percentile of the metropolitan area). The EPA defines regional centrality as proportional accessibility to regional destinations by auto and transit, respectively. Kevin Ramsey and Alexander Bell, EPA Smart Location Database Version 2.0 User Guide. March 14, 2014. https://www.epa.gov/sites/production/files/2014-03/documents/sld_userguide.pdf. [ Return to Note 26 ]

27 Cohen, M., Fiszer, M., Ratzon, A., and Sasson, R. (2019). Incentivizing Commuters to Carpool: A Large Field Experiment with Waze. [ Return to Note 27 ]

28 The actual portion of observed trips that were shared was 29.9 percent, but 30 percent is used here for the simplicity of illustrating the concept. [ Return to Note 28 ]

29 Respondents whose last trip was private were presented with 11 different choices. Respondents whose last trip was shared were presented with nine different choices. [ Return to Note 29 ]

30 Schaller, B. (2018). [ Return to Note 30 ]

31 Hou, Y. et al (2020). [ Return to Note 31 ]

32 Sarriera, J.M. et al (2017). [ Return to Note 32 ]