Introduction
Literature review
Intra-respondent preference heterogeneity
Variety-seeking
Urban air mobility
Survey and data
UberAIR service context
Questionnaire and respondent sampling
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Home zip code match qualifying zip code for the targeted location (Dallas-Fort Worth or Los Angeles MSAs);
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Having used at least one of the following transportation modes and services within the last month - Personal or household vehicle; Rent vehicle; Car-share service; Bus; Light rail, metro, or subway; Commuter rail; Taxicab; Ride-sourcing;
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Having completed at least one ground trip that took place in, around, or through the Dallas-Fort Worth/Los Angeles area;
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The trip was between 7–75 miles (one-way);
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The trip took at least 30 min in total (one-way);
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The trip purpose was one of the following purposes - Work commute; Other work-related business; Go to/from school; Go to/from airport; Shopping; Social or recreational; Entertainment event; Other personal business.
Count | Percentage (out of 2419 respondents) | |
---|---|---|
Trip purpose | ||
Work commute | 310 | 12.8 |
Other work-related business | 307 | 12.7 |
Go to/from school | 274 | 11.3 |
Go to/from airport | 315 | 13.0 |
Shopping | 308 | 12.7 |
Social or recreational | 306 | 12.6 |
Entertainment event | 294 | 12.2 |
Other personal business | 305 | 12.6 |
Trip mode | ||
Personal/household vehicle | 1540 | 63.7 |
Transit | 142 | 5.9 |
UberX | 542 | 22.4 |
UberPOOL | 195 | 8.1 |
Trip experience and socio-demographic characteristics
Reference mode | Personal/ household vehicle | Transit | UberX | UberPOOL |
---|---|---|---|---|
Total respondents # | 1540 | 142 | 542 | 195 |
Respondents # who experienced delay | 1006 (65%) | NA | 304 (56%) | 134 (69%) |
Average total delay time (min) | 15 | NA | 11 | 17 |
Average Google-calculated trip distance (mile) | 25.5 | 18 | 22.7 | 14.6 |
Average Google-calculated trip time (min) | 33 | 27 | 32 | 26 |
Average total trip duration (min) | 60 | 86 | 45 | 51 |
Socio-demo characteristics | Level | Amount | Percentage (out of 2419 respondents) |
---|---|---|---|
Residence | Dallas | 1101 | 45.5 |
LA | 1318 | 54.5 | |
Gender | Female | 1616 | 66.8 |
Male | 777 | 32.1 | |
Prefer not to say | 26 | 1.1 | |
Age | 18–24 | 308 | 12.7 |
25–29 | 351 | 14.5 | |
30–34 | 338 | 14.0 | |
35–39 | 287 | 11.9 | |
40–44 | 243 | 10.0 | |
45–49 | 195 | 8.1 | |
50–54 | 184 | 7.6 | |
55–59 | 168 | 6.9 | |
60–64 | 140 | 5.8 | |
65–69 | 108 | 4.5 | |
70 or older | 97 | 4.0 | |
Household vehicle | None | 151 | 6.2 |
1 Vehicle | 809 | 33.4 | |
2 Vehicles | 962 | 39.8 | |
3 Vehicles | 331 | 13.7 | |
4 Vehicles | 114 | 4.7 | |
5 or more vehicles | 52 | 2.1 | |
Household annual income | <$35,000 | 479 | 19.8 |
$35,000–$49,999 | 335 | 13.8 | |
$50,000–$74,999 | 416 | 17.2 | |
$75,000–$99,999 | 368 | 15.2 | |
$100,000–$149,999 | 341 | 14.1 | |
$150,000–$199,999 | 153 | 6.3 | |
$200,000–$249,999 | 75 | 3.1 | |
$250,000–$499,999 | 62 | 2.6 | |
>$500,000 | 38 | 1.6 | |
Prefer not to say | 152 | 6.3 |
Stated choice survey
Alternatives | |||||
---|---|---|---|---|---|
Private vehicle | Transit | UberX | UberPOOL | UberAIR | |
Attributes (median, mean) | |||||
Travel cost ($) | – | (3, 8) | (35, 40) | (28, 32) | (70, 88) |
Travel time (min) | (58, 70) | (87, 99) | (51, 62) | (55, 68) | – |
Flight time (min) | – | – | – | – | (12, 15) |
Access time (min) | – | – | – | – | (7, 9) |
Egress time (min) | – | – | – | – | (7, 9) |
Attitudinal statements
# | Attitudinal statements | Underlying constructs |
---|---|---|
1 | I am comfortable with flying in a small aircraft | Comfort of flying |
2 | Traffic congestion is a major problem in my area | Dissatisfaction for status-quo |
3 | I wouldn’t mind pooling with other people on eVTOL flights | – |
4 | Uber is my preferred rideshare service | ✗ |
5 | I would use an autonomous vehicle if it is available | – |
6 | I am comfortable with flying in a battery-powered aircraft | Comfort of flying |
7 | My current travel options for long-distance trips (50–100 miles) take too long | Dissatisfaction for status-quo |
8 | I am one of the first to adopt new technology | Variety-seeking |
9 | I usually take the cheapest mode of transportation available to me | ✗ |
10 | I’m excited for eVTOL travel to become available in my area | Variety-seeking |
11 | I wish travel times were more consistent and predictable in my area | Dissatisfaction for status-quo |
12 | I am concerned about my impact on the environment | ✗ |
Score | Alternation | Novelty-seeking | ||
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Ride-sourcing companies used in real life (mean) | Different alternatives chosen across SC tasks (mean) | Times UberAIR chosen in SC tasks (mean) | Times reference mode chosen in SC tasks (mean) | |
Statement #8 | ||||
1 | 0.6 | 1.6 | 0.9 | 7.5 |
2 | 0.8 | 1.8 | 1.3 | 6.1 |
3 | 1.0 | 2.0 | 1.7 | 5.0 |
4 | 1.3 | 2.2 | 2.8 | 3.8 |
5 | 1.5 | 2.3 | 3.7 | 1.9 |
Statement #10 | ||||
1 | 0.6 | 1.4 | 0.7 | 7.3 |
2 | 0.7 | 1.6 | 0.6 | 7.2 |
3 | 0.9 | 1.9 | 1.2 | 5.6 |
4 | 1.1 | 2.2 | 2.6 | 4.3 |
5 | 1.5 | 2.3 | 3.8 | 2.2 |
Methodology
Hypothesis
Basic Latent Class (LC) model
New model 1: Two-layer Latent Class (2L-LC) model
inter-respondent layer
Intra-respondent layer
New model 2: Two-layer latent variable latent class (2L-LV-LC) model
Structural equations for latent variable
Latent variables in class allocation functions
Latent variables in measurement equations
Log-likelihood function
Estimation and results
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Firstly, we calculated the value of travel time (VTT, $/min) for each time component. The VTT estimates were computed both over the sample and within each class. As to model 2 and model 3, only ASCs vary at the task level, whereas all the sensitivity parameters are kept constant across choice tasks given class membership. Thus, VTT results are the same for an “alternation-seekers” subclass and an “alternation-avoider” subclass if they are grouped under the same class s at the inter-respondent layer. It needs to be noted that as a non-linear specification of travel cost is adopted in each model, VTT depends on the travel cost. Herein, we used the price of the chosen alternative in calculating VTT estimates.
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Secondly, we computed the market share for each alternative by averaging the choice probabilities for each alternative across all the tasks using the model estimates. These market shares were calculated within each class for the basic latent class model (i.e. model 1). Regarding model 2 and model 3, we can obtain four different sets of within-class choice probabilities, each for one subclass. Additionally, for the “alternation-seekers” subclass, the choice probability for each alternative at a given choice task is obtained by averaging across all the \(2^{J-1}=16\) combinations. Again, we cannot present detailed market shares across alternatives due to confidentiality restrictions. Instead, we illustrate the order of market shares for the same alternative across (sub)classes. Specifically, we hide the market shares for the first (sub)class in each latent class model (i.e. Class 1 in model 1, and subclass (1,1) in model 2 and model 3), marked with “\(\star\)”. Moreover, we indicate how the market share in each of the remaining (sub)classes changes relative to the first (sub)class for a given alternative. The minus symbol “−” and the plus symbol “\(+\)” suggest that the market share in the corresponding (sub)class is lower and higher than that in the starred first (sub)class, respectively. When there are more than two classes, and using the example where the value is highest in the first class, a single dash “−” indicates the second highest value for that ASC, a double-dash “\(--\)” the third highest, etc.
Model | Model 1: basic LC | Model 2: 2L-LC | Model 3: 2L-LV-LC | ||||||
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Parameter# | 19 | 24 | 47 | ||||||
LL | −16,929.74 | −15,625.74 | Whole: −24,443.96, SC component: −15,613.48 | ||||||
\(\rho ^2\) | 0.4611 | 0.5026 | SC component: 0.5030 | ||||||
BIC | 34,051.26 | 31,493.73 | Whole model: 49,362.33 |
Est. | Rob. t -Rat. | Est. | Rob. t -rat. | Est. | Rob. t -rat. | ||||
---|---|---|---|---|---|---|---|---|---|
\(\beta _{\mathrm {access, 1}}\) | −0.099 | −7.10 | \(\beta _{\mathrm {access, 1}}\) | −0.140 | −4.92 | \(\beta _{\mathrm {access, 1}}\) | −0.137 | −4.88 | |
\(\beta _{\mathrm {egress, 1}}\) | −0.122 | −7.93 | \(\beta _{\mathrm {egress, 1}}\) | −0.170 | −6.10 | \(\beta _{\mathrm {egress, 1}}\) | −0.169 | −6.12 | |
\(\beta _{\mathrm {flight, 1}}\) | −0.078 | −8.90 | \(\beta _{\mathrm {flight, 1}}\) | −0.117 | −6.80 | \(\beta _{\mathrm {flight, 1}}\) | −0.115 | −6.81 | |
\(\beta _{\mathrm {travel, 1}}\) | −0.040 | −11.38 | \(\beta _{\mathrm {travel, 1}}\) | −0.058 | −7.22 | \(\beta _{\mathrm {travel, 1}}\) | −0.057 | −7.27 | |
\(\beta _{\mathrm {cost, 1}}\) | −3.530 | −11.71 | \(\beta _{\mathrm {cost, 1}}\) | −6.670 | −15.05 | \(\beta _{\mathrm {cost, 1}}\) | −6.654 | −14.59 | |
\(\delta _{\mathrm {car, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {car, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {car, 1}}\) | \(\star\) | \(\star\) | |
\(\delta _{\mathrm {transit, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {transit, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {transit, 1}}\) | \(\star\) | \(\star\) | |
\(\delta _{\mathrm {uberpool, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {uberpool, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {uberpool, 1}}\) | \(\star\) | \(\star\) | |
\(\delta _{\mathrm {uberair, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {uberair, 1}}\) | \(\star\) | \(\star\) | \(\delta _{\mathrm {uberair, 1}}\) | \(\star\) | \(\star\) | |
\(\beta _{\mathrm {access, 2}}\) | −0.018 | −2.56 | \(\beta _{\mathrm {access, 2}}\) | −0.061 | −5.08 | \(\beta _{\mathrm {access, 2}}\) | −0.062 | −5.04 | |
\(\beta _{\mathrm {egress, 2}}\) | −0.044 | −5.57 | \(\beta _{\mathrm {egress, 2}}\) | −0.088 | −5.24 | \(\beta _{\mathrm {egress, 2}}\) | −0.091 | −5.10 | |
\(\beta _{\mathrm {flight, 2}}\) | −0.021 | −4.87 | \(\beta _{\mathrm {flight, 2}}\) | −0.044 | −5.29 | \(\beta _{\mathrm {flight, 2}}\) | −0.046 | −5.19 | |
\(\beta _{\mathrm {travel, 2}}\) | −0.021 | −8.37 | \(\beta _{\mathrm {travel, 2}}\) | −0.044 | −10.62 | \(\beta _{\mathrm {travel, 2}}\) | −0.045 | −10.34 | |
\(\beta _{\mathrm {cost, 2}}\) | −1.740 | −15.93 | \(\beta _{\mathrm {cost, 2}}\) | −3.156 | −16.75 | \(\beta _{\mathrm {cost, 2}}\) | −3.185 | −16.55 | |
\(\delta _{\mathrm {car, 2}}-\delta _{\mathrm {car, 1}}\) | 2.499 | 2.40 | \(\delta _{\mathrm {car, 2}}-\delta _{\mathrm {car, 1}}\) | 4.072 | 3.11 | \(\delta _{\mathrm {car, 2}}-\delta _{\mathrm {car, 1}}\) | 4.030 | 3.04 | |
\(\delta _{\mathrm {transit, 2}}-\delta _{\mathrm {transit, 1}}\) | −7.148 | −5.96 | \(\delta _{\mathrm {transit, 2}}-\delta _{\mathrm {transit, 1}}\) | −14.826 | −6.22 | \(\delta _{\mathrm {transit, 2}}-\delta _{\mathrm {transit, 1}}\) | −14.597 | −6.16 | |
\(\delta _{\mathrm {uberpool, 2}}-\delta _{\mathrm {uberpool, 1}}\) | 3.348 | 19.07 | \(\delta _{\mathrm {uberpool, 2}}-\delta _{\mathrm {uberpool, 1}}\) | 4.768 | 16.20 | \(\delta _{\mathrm {uberpool, 2}}-\delta _{\mathrm {uberpool, 1}}\) | 4.868 | 16.40 | |
\(\delta _{\mathrm {uberair, 2}}-\delta _{\mathrm {uberair, 1}}\) | −1.017 | −3.31 | \(\delta _{\mathrm {uberair, 2}}-\delta _{\mathrm {uberair, 1}}\) | −3.600 | −6.86 | \(\delta _{\mathrm {uberair, 2}}-\delta _{\mathrm {uberair, 1}}\) | −3.545 | −6.66 | |
\(\gamma _{1}\) | 0.280 | 3.78 | \(\gamma _{1}\) | 0.452 | 6.54 | \(\gamma _{1}\) | 0.444 | 5.95 | |
– | – | – | – | \(\tau _{\mathrm {NS}}\) | −0.523 | −9.24 | |||
\(\Delta _{\mathrm {car}}\) | 3.315 | 10.00 | \(\Delta _{\mathrm {car}}\) | 3.332 | 9.81 | ||||
\(\Delta _{\mathrm {transit}}\) | 11.205 | 9.45 | \(\Delta _{\mathrm {transit}}\) | 11.244 | 9.32 | ||||
\(\Delta _{\mathrm {uberpool}}\) | −5.008 | −10.25 | \(\Delta _{\mathrm {uberpool}}\) | −5.037 | −10.07 | ||||
\(\Delta _{\mathrm {uberair}}\) | 8.761 | 23.35 | \(\Delta _{\mathrm {uberair}}\) | 8.851 | 22.97 | ||||
\(\lambda _{1}\) | 0.738 | 11.49 | \(\lambda _{1}\) | 0.798 | 11.61 | ||||
– | – | – | \(\tau _{\mathrm {AT}}\) | −0.325 | −5.27 | ||||
\(\zeta _{\mathrm {ATTI8}}\) | 1.616 | 12.78 | |||||||
\(\zeta _{\mathrm {ATTI10}}\) | 1.555 | 12.69 | |||||||
\(\zeta _{\mathrm {GINI}}\) | −0.068 | −13.17 | |||||||
\(\zeta _{\mathrm {TNC}}\) | 1.111 | 12.64 | |||||||
\(\sigma _{\mathrm {GINI}}\) | 0.206 | 75.22 | |||||||
\(\mu _{\mathrm {ATTI8\_1}}\) | −3.250 | −22.72 | |||||||
\(\mu _{\mathrm {ATTI8\_2}}\) | −1.145 | −14.42 | |||||||
\(\mu _{\mathrm {ATTI8\_3}}\) | 0.794 | 12.10 | |||||||
\(\mu _{\mathrm {ATTI8\_4}}\) | 3.004 | 22.58 | |||||||
\(\mu _{\mathrm {ATTI10\_1}}\) | −3.500 | −23.46 | |||||||
\(\mu _{\mathrm {ATTI10\_2}}\) | −2.246 | −21.05 | |||||||
\(\mu _{\mathrm {ATTI10\_3}}\) | 0.121 | 2.01 | |||||||
\(\mu _{\mathrm {ATTI10\_4}}\) | 1.991 | 20.71 | |||||||
\(\mu _{\mathrm {TNC\_no experience}}\) | −0.850 | −16.18 | |||||||
\(\mu _{\mathrm {TNC\_one}}\) | 0.671 | 11.82 | |||||||
\(\mu _{\mathrm {TNC\_two}}\) | 5.226 | 22.10 | |||||||
\(\kappa _{\mathrm {age}}\) | −1.185 | −12.87 | |||||||
\(\kappa _{\mathrm {income}}\) | 0.213 | 10.16 | |||||||
\(\kappa _{\mathrm {female}}\) | −0.660 | −11.23 | |||||||
\(\kappa _{\mathrm {delay}}\) | 0.200 | 3.79 | |||||||
\(\kappa _{\mathrm {vehicles}}\) | −0.094 | −3.36 |
Model | Model 1:basic LC | Model 2:2L-LC | Model 3:2L-LV-LC | ||||||||||
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Parameter # | 19 | 24 | 47 | ||||||||||
LL(whole) | −16,929.74 | −15,625.74 | −24,443.96 | ||||||||||
LL(SC) | – | – | −15,613.48 | ||||||||||
\(\rho ^2\) | 0.4611 | 0.5026 | SC component: 0.5030 | ||||||||||
BIC | 34,051.26 | 31,493.73 | Whole model: 49,362.33 |
Class 1 (novelty-avoiders) | Class 2 (novelty-seekers) | All | Class 1 (novelty-avoiders) | Class 2 (novelty-seekers) | All | Class 1 (novelty-avoiders) | Class 2 (novelty-seekers) | All | |||||
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Value of time (VTT, $/h) | |||||||||||||
Access time | 36.20 | 13.43 | 26.40 | 27.05 | 24.74 | 26.15 | 26.66 | 25.07 | 26.03 | ||||
Egress time | 44.77 | 32.28 | 39.40 | 32.93 | 36.14 | 34.18 | 32.89 | 36.78 | 34.43 | ||||
Flight time | 28.36 | 15.32 | 22.75 | 22.57 | 18.11 | 20.83 | 22.22 | 18.53 | 20.75 | ||||
Travel time | 14.51 | 15.87 | 15.09 | 11.30 | 18.14 | 13.96 | 11.15 | 18.16 | 13.94 |
Class 1 (novelty-avoiders) | Class 2 (novelty-seekers) | Alternation- avoiders (1,1) | Alternation-seekers (1,2) | Alternation-avoiders (2,1) | Alternation-seekers (2,2) | Alternation-avoiders (1,1) | Alternation-seekers (1,2) | Alternation-avoiders (2,1) | Alternation-seekers (2,2) | ||||
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Market share changes | |||||||||||||
Car | \(\star\) | − | \(\star\) | − | − − | − − − | \(\star\) | − | − − | − − − | |||
Transit | \(\star\) | − | \(\star\) | − | − − − | − − | \(\star\) | − | − − − | − − | |||
UberX | \(\star\) | − | \(\star\) | − − − | − | − − | \(\star\) | − − − | − | − − | |||
UberPool | \(\star\) | + | \(\star\) | + | +++ | ++ | \(\star\) | + | +++ | ++ | |||
UberAir | \(\star\) | + | \(\star\) | ++ | + | +++ | \(\star\) | ++ | + | +++ |
Model 1: Basic LC model
Sample-level results
Class-specific results
Model 2: 2L-LC model
Model estimates
Value-of-time results
Within-class choice probabilities
Classes’ profiles
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Subclass (1, 1): 41.35%
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Low tendency to try new modes including UberAIR (i.e. avoid novelty)
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Stable preference across choice tasks (i.e. avoid alternation)
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Subclass (1, 2): 19.77%
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Low tendency to try new modes including UberAIR (i.e. avoid novelty)
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Unstable preference across choice tasks (i.e. seek alternation)
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Subclass (2, 1): 26.31%
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High tendency to try new modes including UberAIR (i.e. seek novelty)
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Stable preference across choice tasks (i.e. avoid alternation)
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Subclass (2, 2): 12.58%
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High tendency to try new modes including UberAIR (i.e. seek novelty)
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Unstable preference across choice tasks (i.e. seek alternation)
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