Term project for TR-GY 7073 Travel Behavioral Informatics, NYU

1. Travel mode choice modeling for
Air-taxis
Srushti Rath
Graduate student
Travel Behavior Informatics-Course project
Course instructor: Prof. Joseph Chow
Source: Greenbiz

2. ➢ On demand Urban
Air Mobility(UAM)
❑Air taxi: electric vertical take off and landing (eVTOL)
❑Capacity: 3-4 passengers
❑Noise: about 15db (quieter than helicopters)
❑Vehicle size: 45 ft. max dimension (including fully
extended rotors)
❑Cruise: 1000 ft. above ground level (speed: 150-200 mph)
❑Cost per passenger mile:
Initial $5.73 → $1.86 → $0.44
❑Charge: one time - can fly up to 60 miles.
Source: Bell
Autonomous Air taxi model (Bell
nexus) introduced at CES 2019 -
partnership with Uber Elevate

3. ➢ Survey by Airbus
(Uber Elevate summit)
❑Study: best use cases for UAM adoption
❑Three focus groups: NY, Frankfurt and Shanghai
✓Value proposition: time saving, more speed in
urban mobility
✓Best use case: airport transfers
✓Impact: business trips
✓Time reduction – 50% → acceptable prices: 2-2.5
times taxi price (USA/GER), 6 times taxi price
(China)
Source: Air bus

4. Infrastructure:
❑ Skyports/Vertiports : a place for boarding and alighting
passengers along with parking spaces, charging points,
and other facilities.
❑ Vertistop: a touchdown and liftoff area for boarding
and alighting process.
UBER | Humphreys & Partners Architects

5. ➢ Project idea
Develop discrete mode choice
model to estimate demand for
urban air mobility (on demand
service- Air-taxis).
Interpret major factors affecting
the demand.
Use the model in planning of
skyports for air taxis.

6. ➢ Model description
• Cost ($)
• Travel time (min)
Mode specific:
• Age
• Gender
• Income
• Employment
• Trip purpose (work
trip, non-work trip)
Individual specific :
Explanatory variables
Disturbances
UTILITY
CHOICE
• Use case: airport transfers.
• Alternatives (modes) :
Existing -Transit, Taxi, Car;
New - Air taxi.
• Stated preference survey : online
audience (150 respondents) using survey
monkey.
• Questionnaire: individual specific;
alternative specific (travel time and cost
for existing modes of travel from home
to airport).
• Each respondent : 5 scenarios for air
taxi (each scenario presented: travel
time and corresponding cost).
➢ Survey description

7. ➢ Travel time using different modes
• Access time (from home to nearest skyport), using
Uber/Lyft/taxi service/personal car.
• Transfer time (from vehicle to skyport).
• Air taxi time (flying to landing zones near
airport).

8. ➢Air taxi scenarios in survey:
• Input from previous research on “Airtaxi skyport location problem
for airport access”*. This study is referred as SLP in the slides.
• Considering 10 skyports (optimized locations) in NYC (found in
SLP), the following are used to define air taxi scenarios:
• Travel time:
1. Access time: 5, 10, 15, 20, and 25 min.
2. Transfer time: 5, 10, and 15 min.
3. Air taxi time: Avg. 10 min (Euclidean distance and 150 mph).
• Travel cost:
1. Access cost: Uber/Lyft market price :$3 base fare+ $0.3/min
+$1.5/mile +$1 safety fee.
2. Air taxi cost: using Uber’s proposed price/passenger mile, and air
taxi time and speed (specified above):
$143.25 (short term), $46.5(medium term), and $11(long term)
*S. Rath and J. Y. Chow, “Air taxi skyport location problem for airport
access,” arXiv pre-print, vol. abs/1904.01497, 2019. [Online].
Available: https://arxiv.org/abs/1904.01497:

9. ➢ Sample population distribution
Income
Scenario1
35 min,
$56.50
Scenario 2
55 min,
$30
Scenario 3
40 min,
$159.25
Scenario 5
25min,
$150.25
Scenario 4
35 min,
$18
Air taxi preference
Age

10. ➢ Methodology Filtered survey
data
Before air taxi
(115 responses)
MNL (using
R)
Estimate mode share
After air taxi (555
responses)
MXL : Panel dataset
(using R)
Estimate mode share
and derive demand at
skyports
Discrete choice analysis →
• Before air taxi → Random
utility model (McFadden
1974): Multinominal logit
model (MNL)
• After air taxi → Mixed
logit model: MXL
(McFadden and Train
2000)

11. R^2 = 0.16
Train data accuracy:
Transit: 0.64, Taxi: 0.61, Car: 0.63
Test data accuracy:
Transit: 0.60, Taxi: 0.60, Car: 0.65
➢ Results: MNL model (before air taxi)
Significant variables:
Individual specific : age and income
Alternative specific : travel time and cost
Utility functions (using R):
Age category Income
18-24 1 Under $15,000
25-34 2 $15,000 to $29,999
35-44 3 $30,000 to $49,999
45-54 4 $50,000 to $74,999
55-64 5 $75,000 to $99,999
65+ 6 $100,000 to $150,000
7 Over $150,000

12. βtraveltime = N~(-0.00189, 0.000519)
βcost = N~(-0.02082,0.000135)
*Age category 3 and 5, and income category 6 and 7 (high income) have positive
effect on utility of air taxi.
R^2 = 0.19
Train data accuracy:
Transit: 0.60, Taxi: 0.54, Car: 0.63, Air taxi: 0.67
Test data accuracy:
Transit: 0.60, Taxi: 0.58, Car: 0.61, Air taxi: 0.62
➢ Results: MXL model (before air taxi)
Travel time and cost : normally distributed and correlated.
Utility functions (using R):
Age category Income
18-24 1 Under $15,000
25-34 2 $15,000 to $29,999
35-44 3 $30,000 to $49,999
45-54 4 $50,000 to $74,999
55-64 5 $75,000 to $99,999
65+ 6 $100,000 to $150,000
7 Over $150,000

13. ➢ Airport demand:
Choropleth map showing demand per taxi zone in NYC to major three airports
• Geographic boundaries:
Taxi zones in NYC.
• Airport trips: derived
using FHV trip record
data (NYC TLC
commission) in January
2018.

14. Data:
• Findings from SLP used for:
1. Skyport location: out of 10 skyports in
NYC, one skyport in Manhattan (i.e., taxi
zone 79) is selected for demand estimation.
2. Demand segments: origin taxi zones
served by the selected skyport to reach
airports.
• Individual specific attributes: NTA
data: American community survey
(2008-2012).
• Mode specific attributes: average values
(using google maps, and market rates);
transfer time = 10 minutes.
Taxi zone
ID Taxi zone name NTA code
population
total
median
income
category
median age
category
4 Alphabet City MN28 74243 2 3
79 East Village MN22 43755 3 2
107 Gramercy MN21 26075 5 2
113 Greenwich Village North MN23 67185 5 2
114 West Village MN23 67185 5 2
144 SoHo MN24 40059 5 2
148 Lower East Side MN27 46550 2 2
211
SoHo-TriBeCa-Civic Center-Little
Italy MN24 40059 5 2
219 Springfield Gardens South QN03 19901 4 2
224 Stuy Town/Peter Cooper Village MN50 21864 4 3
232 Two Bridges/Seward Park MN28 74243 2 3
➢ Demand estimation:
Age and income for demand segments for selected skyport
*For calculation: It is assumed that the FHV demand derived per segment (per airport) represents total aggregated demand
(shared by different travel modes).

15. Taxi zone ID NTAcode Total demand Transit% Taxi% Car%
4 MN28 2115 4 20 76
79 MN22 9196 17 49 34
107 MN21 8196 46 14 40
113 MN23 4122 58 8 34
114 MN23 3454 53 11 36
144 MN24 5551 57 11 32
148 MN27 6122 11 15 74
211 MN24 3211 51 13 36
219 QN03 12 21 12 67
224 MN50 2702 2 23 75
232 MN28 2300 5 20 75
Mode share (%) : Transit, Taxi, Car
➢ Mode share (before air taxi):

16. ➢ Mode share after air taxi:
This study is based on few assumptions but the analysis provides the following insights:
• Mode choice modeling can be used for skyport planning (such as demand analysis, inferring high
demand generating segments, delay estimation, air taxi fleet and facilities planning).
• Estimation under different price scenarios gives an idea on mode shifts.
• Individuals’ trip data (to airport) for different modes of travel could produce promising results.
Mode share Short term Medium term Long term
Taxi
zone ID NTAcode Total demand Transit% Taxi% Car% Airtaxi% Transit% Taxi% Car% Airtaxi% Transit% Taxi% Car% Airtaxi%
4 MN28 2115 30 14 43 13 29 15 43 14 25 14 39 22
79 MN22 9196 33 16 39 12 31 16 39 14 25 14 35 25
107 MN21 8196 38 18 30 14 36 18 30 16 31 17 27 24
113 MN23 4122 41 16 28 14 39 17 28 16 33 16 26 24
114 MN23 3454 40 17 29 14 38 18 29 16 32 17 26 24
144 MN24 5551 40 17 29 14 38 18 29 16 33 17 27 24
148 MN27 6122 17 8 65 9 17 9 67 8 15 8 63 13
211 MN24 3211 39 19 28 14 37 19 28 16 32 18 26 24
219 QN03 12 11 1 84 3 11 1 86 1 11 1 86 2
224 MN50 2702 6 3 85 6 6 3 88 3 6 3 86 5
232 MN28 2300 29 14 44 13 28 15 44 13 25 14 41 20
Mode share (%) : Transit, Taxi, Car, Air taxi – price scenarios: short term, medium term, long term

17. ➢ References:
• J. Holden and N. Goel, “Uber elevate: Fast-forwarding to a future of on-demand urban air transportation,” Uber
Technologies, Inc., San Francisco, CA (2016).
• R. Rothfeld, M. Balac, K. O. Ploetner, and C. Antoniou, “Initial analysis of urban air mobility’s transport performance in sioux
falls,” in 2018 Aviation Technology, Integration, and Operations Conference (2018).
• M.Thompson, “Panel: Perspectives on prospective markets,” in Proceedings of the 5th Annual AHS Transformative VTOL
Workshop (2018).
• Garrow, Laurie A., et al. "If You Fly It, Will Commuters Come? A Survey to Model Demand for eVTOL Urban Air Trips." 2018
Aviation Technology, Integration, and Operations Conference.
• Balac, Milos, et al. "Demand estimation for aerial vehicles in urban settings." IEEE Intelligent Transportation Systems
Magazine (2018).
• M. D. Patterson, K. R. Antcliff, and L. W. Kohlman, “A proposed approach to studying urban air mobility missions including
an initial exploration of mission requirements,” nasa.gov, 2018.
• McFadden, D.: Conditional logit analysis of qualitative choice behaviour. In: Zarembka, P. (ed.) Frontiers in Econometrics,
pp. 105–142. Academic Press, New York (1974).
• McFadden, Daniel, and Kenneth Train. "Mixed MNL models for discrete response." Journal of applied Econometrics 15.5
(2000): 447-470.
• “Taxi Trip Record Data, NYC TLC Commission,” http://www.nyc.gov/html/tlc/html/about/trip record data.shtml.
• S. Rath and J. Y. Chow, “Air taxi skyport location problem for airport access,” arXiv pre-print, vol. abs/1904.01497, 2019.
[Online]. Available: https://arxiv.org/abs/1904.01497.

18. Thank You