Mode choice in NYC at block group levell

For Dr. Chow - Travel Behavorial Informatics Spring 2024

1. Mode choice and the microenvironment:
Comparing microenvironment support for the mode choices made in them.
Course: Travel Behavior Informatics - SP2024
Instructor: Professor Joseph Chow
Grace Douglas,
M.Sc., Industrial & Systems Engineering
Ph.D. Candidate, Transportation Engineering
Civil & Urban Engineering, New York University

2. MotivationResearch questionBackgroundMethodsResultsDiscussion
Environments
built to support
active and mass
transport modes
promote healthy
lifestyles
(Dannenberg, 2003)

3. MotivationResearch questionBackgroundMethodsResultsDiscussion
Active and mass mode choice
preference varies by environment
(U.S. Census Bureau, ACS, 2013)

4. MotivationResearch questionsBackgroundMethodsResultsDiscussion
1.Does availability and quality of non-motorized infrastructure
impact active mode choice preference?
2.Do individual attributes interact with built environment
factors to shape mode choice behavior?

5. * Shared spaces are roads designed to minimize segregaton etteen
multmodal trafc (NYC DOT, 2014)
Vehicle-oriented Shared space
•Improve accessibility (Elliot, 2017)
•Motorized (vehicles, mass transit)
•Nonmotorized (pedestrians, cyclists)
•Increase traffic flow (Jacques, 2016)
•Shared space  30,100 people/h
•Vehicle-oriented  12,300 people/h
•Promote sustainability (Creutzig, 2020)
•Vehicle emissions
•Noise pollution
5
Mixed traffic environments…
MotivationResearch questionBackgroundMethodsResultsDiscussion
…. are shared spaces.

6. And shared streets are safe streets...
6
Douglas et al., 2022
•Pedestrian-friendly facilities mitigate driver distraction?
MotivatonResearch questonBackgroundMethodsResultsDiscussion

7. 7
MotivatonResearch questonBackgroundMethodsResultsDiscussion
Vision Zero Network
•Road deaths are unacceptable
•Road deaths are preventable
•Pedestrian islands,
•curb extensions,
•raised crosswalks, and
•leading pedestrian intervals create safer shared spaces
VZ in NYC: Street Improvement Projects (SIPs)

8. Objective: Examine the impact of pedestrian
infrastructure* on mode choice.
MotivatonResearch questonBackgroundMethodsResultsDiscussion
Curb extension
Enhanced crossing
* Pedestrian infrastructure refers VZ sips and supplementing microenvironment features
** Unit of analysis → census block groups
1)Mixed logit model to estimate mode shares in areas* with local
mode support
2)Examine elasticities between mode shares and Vision Zero
pedestrian-oriented improvements

9. DATA
MotivatonResearch questonBackgroundMethodsResultsDiscussion
Source Variables Usage
US Census Bureau Block group geospatial dataunit of analysis
The Regional Establishment
Survey
1)Mode choice
2)Sociodemographics
1)choice outcome
2)individual-specific
NYC and NYS Block Group
Travel O-D estimates
(Ren&Chow, 2023)
1)Travel time
2)Cost
1)alternative-specific
2)general/context variable
NYC Vision Zero Network Active and soft mode support:
1)Shared street facilities
2)Street improvements
1)general/context variable
NYC Open Data Portal Motorized mode support:
1)Average road width
2)Dominant road type
1)general/context variable

10. DATA: The Regional Establishment Survey
•Alternative-specifc
•Mode (sof, mass, personal)
•Traivel tme (hrs)
•Cost ($)
•Indiividual-specifc
•Age
•Income ($ range)
•Mobility status (did your mobility impact your mode choice?)
•Purpose (business, personal, rec, ivisit, food, shopping, school)
MotivatonResearch questonBackgroundMethodsResultsDiscussion
●Historical surivey data (2015-16) (n = 7,840 suriveys total)
●NYMTC Regional Establishment Surivey (RES) is collected to understand traivel paterns in a
28-county geographic area in and around the New York metropolitan area.
●Afer combining with Census Block group statstcal areas, 123 block groups were
considered for analysis.

11. DATA: NYC Open Data Portal
MotivatonResearch questonBackgroundMethodsResultsDiscussion
Open Data is free public data published by New York City agencies and other partners.
●This data source supplemented Vision Zero Street Improvement data to consider motorized
mode support at the block group level.
●Average roadway width
●Dominant roadway classification (type)

12. Park improvements
Slow zones
Enhanced
crossing facilities
Intersection
improvements
MotivatonResearch questonBackgroundMethodsResultsDiscussion
DATA: NYC Vision Zero
Network
Four street improvements project
types were considered:
1)Intersection improvements:
2)Enhanced crossing facilities
3)Park improvements
4)Slow Zones

13. MotivatonResearch questonBackgroundMethodsResultsDiscussion
Modeling – mixed logit
Model formulation:
●Utility estimation:
●Elasticity estimation:

14. MotivatonResearch questonBackgroundMethodsResultsDiscussion
Utilities: Table 3 shows the results
from a mixed logit model with personal
vehicle set as the reference level.
●500 Halton draws were used to estimate the
parameters of the coefficient distributions.
●Intersection improvement SIP shows
significant impact on soft mode
choices.
●Intersection improvements refer to high-risk
intersections that have been completely
renovated with new signage, signaling, and
often changes to the physical built
environment (curb extensions, additional
pedestrian and cyclist facilities)

15. Elasticities
●Smaller green spaces associated
with decreased preference for
non-motorized modes and mass
transit, hinting at the potential role
of larger green spaces in fostering
walkability and transit use.
●A 1% increase in the proportion of
individuals older than 34 years is
associated with a 0.342%
decrease in the probability of
choosing walking mode.

16. References
•Dannenberg, A. L., Jackson, R. J., Frumkin, H., Schieber, R. A., Pratt, M., Kochtitzky, C., & Tilson, H. H. (2003). The impact of community
design and land-use choices on public health: a scientific research agenda. American Journal of Public Health, 93(9), 1500–1508.
https://doi.org/10.2105/ajph.93.9.1500
•Chen, L., Chen, C., Ewing, R., & Chen, C. (2012). The relative effectiveness of pedestrian safety countermeasures at urban intersections —
lessons from a New York city experience. https://nacto.org/docs/usdg/relative_effectiveness_of_pedestrian_safety_counter_measures_chen.pdf
•Department of Transportation (DOT). (2022). Pedestrian Mobility Plan Pedestrian Demand [dataset].
https://data.cityofnewyork.us/Transportation/Pedestrian-Mobility-Plan-Pedestrian-Demand/fwpa-qxaf/about_data
•Speck, Jeff (2012)Walkable City: How Downtown Can Save America, One Step at a TimeNova York: North Point Press, 312 p.ISBN 978-
0865477728
•Bissell, D., Birtchnell, T., Elliott, A., & Hsu, E. L. (2020). Autonomous automobilities: The social impacts of driverless vehicles. Current
Sociology. La Sociologie Contemporaine, 68(1), 116–134. https://doi.org/10.1177/0011392118816743
•da Costa, J. O., Jacques, M. A. P., Soares, F. E. C., & Freitas, E. F. (2016). Integration of geometric consistency contributory factors in three-leg
junctions collision prediction models of Portuguese two-lane national highways. Accident Analysis & Prevention, 86, 59-67.
•Creutzig, F., Javaid, A., Soomauroo, Z., Lohrey, S., Milojevic-Dupont, N., Ramakrishnan, A., Sethi, M., Liu, L., Niamir, L., Bren d’Amour, C.,
Weddige, U., Lenzi, D., Kowarsch, M., Arndt, L., Baumann, L., Betzien, J., Fonkwa, L., Huber, B., Mendez, E., … Zausch, J. M. (2020). Fair
street space allocation: ethical principles and empirical insights. Transport Reviews, 40(6), 711–733.
https://doi.org/10.1080/01441647.2020.1762795