doobneek, Nicholas and Michael are NYU students who proposed a model to reduce crowding on NYC subway.

1. A Utility-Based
Analysis for Lexington
Avenue Subway
Ridership Rebalancing
Michael Frajman
doobneek
Nicholas Hudanich

2. 2
Background
● Lexington Ave Subway = Most congested
subway line in North America
● Alternatives (2AS, M15, M1)
● Alleviation of 2AS Extension
● Crowded conditions consequences
○ Dwell times
○ Accident risk (3/day)
○ Crime
○ Public health: disease vector
https://comptroller.nyc.gov/wp-c ontent/uploa ds/documents/The-Human-C ost-of-Subway-Delays.pdf

3. 3
Background
● Project: transit
improvements/investments on UES, and
the resulting mode shifts induced
○ Example: More frequent buses =
shift from subway?
● Create a model for current conditions
● Use model to anticipate changes under
these improvements

4. 4
Model Summary
● Find rebalancing solutions to mitigate congestion on the Lexington Avenue
Subway. Consider the AM peak hour.
● Consider the utility of existing modes, perturb utilities as needed
● M15 bus, M1 bus, 2nd Avenue Subway, and the Lexington Avenue Subway
● Phase I: Collect data, formulate model for existing conditions
○ MTA entry data
○ Census population data
● User time cost coefficient determination
○ Utility function formulation
○ Chi Square minimization function
● Phase II: Use set constants to assess changes
○ Headway changes
○ Cost changes
○ New infrastructure
○ Less travel time to core

5. 5
Data Collection
● Census block aggregation for single point
zone-based utility consideration

6. 6
Data Collection
● Zones created to best represent set of
selections for transit that zone users have
● Example:

7. 7
Data Collection

8. 8
Data Collection
● Scaled to entries

9. 9
Data Collection
● Subway entries
○ MTA daily weekday average
○ Halvorsen et al: L train estimate for
12% of daily load in peak hour, 8AM-
9AM
● Bus entries
○ Limited data
○ Use full line ridership, allocate to our
stations by population / total line
NTA population

10. 10
Formulation
● Utility functions for each of the six zones, for each alternative
● Consider
○ Walking time cost (α)
○ Bus waiting time cost (β)
○ Bus travel time cost (γ)
○ Subway waiting time cost (δ)
○ Subway travel time cost (ε)
● Example: Zone 1

11. 11
Formulation
● Excel optimization program
● Using given constants and “transit takers”, calculate Ps for each alternative in each
zone.
● Multiply by transit takers for total usage
● Calculate Chi Square of each of the 25 stations
● Minimize Chi Square as the objective

12. 12
Solution (Current Conditions)
● Solved

13. 13
Alternative 1
● M1 Bus headway reduced to 4 minutes (from 15)

14. 14
Alternative 2
● M1 Bus headway reduced to 8 minutes (from 15)

15. 15
Alternative 3
● M1/M15 Better Bus Lane Enforcement (-30% Ma, -20% M15)

16. 16
Alternative 4
● M1 Bus headway reduced to 4 minutes (from 15)
+ Better Bus Lane Enforcement (-30% Ma, -20% M15)

17. 17
Alternative 5
● 25% Improvement to Waiting Time Cost (Facilities)

18. 18
Alternative 6
● 25% Improvement to Waiting Time Cost (Facilities)
+ Better Bus Lane Enforcement (-30% Ma, -20% M15)

19. 19
Alternative 7
● 2nd Avenue Subway 2nd Phase Full Build

20. 20
Alternative 8
● 2nd Avenue Subway 2nd Phase Full Build + Better Bus Lane Enforcement

21. 21
Insights
● Short term: Better Bus Lane Enforcement
● Short term: Facilities improvement (Even
just M1 only)
● Long term: Second Avenue Subway Phase 2
● Not considered in this study / future study:
○ Rideshare
○ Cycling
○ Temporal variation (we assume flat
arrival for 1 hour)
https://www.cbsnews.com/pictures/building-nycs-second-ave-subwa y/
https://www.nyc.gov/html/brt/html/about/bus -lanes.shtml

22. 22
Thank you!