1) The document proposes replacing subway service in Manhattan with on-demand ridesharing during overnight repair periods to reduce costs. 2) A simulation would be used to model routing on-demand vehicles to service subway trips between 12AM-5AM using demand data and routing algorithms. 3) Key metrics like vehicle needs, passenger wait times, and repair costs vs transportation costs would be compared to evaluate the alternative. The simulation aims to answer if on-demand ridesharing can adequately replace subway service during repairs.

1. Replacing
Subway
Service with
on-demand
ridesharing Christian Moscardi,
NYU CUSP, C2SMART Lab

2. Motivation
● Repairing the subway while
running: $$$
● BUT, NYC is the city that never
sleeps
Big question: to facilitate repairs,
can we shut down Manhattan’s
subway during times of minimal
demand and replace it with
alternative transportation?

3. Key Measures
● Number of vehicles required
● Passenger Level of Service (wait time, travel time)
● Ideal: repair cost vs. cost of running vehicles

4. How to answer? A simulation
● Set up a simulation of a station-to-station, on-demand ridesharing vehicle
system
● Take subway trips and service them with an on-demand system
● Compare key metrics
Parameters
● Number of vehicles
● Vehicle capacity
● Passenger tolerances to delay

5. Data
● Demands
○ NYMTC Travel Survey (O/D Only)
○ MTA Turnstile data (coming soon)
● Road Network
○ NYC LION Shapefile
○ MTA Subway Station Locations
○ Road Speeds: Taxi Data

6. Step 1: NYMTC Data
● Filter to trips between 12AM - 5AM
● Infer O/D stations from ‘TAZ’ + likely route
● 114 trips involving Manhattan between
12AM - 5AM
● Network setup much like Spiess & Florian
(w/o hyperpaths)
Origins

7. Step 1: NYMTC Data
(final results)
(Actual travel time) - (my guess) [in minutes]

8. Step 2: Road Network
● Need road network

9. Step 2: Road Network
● Need road speeds
● Road speeds: iterative algorithm, use taxi
trips
● (Needs some work)

10. Step 3: Simulation
● How to efficiently run an on-demand ridesharing system?
● Alonso-Mora et. al.

11. Example What is the optimal route?
travel(v, R)
R=set of requests
v= vehicle
Returns: feasibility, minimum
cost of having V pick up /
drop off requests R
Constraints:
1. max. waiting time
2. max. out-of-the-way
time

12. Example

13. Example

14. Example
1 vehicle, 2 requests = ~16
1 vehicle, 4 requests = ~1776 possible
combinations
1 vehicle, 10 requests = O(10^10) =
O(110,000,000,000) possible
combinations
And what if we add more vehicles!?!??
We can scale with heuristic techniques

16. Example
Result of applying step B)
and generating RV graph.
Red lines are
request-vehicle pairings
Blue line is request-request
pairing

17. Example
Result of applying step B)
and generating RTV graph.

18. ILP Formulation

19. Example
Result of solving ILP

20. (Immediate) Future Work
● Turn into a clock-based simulation
● Finish out some sort of demand generation (turnstile data? Poisson?)
● Implement ILP solver (currently using greedy heuristic)

21. Bigger-picture work
Alonso-Mora et. al.
● Speed up travel(v, R) function - dynamic programming?
● Computation times still slow, parallelize / distribute?
● Simulation batches requests in 30s intervals -- need to determine user
tolerance
NYC Subway Shutdown
● What about weekends? Other off-peak hours?
● Can we better infer or estimate demand (hello, NYCT!)
● What are appropriate boundaries for train logistics?

22. Thank you!
https://github.com/cmoscardi/subway_explore
https://twitter.com/c_moscardi
clm633@nyu.edu