New York City Transit

Real-Time Service Enhancement Tools

at New York City Transit

Alla ReddyBrian LevineSystem Data & ResearchOperations Planning, NYC Transit

PresentedatTransitData20162nd InternationalWorkshoponAutomatedDataCollectionSystems

August9,2016

OP/SDR works closely with operating departments to use real-time data for better service management

• Real-time Tools have been developed by analytical staff at NYCT to aid operating departments in managing service

– Stringline Charts plot real-time train movements in time-space diagram (2013-present)

– Gap table alerts dispatchers to large gaps and/or bunching (2014)– Real-time Platform Crowding Estimation to alert dispatchers of overcrowded

platforms (2015)– Service Intervention Recommendation Engine provides dispatchers with

suggestions of holding trains or skipping stations to evenly space service (2016)• Successes

– Constant communication between analytical staff and Rail Control Center personnel to continuously incorporate feedback and input into these tools

– Converted theoretical knowledge to practical tools being used on a daily basis• Challenges

– Data feeds being used for unintended purposes, lack of direct real-time access to data requires overcoming imperfect or lagged information

– Changing ingrained culture and habits

1

Stringlines: Motivation

• Developed in 2013 using GTFS-RT data (based on ATS-A for 1-6 lines)• Expanded to use Programmable Logic Controller (track circuit) data

combined with manually entered train identification by dispatchers– Building our own “ATS-A like” system in house with no additional staff

• Also incorporates L line, which runs on CBTC (5+ years to obtain data)• New Functionality

– Links to incident database to provide contextual information for service irregularity– Wait Assessment mode showing headway regularity overlaid on top of Stringlines

• Future work includes real-time performance monitoring (ATS Data Mart)2

https://rt-string.nyct.com/rtstring/rtstring.html

Real-time Platform Crowding Tool: Motivation

3

InformationType Source DataType Application

• Passenger entrances

OMB AFC Entry Data

Static (historic)

§ Historic data used to forecast station entrances§ 6-minute AFC data uniformly distributed in 30-second intervals

• Passenger direction & train preferences

Subway Ridership Model AFC OD Table

Static

§ Incoming passengers assigned direction and train preferences based on 30-min period§ Assignment to a preference group does not change due to gaps in service

• Predicted Train Arrival & Departure Time*

GTFS-RT Data Feed

Dynamic (Real-time)

§ Train arrivals rounded to 30 second interval§ Passengers leave platform at arrival of needed train

A

B

C

• Real-time information can help alleviate crowding and prevent dangerous conditions by helping operating departments make better service management decisions (rerouting trains, diverting customers)

• Production model developed using Python and runs in real-time

Real-time Platform Crowding Tool: Wall Street Pilot

4(2) Flatbush Ave –Brooklyn College

(3) New Lots Ave

(3) Harlem – 148 St(2) Wakefield – 241 St

Wall Street

Atlantic Ave –LIRR Connection

Penn Station (Long Island Railroad, NJ Transit, Amtrak); Times Square (Port Authority Bus Terminal)

Real-time Platform Crowding Dashboard: Deployed for Wall Street 2/3 Station

5Link to dashboard

Auto updates Peak Crowding Passenger Profile (for appropriate

service actions)

A real time train arrival feed is combined with historic ridership

patterns to estimate current crowding

Past Crowding

Predicted Crowding

Service Intervention Recommendation Engine (SIRE): Motivation

6

“Stringlines arehardtoreadandinterpretinrealtime”

“Gaptable&platformcrowdinghelpdrawattentionbutdon’ttell

mewhattodo”

Dispatchersmustinvestigatetodeterminewhat(ifany)actiontotake

q How heavily loaded is the train likely to be?

q How busy are upcoming stops?

q What action should be taken (skip/hold)?

q Which station(s) should be skipped?

q Where should the train be held?

q How long should the train be held?

Ø Not based off of a guess, due to perceived notions, or “that’s the way it has always been done”

Tools were previously developed to help dispatchers manage even spacing, but required active effort

SIRE dashboard tells dispatchers exactly where to hold & skip for optimal spacing and benefit

7

NBtrainpassedpeakloadpointgivenskips,servingsomein

betweenstations

Passenger Time Savings from SIRE Holds

8

Hold

Time

Passengers already on train delayed by hold

Boarding passengers who wait longer due to hold (would have made train w/o hold)

Boarding passengers who wait less due to hold (would have to wait for next train w/o hold)

• Can calculate net passenger time savings from holds prompted by SIRE

People who benefit: passengers boarding downstream who wait less because of even spacing

People penalized: • Passengers already on train delayed

by hold• Passengers waiting longer due to hold

Factors not considered:• Hold’s benefit to following trains

(shorter dwell time)• Time savings from prevented denied

boardings• Train “catching up” further with

previous train w/o hold

Methodology probably underestimates net passenger benefit from holds

Example: Passenger Time Savings from SIRE Holds

9

125StholdperSIRErecommendation

Southbound, 5/19/16

Est.trajectoryw/ohold

• 3 min SB hold at 125 St prompted by SIRE (0932+ PEL/BBR)

• Hold saved 4048 min of passenger time

– 7521 min saved by 1140 boarding passengers waiting less s/o 125 St

– 2941 min lost by 840 other boarding passengers waiting more s/o 125 St

– 532 min delay to 152 passengers on train going through 125 St

– SB holds at 125 St can produce very large passenger time savings

Future Plans

• Long-term plans to integrate SIRE and other tools into a next generation dispatching/tracking system

• Continue collaborating with RCC staff to fine tune and improve tools • Direct real-time data feed of train movements on the A Division

expected later this year (Data Mart)• Additional PLCs hooked up to NYCT network would allow better train

tracking on the B division• Next generation fare payment system would improve ridership data

quality and transmission time

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Appendix

Platform Crowding MethodologyStation entries by time, day, month estimated from historic AFC data

• Defined linear regression estimate for each 6-minute AFC period– 6-minute predictions uniformly distributed into 30-second time intervals

• Dependencies on year, month, day and working day– Has set of major/minor holidays

• Passengers assumed to arrive to platform 30-seconds after turnstile– Differs by station/platform

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Platform Crowding MethodologyPassenger direction & preferences modeling

• Wall Street pilot station has a single platform• Passenger assigned to classes of train preference in half-hour

increments– Based on modeled OD estimation and route choice

• Removed from on-platform volume when their train option(s) arrive(s)• On-platform volume estimate updated every 30 seconds

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Indifferent 2Train 3TrainNB 73% 2% 1%SB 21% 2% 2%

Station LikelyLineUsedLikely

Direction34St-PennStation Indifferent NB

AtlanticAv Indifferent SB42St-PA BusTerminal Indifferent NB

TimesSq-42St Indifferent NB72St Indifferent NB96St Indifferent NB14St Indifferent NB

Flushing-MainSt Indifferent NBBoroughHall Indifferent SB

86St Indifferent NB

Top10destinationsfromWallSt4pm- 6.59pmon5th Feb2015(makeup49%oftrips)

Platform Crowding Validation Prediction generation validated with manual field checks

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• Estimations were higher than manual checks, likely due to manual undercounting due to platform obstructions

Identifywhere/whencanskip/hold

• Dynamicbytimeofday• Basedonhistoricalridership• Nottrain-specific

Identificationofcandidatetrainsforskips/holds

• Basedonreal-timemovements,crowding,etc.

Matchtrainswithpotentialactions

• Lookforpossibleskips/holdsforcandidatetrains

• Choosetoprecommendations

Stopsoktoholdat/skip TrainIDsofcandidates

Finaloutputofskip/holdrecommendations

SIRE MethodologySkip/hold logic captures good dispatcher management + constraints

15

Atrainmightneedaholdif…

q Gap after – gap before > x min

Imightneedahold

Butnotif…

q Put-in or merge will make gap after smallerq The estimated time savings (in passenger-

minutes) is negative

Atrainmightneedaskipif…

q Gap before – gap after > x minutesq Train > 2 stops from terminal of trip

Imightneedaskip

Butnotif…

q Put-in or merge will make gap ahead smallerq Dispatcher already gave train a skipq SIRE already gave train’s leader a holdq Heavy boarding/alighting or ADA station

Example: Hold with Net Time Loss not Recommended by SIRE

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HuntsPtAveholdbyCTD

Est.trajectoryw/ohold

• SIRE doesn’t recommend holds that don’t save passenger time

• 2 min hold at Hunts Point done by CTD w/o SIRE recommendation (808 BBR/PEL)

• Hold produced net 131 minutes of delay to passengers

– 349 min saved by 22 boarding passengers waiting less Hunts Point and north

– 95 min lost by 42 other boarding passengers waiting more Hunts Point and north

– 385 min delay to 169 passengers on train going through Hunts Point

Northbound, 5/24/16