Data Driven Approach - 15th TRB National Transportation Planning

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Simplified Data Driven Methods for FTA Small Starts Project Evaluation Jeffrey Roux (AECOM) Prasad Pulaguntla (AECOM)

May 8, 2013

Agenda • Richmond Broad Street BRT Project Overview • The Challenge • Approach • Bump on the Road • Project extensions • Lessons Learned

• Acknowledge MANY contributions to the study

SERPM 6.7

February 17,

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Broad Street Corridor • Broad Street is THE bus corridor in Richmond, VA with 17,000 daily transit trips. • Broad Street serves as main GRTC artery in Downtown • Today: bus bunching, substandard lanes and accidents result in a relatively slow and unreliable journey. • Full BRT saves up to 3 min. on local bus trips and 6 min. on BRT/express bus trips Page 3

Problem Statement

• Existing conditions – 48 buses/direction/peak hour within downtown RIC – Current service is “local” stopping every block – Narrow Parking lane (8’) dedicated to buses in peakperiod (with poor enforcement) 

Problems – High bus volumes with multiple routes entering the trunk line lead to bus bunching and low reliability – Existing narrow downtown bus lanes cause conflicts and accidents – Conga-line conditions leads to very slow travel times

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Goals and Objectives

•Provide more attractive transit service – Improve travel times – Reduce accidents – Improve customer experience/amenities – Improve system efficiency – Improve reliability – Improve convenience, efficiency of transfers – Expand market

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Project Tactics • BRT trunk line to provide a higher quality transit service: – Higher quality, low floor, branded vehicles – Formal BRT transit stops • Formal branded shelters • Off-board fare collection

– Dedicated bus lanes for half of corridor to speed trips – Traffic signal priority – Trunk line BRT is 6 min faster to Downtown (vs. express bus without priority treatments) and 14 min faster compared to local bus.

• Consolidated bus stops in Downtown for ALL buses – Downtown operations consolidate to three stops – Multi-platform boarding – Local buses are 3 min. faster through Downtown

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The Challenge

• Prepare patronage forecast & FTA Project Justification materials for Small Starts application • Off the shelf tools (in 2010): – VDOT Richmond/Tri-Cities Forecasting Model (RTFM): • Highway focused forecasting model • Very good representations of highway travel speeds • Basic transit network and limited mode choice capabilities – Fall 2009 GRTC On-Board Survey

• Approach: Develop data-driven approach from RTFM networks and on-board surveys Page 7

Data Driven Approach – Two Elements • Transit Networks: – Recoded RTFM Transit Networks (CUBE TP+/TRNBUILD) – Calibrated bus travel times from VDOT peak/off-peak highway travel times (equations of motion) • On-Board Transit Survey – Trip Tables: – VDRPT surveyed GRTC routes in Fall ’09 – Comprehensive picture of GRTC existing markets • Approach: Measure how existing riders (surveyed) impacted by project and estimate new riders through representation of transit network changes

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Transit Network Development Recoded RTFM Transit Networks to Reflect Nov. ‘09 Service (survey): – Separate peak/off-peak transit networks: • Peak (7-9 AM) • Off-Peak (10-2 PM) – Correct route routing (including loops through D’town) – Zone splits in project corridor – Properly reflect stop locations – Developed bus speed relationships, using equations of motion based approach: • AM Peak – Congested highway travel times • Off-Peak – Free flow highway travel times Page 9

Bus Travel Time Validation Approach: Accurately represent bus travel speeds by pivoting off of RTFM highway speeds Process built “equation of motion” approach to replicate current scheduled travel times of all buses: – Used typical bus acceleration/deceleration rates – Calibrated an average “dwell” times per stop – Process worked very well as virtually all buses are within 15% or (3-4 min.) of scheduled end-to-end running times

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2009 GRTC On-Board Survey

4,500 completed surveys representing 32,900 GRTC boardings: – Included VCU and non-VCU bus routes – For approach we processed: • Trip Ends (Productions and Attractions) • Mode of Access (PNR, drop-off and walk) • Time of Day (peak & off-peak) • Converted boardings to linked trips – Conducted on all routes, except inter-city routes: • Petersburg-Downtown Richmond Express • Fredericksburg-Ashland-Downtown Richmond Express • Not material to Project Page 11

2009 GRTC On-Board Survey Trip Productions

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2009 GRTC On-Board Survey Trip Attractions

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“Traditional” Survey Tabulations Table 1: GRTC Regional Bus Service Year 2009 Average Weekday Linked Bus Trips

Table 2: VCU Bus Service Year 2009 Average Weekday Linked Bus Trips

Home Based Work (HBW) Trips

Home Based Work (HBW) Trips

Period Peak Period

Access Mode Walk Drive SubTotal Off-Peak Period Walk Drive SubTotal Total

Vehicles/Household 0 Car HH 1+ Car HH 2,236 1,972 71 1,449 2,307 3,421 2,541 1,714 72 146 2,613 1,860 4,920 5,281

Total 4,208 1,520 5,728 4,255 218 4,473 10,201

Period Peak Period

Access Mode Walk Drive SubTotal Off-Peak Period Walk Drive SubTotal Total

Home Based Other (HBO) Trips Period Peak Period

Access Mode Walk Drive SubTotal Off-Peak Period Walk Drive SubTotal Total

Access Mode Walk Drive SubTotal Off-Peak Period Walk Drive SubTotal Total Grand Total

Total 138 218 356 68 73 141 497

Vehicles/Household 0 Car HH 1+ Car HH 85 346 0 71 85 417 253 629 0 91 253 720 338 1,137

Total 431 71 502 882 91 973 1,475

Vehicles/Household 0 Car HH 1+ Car HH 151 780 13 726 164 1,506 245 924 21 632 266 1,556 430 3,062

Total 931 739 1,670 1,169 653 1,822 3,492

Home Based Other (HBO) Trips Vehicles/Household 0 Car HH 1+ Car HH 1,359 913 35 145 1,394 1,058 2,696 2,021 71 101 2,767 2,122 4,161 3,180

Total 2,272 180 2,452 4,717 172 4,889 7,341

Period Peak Period

Access Mode Walk Drive SubTotal Off-Peak Period Walk Drive SubTotal Total

Non Home Based (NHB) Trips Period Peak Period

Vehicles/Household 0 Car HH 1+ Car HH 10 128 4 214 14 342 36 32 0 73 36 105 50 447

Non Home Based (NHB) Trips Vehicles/Household 0 Car HH 1+ Car HH 873 406 28 245 901 651 1,467 1,153 72 103 1,539 1,256 2,440 1,907 11,521

10,368

Total 1,279 273 1,552 2,620 175 2,795 4,347

Period Peak Period

Access Mode Walk Drive SubTotal Off-Peak Period Walk Drive SubTotal Total

21,889

Grand Total

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818

4,646

5,464

Development of a Base Year 2009 Transit Trip Table • Developed CUBE/TP+ Survey-Based Trip Table (Fall ‘09) – Stratified by: • Time of Day (Peak and Off-Peak) • Mode of Access: – PNR & Walk – KNR treated like walk (with drop-off location treated as PROD end) • VCU and non-VCU trips stratified • VCU Trips Stratified Separately – Operates under contract to GRTC – Free w/valid student/staff ID – Main to Downtown Campus shuttles & fringe parking – Policy of VCU students on BRT unknown Page 15

Calibration of Transit Path Weights • AECOM calibrated TRNBUILD path-building and assignment weights through iterative assignment of transit trip table • Started with FTA “national experience” weights and tuned

• Final weights used were: – – – – – –

IVTT Weight = 1.0 Waiting/Transfer Waiting Time Weight = 1.5 Walk Time Weight = 2.0 Drive Time Weight = 2.0 Transfer penalty = 6.0 min per transfer Walk speed = 3 mph

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2009 Weekday Modeled vs. Observed by GRTC Route Group

Local Routes

Route Number

1/2 3/4 5 6 7 8 10 11 13 16 18 19 20 22 24 32 34 53 37 61 62/63 67 68 70/71 72/73 74 91 92 93 100 999 - Express/BRT Subtotal

2009 Surveyed Boardings

2,343 1,658 3625 1,010 1,856 155 312 630 249 294 32 327 430 2,176 1,649 1,902 3,304 19 1,406 1,457 902 219 17 63

26,035

2009 Survey Assignment

2,275 3,079 3,639 1,391 1,172 56 260 384 333 121 556 2,520 1,835 1,676 2,840 155 1,373 1,301 568 367 27 25,928

Group

Express Routes

VCU Routes

Route Number

26 27 28 29 64 65 66 81 82 Subtotal 84 86 87 95 99 Subtotal Total

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2009 Surveyed Boardings

196 228 26 464 214

176 308 1,612 3,162 987 804

2009 Survey Assignment

480 178 84 275 242 169 82 371 1,881

4,953

3,248 862 374 577 5,061

32,600

32,870

Developing Project Forecast/FTA Justification Materials • 2009 demand grown to represent 2015 no-build using MPO estimates of POP growth • Development of CUBE/TP+ process where calibrated path-weight parameters are multiplied to create an equivalent IVTT “transit impedance” score for each I-J pair • Alternative specific effects for BRT built into the transit impedence – 5% discount for BRT IVTT – 5 min. “constant effect” for BRT only riders, 2 min for BRT & Local Bus Riders

• Compare the “transit impedance” between alternative and use an elasticity range of -0.3 (low) to -0.7 (high) to estimate “new” riders through alternative progression (No-Build – TSM Baseline – Build) • Assign the resulting transit trip tables to the network for project ridership forecasts • Compare resulting transit impedance to estimate Transit System User Benefits Page 18

Bump in the Road – New PNR Lot Park-and-Ride at Staples Mill (near end of line) – Scale is small (about 100 spaces) – No existing PNR behavior in corridor - PNR’s are in fringe suburban areas feeding express buses to Downtown – Since modest number of spaces, we assumed: • 75% utilized (additional new riders, about 150) • Calculate UB’s by treating these riders as “new” riders (i.e. half the benefit)

Not sophisticated, but how far could we be off? Bigger transformation would require more sophisticated approach.

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Project Extensions • GRTC used on-board survey data to plan their 2015 bus network • Refinements to transit networks/procedures were embedded in the VDOT Richmond/Tri-Cities Forecasting Model update (2012) – Bus speeds – Network coding – Path/assignment weights • On-Board survey used for RTFM mode choice calibration targets • Comprehensive on-board transit survey allowed us to develop procedures to simulate “non traditional” trips: – Drop-off transit trips to non-formal locations – VCU fringe parking trips

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Summary/Wrap-Up •

Small Starts project justification requires forecaster to tell the story of the project “who will benefit and why”



Data driven approach is ideal for developing patronage forecasts for incremental transit service improvements: – – – –

Routed in real traveler patterns, not models Transparent & easy to articulate benefits of projects Easy to describe cause and effect in the forecasts Forecaster can articulate risk by segmenting: • •



What is “known” from the survey data (X riders in the corridor who will save at least 5 min. from the project) What is not “known” (penetration of choice market, given BRT introduction)

FTA was very supportive of approach Page 21

Acknowledgements Virginia Department of Rail & Public Transportation – Amy Inman GRTC – Larry Hagin and Scott Clark VDOT – Rick Tambellini, Juyin Chen and Paul Agnello FTA – Jim Ryan, Ken Cervenka, Nazrul Islam AECOM – Jeff Bruggeman Connectics Transportation Group – Jim Baker Michael Baker – Lorna Parkins, Scudder Wagg Parsons Transportation Group – Gibran Hadj-Chikh VHB - Ram Jagannathan

Project “Graduates” – Chandra Khare, Abishek Komma, Jeremy Raw, Bill Woodford, Mike Lambert, Frank Spielberg and Sharon Hollis

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Thank You!

[email protected] [email protected]

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