A Comparison of Ridership Response to Incremental BRT Upgrades Considering Land-Use and Network Effects

Anson Stewart January 15th, 2013

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Overview • Incremental BRT in car-centric cities • Pre/post analysis • Direct ridership modeling • Cross-sectional analysis

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BRT – Integrated or Incremental? • “The major components of BRT are planned with the objective of

improving the key attributes of speed, reliability, and identity. Collectively, as an integrated package, they form a complete rapid-transit system with significant customer convenience and transit level of service benefits” (TRB, 2001).

Vs.

• “Incremental development of BRT will often be desirable. Incremental development may provide an early opportunity to demonstrate BRT’s potential benefits to riders, decision makers, and the general public, while still enabling system expansion and possible upgrading.” (TCRP 90, 2003)

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Benefits of BRT Elements

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• TCRP 90 – Bus Rapid Transit – Case Studies and Implementation Guidelines

• TCRP 118 – Bus Rapid Transit Practitioner’s Guide

• Characteristics of BRT for Decision-Making (2009)

• “Quantifying the Benefits of Bus Rapid Transit Elements” (2010)

Research Objective

• Determine which incremental upgrades to conventional bus service most effectively improve productivity and quality in the context of larger more developed cities

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External Factors?

BRT Service Characteristics • Priority lanes • Signal priority • All-door boarding • Increased stop spacing

Ridership and Productivity • Boardings • Boardings per service

hour • Boardings per veh. mile

Performance Indicators • Commercial Speed • Loading • Reliability

Overview • Incremental BRT in car-centric cities • Pre/post analysis • Direct ridership modeling • Cross-sectional analysis

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Pre/Post Analysis • Comparing longitudinal changes • Dependent variable

• Percent increase in ridership • Independent variables

• Percent of corridor with dedicated lanes • Percent of intersections with signal priority • Percent of stops with all-door boarding • Percent increase in speed • Percent increase in stop spacing

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Pre/Post Analysis

City Corridor

Pct Dedicated Lanes Pct TSP

Pct All-door Boarding

Pct Speed Increase

Pct Stop Spacing Increase

Pct Ridership Increase

Miami Busway 1 0 0 0.29 1.79 Orlando Lymmo 1 0 1 0.33 Los Angeles Orange Line 0.93 1 1 0.16 0.51 Boston Washington Street 0.92 0 0 0.09 0.64 0.92 New York M34 SBS 0.67 0.06 1 0.23 0.01 0.31 Eugene EmX 0.65 1 1 0.06 2.52 1.32 Kansas City MAX 0.63 0.89 0 0.25 1.32 0.5 New York M15 SBS 0.62 0.4 1 0.2 0.1 0.12 Cleveland HealthLine 0.62 0 1 0.26 1.24 0.58 Las Vegas North Las Vegas MAX 0.6 0.6 1 0.25 1.69 0.43 New York Bx12 SBS 0.28 0.57 1 0.19 1.40 0.12 Albuquerque Rapid Ride 0.05 0.8 0 0.26 2.48 0.67 Los Angeles Wilshire/Whittier Rapid 0 1 0 0.29 4.60 0.33 Los Angeles Ventura Rapid 0 1 0 0.23 2.23 0.26 Oakland Rapid San Pablo Corridor 0 1 0 0.17 1.42 0.13 San Jose Rapid 522 0 0.44 0 0.2 2.64 0.18

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Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2290 0.1729 1.325 0.2064 Pct.Dedicated.Lanes 0.6067 0.2779 2.183 0.0466 * --- Adjusted R-squared: 0.2006

Avg. 31% Increase

Avg. 54% Increase

Avg. 89% Increase

Percent Ridership Increase vs. Percent Dedicated Lanes

Percent Speed Increase vs. Percent Dedicated Lanes

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R2 = -0.02

Percent Speed Increase vs. Percent Stop Spacing Increase

R2 = -0.03

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Percent Ridership Increase vs. Percent Speed Increase

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Percent Ridership Gain

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.21682 0.31495 0.688 0.50855

Pct.Dedicated.Lanes 0.84899 0.25843 3.285 0.00945 **

Speed.Increase -2.23115 1.01773 -2.192 0.05604 .

Stop.Spacing.Increase 0.21319 0.06806 3.132 0.01208 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Adjusted R-squared: 0.618

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-0.50

0.00

0.50

1.00

WashingtonStreet

Wilshire/WhittierRapid

Ventura Rapid Bx12 SBS M15 SBS

Boston Los Angeles Los Angeles New York New York

% Change in Ridership%Change in Boardings per Service Hour

Ridership and Productivity

Overview • Incremental BRT in car-centric cities • Pre/post analysis • Direct ridership modeling • Cross-sectional analysis

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Stop-level Sketch Planning • TCRP 16 • Lane et al. (2006). “Sketch Models to Forecast Commuter

and Light Rail Ridership” • Stop-level ridership model for 17 US regions

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Direct Ridership Modeling • Cervero (2010)

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Direct Ridership Modeling • Extending stop-level DRM to corridor-level analysis

• Revise binary consideration of right-of-way • Scale branches based on frequency • Consider network-length buffers (“reach” metric)

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Overview • Incremental BRT in car-centric cities • Pre/post analysis • Direct ridership modeling • Cross-sectional analysis

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Cross-Sectional Analysis • Dependent Variables

• Boardings per service hour

• Independent Variables • Percentage of corridor with priority lanes • Percentage of intersections with signal priority • Percentage of stops with all-door boarding • Stop spacing

• Population density along corridor • Auto ownership along corridor • Employment density along corridor

• Transfers from other services/modes

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Land Use, from GIS

Network, from alighting estimation or GTFS Transfer Potential

Land Use

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City Data Year Route Corridor Average Weekday Boardings

Weekday Boardings/ Service Hour

Weekday Boardings/ Service Mile

Weekday Boardings/ Route Mile

Land Area Within 0.5 miles of Stop

Population Density Within 0.5 Miles of Stop

NYC 2011 M15 SBS1st/2nd Ave 33,467 77.9 8.0 86,456 NYC 2009 Bx12 SBSFordham 30,490 94.5 7.4 42,903 NYC 2011 B41 Flatbush 33,948 52.0 9.6 40,628 NYC 2011 Q12 Sanford Ave/Nort 10,571 47.9 5.9 27,186 LOS 2011 754 Vermont 21,275 93.4 14.0 23,244 LOS 2011 204 Vermont 28,032 97.9 14.0 23,244 BOS 2009, 2011 SL4/5 Washington St. 15,086 88.7 12.7 3142.9 3.1 22,241 LOS 2011 720 Wilshire 40,106 60.6 27.4 17,053 LOS 2011 18 Wilshire 24,844 76.2 27.4 17,053 LOS 2011 20 Wilshire 16,630 55.1 27.4 17,053 VAN 2010 B-99 Broadway 57,050 193.8 9.8 14,705 LOS 2011 910 Silver Line 10,423 47.9 11.2 9,779 VAN 2010 99 Broadway 57,050 248.3 14.1 3565.6 7.2 9,601 LOS 2011 901 Orange Line 24,867 81.6 10.7 8,837 MSP 2010, 2009 21 Lake 12,886 58.8 5.9 1451.8 12.9 8,020 MSP 2010, 2009 5 Chicago 16,325 57.6 4.7 1189.0 19.1 6,899 MSP 2010, 2009 10 Central 7,330 43.9 3.4 632.8 16.9 5,020 MSP 2010, 2009 84 Snelling 3,583 38.2 2.4 341.1 12.2 4,934 BOS 2009, 2011 SL1/2 Waterfront 14,940 80.5 10.7 2490.0 3.0 4,432

Network Effects

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Network Effects Hadas (2012): Stop Transfer Potential at the network level

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𝑇 → 𝑋𝑋𝐴

𝑇 → 0.5𝑋𝑋𝐴

Network Effects Scale transfer opportunities according to proportion of corridor trips serving a station

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Transfer Potential - Boston

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Transfer Potential – Los Angeles

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Land Use - Circular Buffer

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Land Use - Street Network Buffer

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A Comparison of Ridership Response to Incremental BRT Upgrades Considering Land-Use and Network Effects

Anson Stewart January 15th, 2013

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