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LT4: A comparison of ridership response to incremental BRT upgrades considering land use and network effects
 

LT4: A comparison of ridership response to incremental BRT upgrades considering land use and network effects

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    LT4: A comparison of ridership response to incremental BRT upgrades considering land use and network effects LT4: A comparison of ridership response to incremental BRT upgrades considering land use and network effects Presentation Transcript

    • 1A Comparison of Ridership Responseto Incremental BRT UpgradesConsidering Land-Use and Network EffectsAnson StewartJanuary 15th, 2013
    • 2Overview• Incremental BRT in car-centric cities• Pre/post analysis• Direct ridership modeling• Cross-sectional analysis
    • 3BRT – 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)
    • 4Benefits of BRT Elements• 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)
    • 5Research ObjectiveBRT Service PerformanceCharacteristics Indicators• Priority lanes • Commercial Speed• Signal priority • Loading• All-door boarding • Reliability• Increased stop spacing External Factors? Ridership and Productivity • Boardings • Boardings per service hour • Boardings per veh. mile• Determine which incremental upgrades to conventional bus service most effectively improve productivity and quality in the context of larger more developed cities
    • 6Overview• Incremental BRT in car-centric cities• Pre/post analysis• Direct ridership modeling• Cross-sectional analysis
    • 7Pre/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
    • 8 Pre/Post Analysis Pct Pct All- Pct Stop Dedicated door Pct Speed Spacing Pct RidershipCity Corridor Lanes Pct TSP Boarding Increase Increase IncreaseMiami Busway 1 0 0 0.29 1.79Orlando Lymmo 1 0 1 0.33Los Angeles Orange Line 0.93 1 1 0.16 0.51Boston Washington Street 0.92 0 0 0.09 0.64 0.92New York M34 SBS 0.67 0.06 1 0.23 0.01 0.31Eugene EmX 0.65 1 1 0.06 2.52 1.32Kansas City MAX 0.63 0.89 0 0.25 1.32 0.5New York M15 SBS 0.62 0.4 1 0.2 0.1 0.12Cleveland HealthLine 0.62 0 1 0.26 1.24 0.58Las Vegas North Las Vegas MAX 0.6 0.6 1 0.25 1.69 0.43New York Bx12 SBS 0.28 0.57 1 0.19 1.40 0.12Albuquerque Rapid Ride 0.05 0.8 0 0.26 2.48 0.67Los Angeles Wilshire/Whittier Rapid 0 1 0 0.29 4.60 0.33Los Angeles Ventura Rapid 0 1 0 0.23 2.23 0.26Oakland Rapid San Pablo Corridor 0 1 0 0.17 1.42 0.13San Jose Rapid 522 0 0.44 0 0.2 2.64 0.18
    • 9Percent Ridership Increase vs. Percent Dedicated Lanes Avg. 89% Increase Avg. 54% Increase Avg. 31% Increase 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
    • 10Percent Speed Increase vs. Percent Dedicated LanesR2 = -0.02
    • 11Percent Speed Increase vs. Percent Stop Spacing IncreaseR2 = -0.03
    • 12Percent Ridership Increase vs. Percent Speed Increase
    • 13Percent Ridership GainCoefficients: Estimate Std. Error t value Pr(>|t|)(Intercept) 0.21682 0.31495 0.688 0.50855Pct.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 ‘ ’ 1Adjusted R-squared: 0.618
    • 14 Ridership and Productivity1.000.50 % Change in Ridership %Change in Boardings per Service Hour0.00 Washington Wilshire/Whittier Ventura Rapid Bx12 SBS M15 SBS Street Rapid Boston Los Angeles Los Angeles New York New York-0.50
    • 15Overview• Incremental BRT in car-centric cities• Pre/post analysis• Direct ridership modeling• Cross-sectional analysis
    • 16Stop-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
    • 17Direct Ridership Modeling• Cervero (2010)
    • 18Direct 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)
    • 19Overview• Incremental BRT in car-centric cities• Pre/post analysis• Direct ridership modeling• Cross-sectional analysis
    • 20Cross-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 Land Use, from GIS • Employment density along corridor Network, from alighting estimation • Transfers from other services/modes or GTFS Transfer Potential
    • 21 Land Use Average Weekday Weekday Boardings/ Weekday Boardings/ Weekday Boardings/ Land Area Within Population DensityCity Data Year Route Corridor Boardings Service Hour Service Mile Route Mile 0.5 miles of Stop Within 0.5 Miles of StopNYC 2011 M15 SBS1st/2nd Ave 33,467 77.9 8.0 86,456NYC 2009 Bx12 SBSFordham 30,490 94.5 7.4 42,903NYC 2011 B41 Flatbush 33,948 52.0 9.6 40,628NYC 2011 Q12 Sanford Ave/Nort 10,571 47.9 5.9 27,186LOS 2011 754 Vermont 21,275 93.4 14.0 23,244LOS 2011 204 Vermont 28,032 97.9 14.0 23,244BOS 2009, 2011 SL4/5 Washington St. 15,086 88.7 12.7 3142.9 3.1 22,241LOS 2011 720 Wilshire 40,106 60.6 27.4 17,053LOS 2011 18 Wilshire 24,844 76.2 27.4 17,053LOS 2011 20 Wilshire 16,630 55.1 27.4 17,053VAN 2010 B-99 Broadway 57,050 193.8 9.8 14,705LOS 2011 910 Silver Line 10,423 47.9 11.2 9,779VAN 2010 99 Broadway 57,050 248.3 14.1 3565.6 7.2 9,601LOS 2011 901 Orange Line 24,867 81.6 10.7 8,837MSP 2010, 2009 21 Lake 12,886 58.8 5.9 1451.8 12.9 8,020MSP 2010, 2009 5 Chicago 16,325 57.6 4.7 1189.0 19.1 6,899MSP 2010, 2009 10 Central 7,330 43.9 3.4 632.8 16.9 5,020MSP 2010, 2009 84 Snelling 3,583 38.2 2.4 341.1 12.2 4,934BOS 2009, 2011 SL1/2 Waterfront 14,940 80.5 10.7 2490.0 3.0 4,432
    • 22Network Effects
    • 23Network EffectsHadas (2012): Stop Transfer Potential at the network level 𝑇 → 𝑋𝑋 𝐴 𝑇 → 0.5𝑋𝑋 𝐴
    • 24Network EffectsScale transfer opportunities according to proportion of corridor tripsserving a station
    • 25Transfer Potential - Boston
    • 26Transfer Potential – Los Angeles
    • 27Land Use - Circular Buffer
    • 28Land Use - Street Network Buffer
    • 29A Comparison of Ridership Responseto Incremental BRT UpgradesConsidering Land-Use and Network EffectsAnson StewartJanuary 15th, 2013