sketch model to forecast heavy-rail ridership len usvyat 1, linda meckel 1, mary dicarlantonio 2,...
TRANSCRIPT
Sketch Modelto Forecast
Heavy-Rail Ridership
Len Usvyat1, Linda Meckel1, Mary DiCarlantonio2, Clayton Lane
1 – PB Americas, Inc.2 – Jeffrey Parker & Associates, Inc.
Research Goals
• Ridership estimation is key to new transit projects
• Catch-22 of cost versus outcome:– Four-step models are difficult and
expensive– Outcome may prove project unfeasible
• Agencies need an easy-to-apply, inexpensive sketch level tool
This Tool versus Others
• Geared to heavy-rail stations outside of downtown
• Computer software needed to calculate the results:– MS Excel– ArcGIS (not required)
• Other tools our team developed:– Light Rail model– Commuter Rail model
What else is out there?
• Pushkarev & Zupan: densities for various transit modes
• Cervero: drawbacks of four-step model and importance of sketch-level tools
• The FDOT TBEST model: patronage by stop• TCRP Report 16: commuter & light rail
models• Update to TCRP Report 16 published in
2006: models for commuter and light rail• ARRF: FTA suggested tool0
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RESIDENTIAL DENSITY(Dwelling Units per Acre)
Local (M etro) Bus
Frequent S ervice
M oderate Service
H ourly S ervice
Express Bus (BRT)
Rail Transit
W alk A ccessA uto Access
L ight R a il TransitH eavy R ail Trans itC om m uter R a ilroad
Data Collection
• Transit agency-specific boardings (2004-2006) for all U.S. heavy-rail transit stations (N=474)
• MPO data for demographic data (same year)
• U.S. CTPP data for the year 2000, where no MPO data is available
Heavy-Rail Systems Included
• 10 cities• 11 systems (NYC
Subway excluded)• 32 heavy rail lines• 474 stations
Region Stations
Non-CBD
Stations
Baltimore 17 12
Boston 51 45
Chicago 143 97
Cleveland 18 17
Los Angeles 16 14
Miami 23 20
New York 13 5
Philadelphia 64 56
San Francisco 43 39
Washington, DC 86 76
Variables Analyzed
• Station area demographics• Station specific transportation
attributes• Corridor demographic characteristics• Metro area demographic and
transportation attributes
Station Area Demographics
• 1/4, 1/2, 1, and 2-mile radii around each station:– Employment (all jobs)– Population and population 16+ years old– Population divided by employment– Average household size– Median household income– Average number of vehicles per household– Zero-car households, households with cars– Zero-car households divided by households
with cars– One-car households, two-car households,
three-car plus households
Station Specific Transportation Attributes
• Whether a station is a secondary downtown (yes or no)• Number of buses connecting to the station • Connection to other rail system (yes or no)• Number of rail lines connected to this station• Availability of parking (yes or no)• Whether it is a terminal station (yes or no)• Indication if the station is a special transit attractor• Cash and commuter fare to downtown (yes or no)• AM, PM, Midday peak and weekend headway (minutes)• Hours transit system is operated per 24-hour period• Distance, time, and speed to primary & secondary
downtown• Distance to nearest station (miles)
Corridor Demographic Characteristics
• Zero-car households divided by households with cars
• Households with cars along the corridor• Zero-car households along the corridor• Total employment along corridor• Total population along corridor• Total employment divided by total
population along the corridor
Metro Area Demographic and Transportation Attributes
• Central Business District (defined in by Lane) – CBD area, ratio of CBD area to total metro area– CBD employment and employment density– CBD employment divided by total metro area
employment
• Metropolitan area (as defined by MPO)– Metro area– Metro employment – Metro population– Median household income in the metro area – Vehicles per household in the metro area
Analysis
• Variable review– Continuous– Discrete– Categorical
• Normality of independent and dependent variables (natural log)
• Correlation coefficients• Multiple linear regression
How to define a CBD?
• Log of employment density (“ED”) by TAZ
• Mean and standard deviation of ED for the entire region
• Map TAZs whose ED is at least 1.5 and 2 standard deviations above the mean
• “CBD area”: contiguous TAZs whose ED is 2 and 1.5 standard deviations above the mean
Station Area Coverage
• Non-exclusive versus exclusive station areas
• Non-overlapping radii do not double count boarding drivers
• Correlation is significantly improved
Station Area “Donuts”
• Variation in density around the station and boardings
• Correlations with boardings
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Heavy-rail station boardings = -972+ 1,625 * [if this is a terminal station, 0 if not]+ 1,346 * [if this is a secondary downtown, 0 if not]+ 1,710 * [if this is a special attractor station, 0 if not]+ 70 * [number of buses connecting to this station]+ 884 * [if there is parking available, 0 if not]+ 2,271 * [if there is connection to other rail, 0 if not]+ 115 * [distance to downtown, in miles]- 2,792 * [ln (midday headway in minutes)]+ 0.024 * [CBD density, in employees per square mile]+ 0.224 * [employment within 0.25 miles of the station]+ 0.133 * [employment within 0.25 to 0.5 miles of
station]+ 0.219 * [population within 0.25 to 0.5 miles of station]+ 5,938 * [empl within 2 miles of entire line div by pop]
Results
• Station level: r2=0.61
• Line level: r2=0.70
• Metropolitan Area level: r2=0.81
Applying the ModelVariables Mean
Standard Deviation
Terminal station 11% 31%
Secondary downtown 12% 33%
Special transit attractor station 5% 21%
Distance to downtown, in miles 5.9 4.8
Number of buses connecting 7 9
Parking available 36% 48%
Connection to other rail modes 11% 31%
CBD density (employees per square mile) 132,000 38,700
Employment within 0.25 miles 3,837 6,716
Population within 0.25 to 0.5 miles 5,037 3,778
Employment within 0.25 to 0.5 miles 4,088 7,295
Ln(midday headway in minutes) 2.3 0.35
Empl within 2 miles of entire line div by pop 74% 20%
Next Steps
• Census tracts• Downtown station ridership• Age of the system (time dependent
analysis)• Size of parking lots• Other station characteristics (elevators,
cleanliness, station attendants, underground/aboveground)
• Time of day analysis• Taking transfers into account• Buses versus bus routes
Conclusion
• Follow the application guidance– Calibrating the model– Means and standard deviation– Urban character is crucial– Linear model drawbacks
• New tools from FTA or other agencies• LRT and Commuter Rail models are
available in TRR 1986• Try it out and let us know your
outcomes!