Sketch Model to Forecast Heavy-Rail Ridership Len Usvyat 1, Linda Meckel 1, Mary DiCarlantonio 2, 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 tool

Data Collection Transit agency-specific boardings ( ) 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 RegionStations Non-CBD Stations Baltimore1712 Boston5145 Chicago14397 Cleveland1817 Los Angeles1614 Miami2320 New York135 Philadelphia6456 San Francisco4339 Washington, DC8676

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 p<0.05

Heavy-rail station boardings = ,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] * [if there is parking available, 0 if not] + 2,271 * [if there is connection to other rail, 0 if not] * [distance to downtown, in miles] - 2,792 * [ln (midday headway in minutes)] * [CBD density, in employees per square mile] * [employment within 0.25 miles of the station] * [employment within 0.25 to 0.5 miles of station] * [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: r 2 =0.61 Line level: r 2 =0.70 Metropolitan Area level: r 2 =0.81

Applying the Model VariablesMean Standard Deviation Terminal station 11%31% Secondary downtown 12%33% Special transit attractor station 5%21% Distance to downtown, in miles Number of buses connecting 79 Parking available 36%48% Connection to other rail modes 11%31% CBD density (employees per square mile) 132,00038,700 Employment within 0.25 miles 3,8376,716 Population within 0.25 to 0.5 miles 5,0373,778 Employment within 0.25 to 0.5 miles 4,0887,295 Ln(midday headway in minutes) 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!

Questions Len Usvyat