presented to
presented by Cambridge Systematics, Inc.
Transportation leadership you can trust.
Special Events Travel Surveys and Model Development
2012 Innovations in Travel Modeling Conference
May 2, 2012
Arun Kuppam, Rachel Copperman, Jason Lemp, Tom Rossi, Lavanya Vallabhaneni, Vladimir Livshits, Ted Brown, Kyunghwi Jeon
Heavy utilization of LRT by special events patrons
Background
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LRT intercept survey indicated non-commute trips during off-peak hours and weekends
MAG travel model does not account for weekend and special events travel
LRT Ridership numbers exceeded forecasts
New LRT opened in early 2009 in Phoenix
Project supported by FTA 5339 funds
Project Objectives
3
Integrate SEM with FTA’s Summit Develop Stand
alone Special Events Model (SEM)
Conduct Special Event Surveys
Special Event Classification
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Concerts, Ballgames Predicted Attendance
Pro / College Sports Event Frequency
Weekly vs. Occasional Regular vs. Periodic
Stadium, Theatre, Street Venue Type
Set start/end vs. Continuous Event Start & End Time
State Fairs Single vs. Multiple Days
Weekdays, Saturdays, Sundays Day-of-Week
Residents vs. Visitors Event Market Area
Proximity to Events Local vs. Regional Attendance
9 Distinguishing Characteristics of Special Events
Surveyed Events
• Continuous, Open arena Arizona Fall Frenzy
• Predicted attendance, Stadium
Diamondbacks game
• Continuous, Multiple days
Arizona State Fair
• Periodic event, Regional AFL Rising Stars Game
• Regular, Visitors ASU Football Game
• Occasional, Closed arena KISS Concert
• Predicted attendance, Visitors Cardinals Game
• Street event Mill Avenue Block Party
• Street, Unknown attendance
PF Chang’s Marathon
• Periodic, Multiple days FBR - WM Golf Open
• Regular, Visitors ASU Basketball Game
• Predicted attendance, Closed arena
NBA Phoenix Suns Game
• Periodic, Stadium Spring Training Game
• Occasional Wrestlemania
• Local, Street Pride Parade
• Local, Closed arena Crossroads of the West Gun Show
• Theatre, Predicted attendance
Conan O’Brien Show
• Street, Local First Friday
• Predicted attendance, Stadium
Diamondbacks game
• Predicted attendance, Closed arena
NBA Phoenix Suns Game
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20 Special Events chosen from more than 300 Special Events
Surveyed Events
6
Survey Data Collection
Partnered with West Group Research
who conducted the survey
Targeted 100-600 surveys per event, or 6,100 total target
Collected 7,264 surveys, out of
which 5,943 were useable/completed
surveys
Collected counts by Gate and Time Period (for data
expansion)
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Target and Completed Surveys
8
Event Venue Capacity
Previous Attendance
Actual Attendance
Target Surveys
Confidence Level
Margin of Error
Completed Surveys
1 Fall Frenzy Music Festival (Pretest)
40,000 4,000-15,000 15,000 100 95.0% 9.80% 123
2 MLB Diamondbacks 49,033 20,000-49,033 30,018 300 95.0% 5.66% 295
3a Arizona State Fair – Thursday
N/A 1,303,690 (two-week
period in 2006)
16,911 167 95.0% 4.38% 227
3b Arizona State Fair – Saturday 32,800 333 95.0% 4.38% 374
4 AFL Rising Stars 10,500 4,000-6,000 4,550 100 95.0% 9.80% 86
5 ASU Football Game 73,379 20,000-60,000 55,989 500 95.0% 4.38% 392
6 KISS Concert 17,799 8,000-17,799 10,876 300 95.0% 5.66% 329
7 NFL Cardinals 63,400 40,000-63,400 64,121 500 95.0% 4.40% 448
8 Mill Avenue Block Party N/A 100,000+ 100,000 500 95.0% 4.38% 571
9 PF Chang’s Marathon N/A 28,000+
Participants 102,556 500 95.0% 4.38% 481
10 FBR – WM Golf Open N/A 538,356 (2008) 122,000 500 95.0% 4.38% 584
Target and Completed Surveys
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Event Venue Capacity
Previous Attendance
Actual Attendance
Target Surveys
Confidence Level
Margin of Error
Completed Surveys
11 ASU Basketball Game
14,198 13,000 9,040 100 95.0% 9.80% 123
12 NBA Phoenix Suns 18,422 8,000-
16,210 18,422 300 95.0% 5.66% 295
13 MLB Spring Training 9,600 9,600 8,854 100 95.0% 9.80% 227
14 Wrestlemania 72,200 72,047 (2008) 72,219 500 95.0% 4.38% 374
15 Pride Parade N/A 12,500 12,000 100 95.0% 9.80% 86
16 Crossroads of the West Gun Show
N/A 9,000- 12,000 5,000 100 95.0% 9.80% 392
17 Conan O’Brien Prohibited Tour 5,500 100-
2,587 5,500 200 95.0% 9.80% 329
18 First Fridays N/A Unknown 10,000 300 95.0% 5.66% 448
19 MLB Diamondbacks 67,455 20,000-
49,033 23,148 300 95.0% 5.66% 571
20 NBA Phoenix Suns 18,422 8,000-
16,210 18,422 300 95.0% 5.66% 481
Q Survey Questions
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Location (gate) and time of interview
Origin location before event • Home, work, hotel, other • Address or intersection
Departure time from origin location
Mode of travel to/from event
Access mode to/from transit
Length of stay at the event
Parking cost and location (blocks/minutes from event) for auto modes
Party size to and from event
Destination location after event • Home, work, hotel, other • Address or intersection
Socioeconomic data (household size, vehicles, income, gender, employment)
Data Expansion Survey
Expansion and Weights
Collected counts
by Gate, Time period
Weighted data by Gate, Time
period, and Party size
Expanded to total
attendance at event
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Party Size Number
of Surveys
Weighted Survey
Numbers
Percentage of Weighted
Survey
Expansion Factor
for Each Party Size
Expansion Factor for
Each Survey 1 2 2 0.07 10.53 5.26
(1x2) (2/28.5) (0.07x150) (10.53/2)
2 2 4 0.14 21.05 10.53 3 3 9 0.32 47.37 15.79 4.5 3 13.5 0.47 71.05 23.68
28.5 150
Example: 10 surveys, 150 attendees
Special Event Modeling Framework
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Trip Generation Size of Event
Model Step Factor
Mode Choice
Trip Assignment
Modes (DA, SR2, SR3+, LRT, Bus, NM)
Day of Week (Weekday/ Weekend)
Time of Day (Peak/Off-Peak)
Trip Distribution
Origin Location Type (Home, Hotel, Work/Other)
Attendance or Venue Capacity
Inputs
Skims, Sociodemo-
graphics
Highway and Transit Networks
Logsums, Employment
User Defined
Model
Origin Choice Model (MNL)
by Origin Location Type
Mode Choice Model (NL) by Origin
Location Type
Multiclass Assignments (Stand-Alone or Integrated)
Outputs
Same as Inputs
Origin-to-Special Event Person Trip Tables by Origin Location Type
Origin-to-Special Event Person Trip
Tables by Mode for Every
Origin Location Type
Auto Volumes by Mode; LRT Ridership
by Station and Time of Day
Special Event Model Objectives
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Predict for Each Special Event – By Location Type (Home-, Hotel, Work/Other-Based)
Number of trips
Time-of-Day of trips
Origins of trips
Mode choice of
trips
VMT generated
from special
event trips
Trip Generation
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Model Overview
Base Year
Forecast Year
Predicts the number of person trips traveling to and from special events
Person trips = attendance at the event
Person trips = minimum { Base Year Attendance * Growth Rate, Venue Capacity }
Time-of-Day
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Set Start and End Time Events
Continuous Start and End Time Events
Time-of-Day Aggregated to Four Time Periods
• Arrival time distributed between 0-3 hours before event start time, and up to 0.5 hours after start time
• Departure time distributed between 0-1 hour before event end time, and up to 0.5 hours after end time
• Arrival time is distributed uniformly between the event start time and 3 hours before the event end time
• Departure time is determined based on arrival time and event duration with all event attendees leaving at or before the event end time
AM Peak, Mid-Day, PM Peak, Night
Trip Distribution Origin Choice Model
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Trip Distribution Model
Destination (or Origin) Choice Models
Model Structure
Size Measures
Utility Measures
Predicts origin choice of trips to special event by location type
Three models were estimated – • Home • Hotel • Work and other
Multinomial logit
• Home: number of HBNW trips produced in a zone (from regular travel model)
• Hotel: hotel employment • Work and other: HBW attractions
(from regular travel model)
• Distance from TAZ to event • Land use at origin • Mode choice logsum
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V a r i a b l e Home-Based Hotel-Based Work/Other-Based
S i z e V a r i a b l e s Ln (HBNW Productions) – Low Income 1.00 Ln (HBNW Productions) – Middle Income 1.00 Ln (HBNW Productions) – High Income 1.00 Ln (Hotel Employment) 1.00 Ln (HBW Attractions) – Work 1.00 Ln (Total Attractions) – Other 1.00
D i s t a n c e V a r i a b l e s Distance (miles) -0.126 -0.0806 -0.183 Distance^2 0.00393 Distance^3 -0.00005 Max (Distance – 8, 0) 0.193
L a n d U s e V a r i a b l e s Retail Employment at Origin 0.000152 CBD Origin 0.476 Suburban Origin -0.78 CBD Origin – Work 0.301 CBD Origin – Middle/High Income -0.173 Urban/Suburban Origin – Other 0.308 Urban Origin – Low Income 0.374
M o d e C h o i c e L o g s u m s Mode Choice Logsum – 0-Vehicles 0.093 Mode Choice Logsum – Low Income, 1+ Vehicle 0.102 Mode Choice Logsum – Middle Income, 1+ Vehicle 0.129 Mode Choice Logsum – High Income, 1+ Vehicle 0.186 Mode Choice Logsum 0.732 Mode Choice Logsum – Work/Other 0.834
Origin Choice Model Coefficients
Trip Distribution Distance
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 5 10 15 20 25 30 35 40 45 50 Origin to Event TAZ Distance (miles)
Home Origin Hotel Origin Work/Other Origin
Origin Choice Utility
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Mode Choice Nesting Structure
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Root
Auto
Drive Alone
Shared Ride-2
Shared Ride-3+
Transit
Walk Access
Light Rail Bus
Drive Access
Light Rail Bus
Walk/ Bike
Mode Choice Coefficients
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• Constrained to 0.6 for second-level nest • Constrained to 0.4 for third-level nest
Nesting Coefficients
• Constrained to VOT of $5.00 and OVTT = 2 x IVTT • Cost ($): -0.018 • IVTT (minutes): -0.015 • OVTT (minutes): -0.03
• Nonmotorized: Unconstrained distance coefficient • Distance: -0.249 (-13.515)
Level-of-Service Coefficients
• Income • Vehicle Availability
Socioeconomic Variables
• CBD origin • Work-based
Land Use and Origin Choice
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0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00
10.00
Mod
e C
hoic
e U
tility
Impact of “Event Type” on LRT Share
LRT-D LRT-W
Mode Choice Event Type Variables
Trip Assignment
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Output from SEM are person trip tables for
each mode and time-of-day
Converted to vehicle trips and added to trip assignment in
regional model
Model Calibration/Validation
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Calibration/Validation Data • Special Events Survey data • Transit Boarding counts (Special
event day counts – Non-special event day counts)
Mostly focused on trip lengths and mode shares
Calibration of Origin Choice Models
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Home – Origin
0.00 0.05 0.10 0.15 0.20 0.25
0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50+ Distance (mi)
Observed Original Model Calibrated Model
Coincidence Ratio – Original Model = 0.925 Coincidence Ratio – Calibrated Model = 0.937
Frequency (in Percent)
0.00 0.05 0.10 0.15 0.20 0.25
0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50+ Distance (mi)
Observed Original Model Calibrated Model
Coincidence Ratio – Original Model = 0.782 Coincidence Ratio – Calibrated Model = 0.772
Frequency (in Percent) Work – Origin
Other – Origin
Hotel – Origin
Calibration of Origin Choice Models (continued)
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0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50+ Distance (mi)
Observed Original Model Calibrated Model
Coincidence Ratio – Original Model = 0.760 Coincidence Ratio – Calibrated Model = 0.762
Frequency (in Percent)
0.00 0.05 0.10 0.15 0.20 0.25
0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50+ Distance (mi)
Observed Original Model Calibrated Model
Coincidence Ratio – Original Model = 0.810 Coincidence Ratio – Calibrated Model = 0.828
Frequency (in Percent)
Calibration of Mode Choice
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Mode Observed –
Transit Count Adjusted Calibrated Model Drive Alone 6.8% 7.1% Shared Ride 2 27.4% 28.0% Shared Ride 3+ 58.6% 57.6% Bus – Drive 0.2% 0.3% Bus – Walk 0.8% 0.9% LRT – Drive 1.9% 1.9% LRT – Walk 1.1% 1.0% Nonmotorized 3.2% 3.2%
• Weekend versus weekday events • Sports events (Pro versus nonpro sports) versus nonsports events • Events with attendance <= 10,000 versus > 10,000 • Tempe versus non-Tempe special events • Set time versus continuous events
Calibration Done by Special Event “Types”
Calibration of Mode Choice – Auto Modes
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0%
10%
20%
30%
40%
50%
60%
Attendance <10k Pro Sports All Sports; > 10K
DA SR2 SR3+
Highest Mode Shares
0% 5%
10% 15% 20% 25% 30% 35% 40%
Pro Sports; Set Times Tempe < 10k
DA SR2 SR3+
Lowest Mode Shares
Calibration of Mode Choice – Transit & Non-motorized Modes
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0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00%
Tempe Tempe Weekday; Tempe
Weekday; Tempe
Tempe
Bus-d Bus-w LRT-d LRT-w NM
Highest Mode Shares
0.00%
0.50%
1.00%
1.50%
2.00%
Pro Sports Pro Sports Not Tempe Not Tempe Pro Sports; Not Tempe
Bus-d Bus-w LRT-d LRT-w NM
Lowest Mode Shares
Integration with SUMMIT
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•Base Scenario: Without LRT •Alternative Scenario: With LRT
Created two scenarios to compute change in transit shares and evaluate transit
user-benefits
Trips
MLB Diamondbacks Event NBA Suns Event
Base Alternate % Increase Base Alternate % Increase Total Trips 41,234 41,234 0.0% 32,566 32,566 0.0%
Transit Trips 941 2,285 142.8% 653 1,458 123.3%
Transit-Dependent Transit Trips
110 199 80.9% 11 33 200.0%
User Benefits (in Hours) MLB Diamondbacks Event NBA Suns Event Total 1,170 699 Auto -13 -11 Transit 1,183 710
Conclusions
Special events trips constitute a large share of LRT ridership
Gate counts by time period very critical in developing expansion factors
SEM, designed as a stand-alone model, easily integrated with regional model
SEM predicts special event trips for any future event or groups of events
SEM, integrated with SUMMIT, can be used to evaluate transit benefits
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