Developing the Transit Demand Index (TDI) Gregory Newmark, Regional Transportation Authority Transport Chicago Presentation July 25, 2012
Outline Present RTA methodology Transit Demand Index (TDI) Demonstrate online application Regional Transit Index Data Viewer www.rtams.org/rtg
How to Assess the Demand for Transit in Chicago s Suburbs?
Methodology Must: 1. Be simple to apply and interpret 2. Rely on available, frequently updated data 3. Scale to different geographies 4. Emphasize planning variables 5. Quantify the demand
Existing Methodologies 1. Density Thresholds 2. Relative Comparison 3. Multiple Regression
Density Thresholds Nelson/Nygaard Consulting Associates
Existing Methodologies 1. Density Thresholds 2. Relative Comparison 3. Multiple Regression
Existing Methodologies 1. Density Thresholds 2. Relative Comparison 3. Multiple Regression
Multiple Regression Example: Housing Price = 90(SQFT) + 40000(#Bedrooms) - 700(Age) Generic Form Y = B 1 X 1 + B 2 X 2 + B 3 X 3 Y = Dependent Variable (What we are predicting) X = Explanatory Variable (What we use to predict) B = Coefficients (How much each variable contributes)
Multiple Regression DVRPC Transit Score
Our Approach Multiple Regression Differentiation in explanatory variables Incorporate all trip types A meaningful dependent variable A mappable dependent variable Transit Trip End Density
CATS Travel Demand Model Transit Trip Tables
CMAP Transportation Analysis Zones
Transit Trip End Density Quantiles Transit Trip Ends / Mile 0 500000 1000000 1500000 2000000 CBD 0 50000 100000 150000 200000 250000 300000 Chicago (exclud 0 5000 10000 Suburbs 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Quantile n = 47 Quantile n = 272 Quantile n = 1,371
How to Assess the Demand for Transit in Chicago s Suburbs?
How to Account for Potential Demand?
The Switch Derive transit demand relationship for city of Chicago ( Chicago Model ) Apply Chicago Model to Suburbs Legend Suburbs Chicago Study Areas Outliers CBD 0 5 10 20 Miles
Outliers Removed Suburbs CBD Legend Study Areas Outliers Chicago 0 5 10 20 Miles
Explanatory Variables Correlation Correlations Ratio Category Variable Chicago Suburbs Suburbs/Chicago Demographic Total Population 0.838 0.690 82% Children (17 and under) 0.703 0.633 90% Adults (18 to 64) 0.854 0.682 80% Seniors (65 and older) 0.642 0.677 105% Workers 0.826 0.682 83% Vehicles 0.786 0.693 88% Total Households 0.848 0.722 85% Households with No Vehicles 0.713 0.600 84% Households with Income <$25k 0.735 0.662 90% Employment Total Employment 0.481 0.497 103% Retail Employment 0.486 0.444 91% Non-Retail Employment 0.442 0.458 104% Total School Enrollment 0.557 0.259 46% Elementary School Enrollment 0.612 0.472 77% High School Enrollment 0.443 0.174 39% Small College Enrollment 0.078 0.104 133% Large College Enrollment 0.202 0.035 17% Urban Form Pedestrian Environment Factor (PEF) 0.720 0.702 98% Regional Employment Context 0.496 0.518 104% 5-Mile Radius Employment Context 0.466 0.500 107%
TDI Transit Demand Index Chicago Model Suburban Model Coefficient Ratio Variable Coefficient Sig. b Coefficient Sig. Suburban/Chicago Demographic Children (17 and under) -0.592 *** -0.380 * 64% Adults (18 to 64) 0.739 *** 0.429 *** 58% Seniors (65 and older) 1.249 *** 1.154 *** 92% Vehicles -0.443 *** -0.174 ** 39% Employment Retail Employment 2.553 *** 0.806 *** 32% Non-Retail Employment 0.344 *** 0.101 *** 29% Model Adjusted R 2 0.955 0.813 F Statistic 857.9 df 6, 238 982.5 df 6, 1349 P Value 0.000 0.000
TDI Transit Demand Index What does the TDI mean?
TDI Transit Demand Index How do we interpret the TDI?
Compare the TSI and the TDI Comparing TSI and TDI in Chicago TSI 0 200 400 600 800 0 5000 10000 15000 20000 TDI
TDI = TSI / 0.043 TDI Thresholds Levels TDI Exemplar Bus Weekday Bus Operations Trips Service Span Headways (minutes) (minutes) Low 558 304 North Riverside - LaGrange 24 695 58 Medium 1,465 305 Cicero - River Forest 63 1,010 32 High 3,837 352 Halstead 165 1,215 15 TDI Thresholds correspond to transit service levels
Application Scaled the TDI to the subzone for suburbs 2010 Census Data 2010 CMAP Employment Data 2012 DMV Data Put the data online in an interactive map www.rtams.org/rtg
Innovations Transit trip end density as a measure Consideration of potential demand Explanatory variable differentiation
Limitations Modeled, not actual data used for regression Several social factors not included Only considers walk access to transit Relies on current transit conditions in Chicago as the upper bound
Future Steps Incorporate drive / park and ride access Apply at the subzone level Employ in planning studies (e.g. I-90) Thank you for listening We welcome your feedback!