CTPP Webinar and Discussion Thursday, July 17, 1-3pm EDT Developing and Validating Regional Travel Forecasting Models with CTPP Data: MAG Experience Kyunghwi Jeon, MAG Petya Maneva, MAG Vladimir Livshits, MAG Acknowledgements: Ken Cervenka, FTA 1
ACS Data Including CTPP Products and PUMS Data Were Used in MAG Model Development and Validation: Trip Generation Model Development Destination Choice Model Development Auto Ownership Model Development Model Validations 2
Focus of this Presentation is on Comparing Commuter Flows between the MAG Model and CTPP 5-year estimates CTPP 26-21 American Community Survey (ACS) 5-year estimates are PERIOD estimates MAG Model Socioeconomic data is based on a single point in time estimate ACS estimates are controlled to the Census Bureau s annual population estimates over the 5 year period 3
MAG Modeling Area: Where We Are Area: 16 thousand square miles 211 Population: 4.5 million people 211 Employment: 1.8 million jobs Traffic Analysis Zones: 3,9 4
MAG 211 Socio-Economic Projections VS. CTPP 5-year Estimates: Not Directly Comparable but Comparison of Distributions of Commuter Flows on an Aggregate Level is still of Interest Population 211 MAG Model 26-21 CTPP Difference Perc. Difference Maricopa 4,14,542 3,751,445 353,97 9% Pinal 395,954 329,26 66,694 2% Total 4,5,496 4,8,75 419,791 1% Employment 211 MAG Model 26-21 CTPP Difference Perc. Difference Maricopa 1,744,641 1,442,481 32,16 21% Pinal 59,313 69,879-1,566-15% Total 1,83,954 1,512,36 291,594 19% 5
Seasonal and Transient Populations Are Included in MAG Projections but Are not Reflected in CTPP Data 211 MAG Model Population Categories: Residential Group Quarters Seasonal Transient 26-21 ACS Database Population Categories: Residential Group Quarters Other Differences in Employment Calculations 6
Socio-Economic data is not directly comparable between CTPP estimates and MAG annual forecast We can t compare absolute trip numbers and need to look at comparison of proportional distribution of travel between CTPP and MAG model. 7
Biggest Population Difference is on the Edge of the Region in Pinal County Proportion of Trips Going To/From this District Does Not Affect Substantially Other Trip Interchanges 8
Pinal County is the Only District with Higher CTPP Employment Estimates than MAG Data 9
How we Delineated Districts: the Starting Point Initial districts (32) that were used to reweight 28 NHTS - Homogenous median household income for the district s block groups - No less than 1 households surveyed in NHTS 1
Income Distribution as a Criterion 11
How We Delineated Districts Previous MAG experience showed that in order to facilitate manual analysis of the largest travel patterns in the region 1 to 15 districts are most appropriate. Other factors to consider: - Average trip length for HBW trips: 12.7 miles - Margins of error in CTPP data sets - Land use and socio-economic data 12
Analysis of Largest Desire Lines and Spider Networks can Help to Determine Number and Configuration of Districts 13
How to Draw Districts Somewhat Similar to Delineating TAZ and Creating Screenlines Starting point can be districts created for another purpose Look for socio-economic homogeneity inside a district but ensure that level of aggregation is sufficient for meaningful comparison Let freeways, jurisdictional limits and major physical barriers serve as boundaries as appropriate Exclude peripheral zones/districts A few iterations might be required 14
Sequence of District Delineation 15
Defining Districts: Conforming to Jurisdictional Boundaries and Freeways 16
Aggregation of TAZs to Districts Model: Select contiguous TAZs in Agency s dataset Aggregate sets of TAZs to districts CTPP: Generate centroids point for CTTP TAZs Overlay CTPP centroids with district polygons Assign whole CTTP TAZs to districts Map both sets of districts for quality assurance 17
Final District System Excluded from comparisons because of lack of spatial overlap between the district and the MAG modeling Area Excluded from comparisons because of lack of spatial overlap between the district and the MAG modeling Area 18
Creating Tables for Comparison 211 MAG Model PA person trip tables are used pk_hbw_aggregated + op_hbw_aggregated 2 26-21 CTPP Database CTPP Flows - TAZ-to-TAZ home-to-work trips, aggregated to district level 19
Modeled District-to-District HBW Trips 211 MAG Model District Total 1 6,153 1,53 99 99 2,613 4,417 7,837 3,152 145 5 43 1 27,31 2 363 34,427 4,68 19,735 21,586 17,64 5,743 379 68 58 432 6 16,252 3 6 3,246 15,313 24,79 6,998 1,669 187 22 1,438 4,183 1,772 1 59,724 4 13 3,57 7,816 9,446 41,687 6,89 328 92 7,718 4,782 3,971 56 166,57 5 35 11,347 3,132 41,59 91,778 21,56 966 23 5,695 1,44 1,562 37 179,226 6 289 12,635 1,913 28,39 91,968 115,19 11,84 2,429 4,974 1,325 1,391 27 272,21 7 2,145 7,344 783 6,435 15,671 31,498 38,934 4,63 85 272 239 4 18,733 8 1,385 327 75 2,452 7,694 11,144 5,658 17,637 593 17 159 15 47,39 9 4 12 614 15,15 15,67 2,198 7 47 32,756 2,953 7,84 98 77,351 1 4 322 4,24 27,197 14,64 1,84 59 35 9,129 5,427 27,77 1,83 135,981 11 6 326 2,481 28,97 21,146 3,131 94 61 3,881 27,785 94,544 1,92 21,471 12 2 81 793 7,478 6,98 1,275 34 147 11,49 1,66 23,137 45,778 17,261 Total 1,44 75,275 41,724 292,534 337,856 217,481 71,749 28,87 16,34 13,996 162,133 49,847 1,498,11 2
CTPP District-to-District Flows 26-21 CTPP Data District Total 1 5,351 874 176 953 3,15 3,718 5,47 1,553 229 284 338 7 21,698 2 278 31,744 3,335 16,4 2,948 17,194 4,543 928 92 1,242 1,234 183 98,553 3 5 2,848 16,52 14,284 6,264 1,685 52 1 1,49 2,278 2,242 15 48,197 4 185 6,64 7,23 87,48 38,73 11,234 1,751 594 6,891 5,62 5,911 515 172,196 5 294 12,84 4,152 38,918 92,122 27,521 3,563 1,143 5,426 3,172 3,958 417 192,77 6 726 14,168 2,971 35,932 85,929 115,116 16,127 4,793 5,68 5,397 6,811 817 294,395 7 1,533 5,523 872 7,998 19,178 28,761 38,17 4,584 1,26 1,25 1,846 159 11,747 8 957 966 347 2,926 7,496 9,189 5,38 15,612 354 71 68 237 44,773 9 4 1,34 1,16 16,64 15,281 3,795 73 516 27,376 3,51 7,786 1,27 78,751 1 22 1,745 3,592 17,22 13,213 4,872 1,271 581 5,835 55,941 21,368 2,82 128,66 11 395 2,217 3,277 26,36 23,349 6,797 1,654 698 21,537 25,6 84,988 4,817 21,95 12 4 755 666 5,778 5,812 2,145 443 523 4,572 8,576 13,22 55,996 98,526 Total 1,69 8,598 44,127 27,475 331,427 232,27 78,956 31,625 81,183 112,419 15,382 67,73 1,49,361 21
Difference in Number of Trips 211 MAG Model 26-21 CTPP Data District Total 1 82 656-77 37-492 699 2,79 1,599-84 -234-295 -69 5,333 2 85 2,683 1,345 3,731 638 446 1,2-549 -24-662 -82-177 7,699 3-44 398-1,27 1,56 734-16 -315-78 29 1,95-47 85 11,527 4-172 -3,7 613 2,966 2,957-5,145-1,423-52 827-28 -1,94-459 -5,626 5-259 -737-1,2 2,591-344 -5,961-2,597-94 269-1,768-2,396-38 -13,544 6-437 -1,533-1,58-7,542 6,39-97 -4,287-2,364-634 -4,72-5,42-79 -22,194 7 612 1,821-89 -1,563-3,57 2,737 917 19-221 -978-1,67-155 -2,14 8 428-639 -272-474 198 1,955 35 2,25 239-531 -521-222 2,536 9-36 -914-42 -1,625 389-1,597-66 -469 5,38-557 18-929 -1,4 1-216 -1,423 432 9,995 851-3,32-1,212-546 3,294-5,514 5,79-1,17 7,321 11-389 -1,891-796 1,737-2,23-3,666-1,56-637 9,344 2,779 9,556-2,897 9,376 12-38 -674 127 1,7 1,168-87 -49-376 6,918 1,49 9,917-1,218 8,735 Total 335-5,323-2,43 22,59 6,429-14,546-7,27-2,818 25,121-8,423 11,751-17,226 7,749 22
Things to Consider when Comparing Number of Trips: Differences in population and employment do not directly translate in differences in proportional distribution of commuter trips between trip interchanges. Most of the trips to work in Pinal County are to Maricopa County. Other trip interchanges are not affected by much. Trip rates fluctuate and are different between CTPP period estimates and 211 MAG point estimate. 23
D2D Trips MODEL D2D Trips CTPP Total Trips CTPP (211 MAG Model 26-21 CTPP Trips) / Total CTPP Trips District Total 1.5%.4% -.1%.% -.3%.5%.19%.11% -.1% -.2% -.2%.%.5% 2.1%.18%.9%.25%.4%.3%.8% -.4% -.2% -.4% -.5% -.1%.1% 3.%.3% -.8%.7%.5%.% -.2% -.1%.%.13% -.3%.1%.% 4 -.1% -.21%.4%.2%.2% -.35% -.1% -.3%.6% -.2% -.13% -.3% -.1% 5 -.2% -.5% -.7%.17% -.2% -.4% -.17% -.6%.2% -.12% -.16% -.3% -.2% 6 -.3% -.1% -.7% -.51%.41% -.1% -.29% -.16% -.4% -.27% -.36% -.5% -.3% 7.4%.12% -.1% -.1% -.24%.18%.6%.% -.1% -.7% -.11% -.1%.4% 8.3% -.4% -.2% -.3%.1%.13%.2%.14%.2% -.4% -.3% -.1%.3% 9.% -.6% -.3% -.11%.3% -.11% -.4% -.3%.36% -.4%.% -.6%.% 1 -.1% -.1%.3%.67%.6% -.2% -.8% -.4%.22% -.37%.38% -.7% -.1% 11 -.3% -.13% -.5%.12% -.15% -.25% -.1% -.4%.63%.19%.64% -.19% -.3% 12.% -.5%.1%.11%.8% -.6% -.3% -.3%.46%.1%.67% -.69%.% Total.2% -.36% -.16% 1.48%.43% -.98% -.48% -.19% 1.69% -.57%.79% -1.16%.52% 24
Modeled Modeled versus CTTP Commuter Flows 14, 12, 1, y = 1.29x R² =.9792 8, 6, 4, 2, 2, 4, 6, 8, 1, 12, 14, CTPP %RMSE=27.66 Each data point represents number of trips for a district-to-district trip interchange. 25
Distribution of Commuter Trips from District 1 Distribution of Commuter Trips to District 1.35.3.25.2.15.1.3.25.2.15.1.5.5.. Distribution of Commuter Trips from District 2 Distribution of Commuter Trips to District 2.35.35.3.3.25.25.2.2.15.15.1.1.5.5 26
.5.4.3.2.1 Distribution of Commuter Trips from District 3.4.3.2.1 Distribution of Commuter Trips to District 3 Distribution of Commuter Trips from District 4 Distribution of Commuter Trips to District 4.6.6.5.5.4.4.3.3.2.2.1.1 27
.6 Distribution of Commuter Trips from District 5.6 Distribution of Commuter Trips to District 5.5.5.4.4.3.3.2.2.1.1 Distribution of Commuter Trips from District 6 Distribution of Commuter Trips to District 6.5.5.4.4.3.3.2.2.1.1 28
Distribution of Commuter Trips from District 7 Distribution of Commuter Trips to District 7.4.35.3.25.2.15.1.5.4.35.3.25.2.15.1.5 Distribution of Commuter Trips from District 8 Distribution of Commuter Trips to District 8.4.4.3.3.2.2.1.1 29
Distribution of Commuter Trips from District 9 Distribution of Commuter Trips to District 9.5.5.4.4.3.3.2.2.1.1.5 Distribution of Commuter Trips from District 1.5 Distribution of Commuter Trips to District 1.4.4.3.3.2.2.1.1 3
.5 Distribution of Commuter Trips from District 11.5 Distribution of Commuter Trips to District 11.4.4.3.3.2.2.1.1 Distribution of Commuter Trips from District 12 Distribution of Commuter Trips to District 12.6.5.4.3.2.1.6.5.4.3.2.1 31
Takeaways Comparison between models and CTPP travel data is possible despite some apparent differences in population and employment statistics and the way data is produced. Comparison with CTPP data provides valuable insight and can be used as a validation tool for trip distribution purposes, for example. In the scarcity of independent data sets such validations provide a unique additional layer of checking trip distributions on regional level. Careful analysis of modeling and CTPP data should precede comparisons. Districts delineations should provide for sufficient level of aggregation to allow for meaningful comparisons and should be based on network topology, socio-economic and travel characteristics in the region. In large regions proper development of the regional travel demand sub-models and comparisons should ensure relatively close match with aggregated CTPP flows with RMSE below 3% and R 2 exceeding.9. 32
Possible Next Steps Analyze possible changes to CTPP products to make annual comparisons more meaningful. Experiment with different district delineations and more detailed districts. Establish an iterative process to delineate districts leading to more homogeneous districts in terms of number of intra-district and inter-district trips. 33
Questions? 34