Tennessee Statewide Model Integration with the National Long Distance Passenger Model and Calibration to AirSage Data Vince Bernardin, PhD, RSG Hadi Sadrsadat, PhD, RSG Steven Trevino, RSG Chin-Cheng Chen, RTCSNV May 16, 2017

Background

TDOT and Tennessee MAJOR INTERSTATE CORRIDORS Tennessee crossed by I-40, I-75, I-24, and I-65 Long distance truck and passenger traffic fight for capacity with local travelers especially in Knoxville and Chattanooga where mountainous topography limits alternative through routes Major studies examining needs for these existing major corridors Possible extension of I-69

Tennessee Statewide Travel Model - History ORIGINAL 2005 TSTM Fixed trip tables, limited coverage VERSION 2 TSTM (2014) 3-step, pivot-point model, 3x network & zonal detail Accelerated schedule to support state’s long range plan TDOT FIRST EXPERIENCE WITH BIG DATA IN TSTM2 8 week dataset from American Transportation Research Institute (ATRI) to support the Version 2 TSTM Over 234,000 individual trucks Over 6.5 million truck trips Represents roughly 11% of the trucks on the road for 56 days

Memphis 1,000 Truck Sample

Same 1,000 Trucks After 24 Hours

Same 1,000 Trucks After 48 Hours

Same 1,000 Trucks After 72 Hours

Same 1,000 Trucks After 5 Days

Same 1,000 Trucks After 7 Days

Number of Count Stations Correlation Coefficient TSTM2 Success SUBSTANTIAL IMPROVEMENT IN MODEL PERFORMANCE Access to TN State University System Volume Range Number of Count Stations Percent Error Correlation Coefficient MAPE %RMSE Model Standard 1 5,000 7,288 10.2 0.69 66.8 91.1 101.4 10,000 1,943 5.5 0.61 31.1 39.6 56.3 20,000 1,700 0.8 0.79 21.0 27.8 51.4 30,000 747 -2.1 0.85 15.9 21.5 35.7 40,000 318 0.6 12.0 18.1 32.0 + 661 -0.1 0.94 11.5 15.6 21.6 Total 12,645 2.1 0.97 47.9 37.0 60.0

TSTM Version 3 BUILD ON SUCCESSES AND CAPITALIZE ON OPPORTUNITIES New Commodity Flow Freight Model Make ATRI-based trucks sensitive Advanced trip-based model to replace 3-step Mode & Destination choice models, Linked NHB trips New AirSage+ATRI-based pivot point Incorporate New National Long Distance Model Calibrate to AirSage Successful use in Chattanooga Successful use for intercity corridors

Model Design & Implementation

Tennessee Statewide Travel Model LONG DISTANCE PASSENGER DEMAND INPUTS Networks SE Data 1. Network Skimming 2. Synthetic Population Expansion DEMAND MODELS Freight Demand Short Distance Passenger Demand Long Distance Passenger Demand 3. rJourney National Long Distance Model 4. Matrix Aggregation / Disaggregation ASSIGNMENT

1. Network Skimming INTEGRATION Statewide model network in TN integrated with National Highway Planning Network outside TN National Zone to National Zone (NUMA to NUMA) skim NUMA to TAZ correspondence for centroids

2. Synthetic Population / Socioeconomics EXPANSION Expand (or sample) base population rather than re-synthesize Based on TAZ households within TN Separate county household totals file outside TN Detailed future demographic not needed outside TN Faster than re-synthesizing population for whole nation SIZE VARIABLES Similarly, socioeconomic size variables for destination choice are scaled Based on TAZ employment, etc., within TN Separate county employment totals file outside TN

3. National Long Distance Model COMPONENTS Household level disaggregate simulation like ‘simplified’ activity-based model Tour generation-scheduling-duration, and party size by business and leisure Destination and mode choice INPUTS/OPTIONS Networks/travel times and costs Autos (from TSTM) Bus (from TSTM) Rail (exogenous input) Air (exogenous input) Possibility for many policy scenarios/alternative futures 5 LD Tour Types in rJourney Visit friends & family Leisure Personal Business Commute Employer’s Business

4. Matrix Aggregation/Disaggregation RECONCILING ZONE SYSTEMS rJourney national model produce NUMA to NUMA trip table Within TN (and immediate halo) NUMAs disaggregated to TAZ Beyond, NUMAs aggregated to larger zones (e.g., states) Final trip table has manageable number of zones  faster assignment Same network uses different zones/centroids for skimming and for assignment

Data Development

Big Data Fusion – AirSage & ATRI FILTERED OUT “INTERMEDIATE” STOPS ON LONG DISTANCE TRUCK TRIPS IN ATRI Meant to make ATRI trips comparable to both AirSage and commodity flows (FAF/Transearch) Used similar but slightly different algorithm than AirSage – compared distances, if AB + BC ≈ AC then drop B B A C B A C

AirSage – ATRI Integration SUBTRACTED ATRI FROM AIRSAGE Before filtering, more negatives: 11% of cells 0.20% of trips After filtering, less negatives: 1.3% of cells 0.09% of trips Still not perfect, but filtering ATRI reduced negatives by 87% Remaining negatives indicate remaining issue with intermediate stops, or perhaps coverage drops along some Interstates BEFORE AFTER

AirSage Expansion Problem AIRSAGE DEFAULT CARRIER MARKET SHARE METHOD Standard practice AirSage does preliminary expansion based on carrier market share by census geography analysts scale for “auto occupancy” (actually, vehicle trips/cell trips) Previously worked reasonably well for both urban areas and intercity corridors But in TN statewide context significant urban under-loading (e.g., -10%) and rural/intercity over-loading (e.g.,+15%)

AirSage Expansion Adjustments TWO-STAGE ADJUSTMENT How best to expand to traffic counts? Parametric – fit distance-based expansion factor curves for residents and non-residents Non-parametric – used ODME for residual adjustments Avoid massive ODME adjustment, provide explanation/understanding of bias and correction Trip length bias hypothesis from previous finding with other passive data (ATRI) and general explanation of observed pattern Under-representation of shorter distance/duration trips confirmed by both parametric and non-parametric methods

Parametric Adjustment Statistic Before After Urban Percentage of Error -10% -2% Rural Percentage of Error +15% +5% TRIP LENGTH CORRECTION Resident Expansion Factor = 0.06 + 1.64*Exp(-0.051*Length) Visitor Expansion Factor = 0.03 + 0.34*Exp(-0.020*Length) Visitors are already long distance travelers – may be more likely to have cell phones / higher auto occupancy 100 mi trip is 12 times as likely to be detected as a 10 mi trip Didn’t we rescale to 0% error? If so, we should update all of this.

Non-Parametric Adjustment (ODME) CONTROLS Minimum factor 0.5 Maximum factor 5.0 10 iterations RESULTS Matrix MAPE 1.29 RMSE vs. counts from 66.3% to 36.1% Modest additional increase in short trips Didn’t we rescale to 0% error? If so, we should update all of this.

Calibration Results

TN Internal Districts DISTRICT SCHEME (INTERNALS)

Long Distance Trip Generation / Frequency MODELS VS. AIRSAGE No purpose in AirSage, different purposes in v2 vs v3, so only compare total long distance trip rates TSTM3 / rJourney generally reproduce AirSage well Old model based on NCHRP 735 was 17% low – but offset by lower vehicle occupancy (3.0 vs 3.5)

Trip Distribution – State Border Crossings INBOUND/OUTBOUND VS WITHIN STATE AirSage shows much more balanced within-state and inbound/outbound long distance trips Gravity models a la NCHRP 735 cannot reproduce this pattern rJouney reproduces the pattern after the addition of a psychological boundary term to its destination choice models Trip Type AirSage TSTM v2 TSTM v3 I-I Trips 110,779 11,597 112,741 I-E and E-I Trips 88,422 177,468 78,790 Total 199,201 189,065 191,531

Trip Length Distribution WITHIN STATE TRIPS Generally good, except too many <75 mi trips

Trip Length Distribution WITHIN STATE + INBOUND/OUTBOUND TSTM3 / rJourney generally tracks AirSage Model expected to be lower at extreme long distances b/c AirSage data districts do not cover whole country

TN Internal Districts DISTRICT SCHEME (INTERNALS)

TN External Districts DISTRICT SCHEME (EXTERNALS)

Within TN Trip Distribution DISTRICT-TO-DISTRICT COMPARISON Importance of comparing actual OD pattern, not just TLFD Very good agreement, overall District level origins & destinations all within 1.5% District level ODs all within 2% except within Nashville district Relative Percentage Difference (Model Version 3 vs AirSage) I-I Trips

To/From TN Trip Distribution DISTRICT-TO-DISTRICT COMPARISON Generally good agreement District level origins & destinations all within 10%, most within 3% Smoky Mtns not attracting enough to/from Knoxville District level ODs all within 4% except within Nashville - Northcentral Relative Percentage Difference (Model Version 3 vs AirSage) I-E & E-I Trips

Conclusions

Conclusions NATIONAL LONG DISTANCE MODEL Capable of integration with statewide models Feasible implementation structures, runtimes (~3-5 hrs) Capable of calibration to new data Calibration to regional data important May need new psychological boundaries, etc. AIRSAGE LONG DISTANCE DATA Valuable new data capable of supporting long distance modeling Data reveals patterns significantly different than NCHRP 735 national defaults Possible trip length bias makes scaling/expansion challenging – and important

DIRECTOR OF TRAVEL FORECASTING Contact Vince Bernardin, Jr, PhD www.rsginc.com DIRECTOR OF TRAVEL FORECASTING Vince.Bernardin@rsginc.com 812.200.2351