May 17 2017 Steven Trevino RSG Vince Bernardin PhD RSG Hadi Sadrsadat PhD RSG Tennessee Statewide Model Overview ASSIGNMENT Freight Demand Short Distance Passenger Demand Long Distance Passenger Demand ID: 796877
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Slide1
Handling Long Distance Trips in Statewide Model Peak Period Assignments
May 17, 2017
Steven Trevino, RSG
Vince Bernardin, PhD, RSG
Hadi Sadrsadat, PhD, RSG
Slide2Tennessee Statewide Model Overview
ASSIGNMENT
Freight Demand
Short Distance Passenger Demand
Long Distance Passenger Demand
Networks
SE Data
LONG DISTANCE PASSENGER DEMAND
1. Network Skimming
2. Synthetic Population Expansion
3.
rJourney
National Long Distance Model
4. Matrix Aggregation / Disaggregation
DEMAND MODELS
INPUTS
Slide3National Long Distance Model Overview
Slide4National Long Distance Model Overview
COMPONENTS‘Simplified’ activity-based model
HH level disaggregate simulation
Tour generation, scheduling, duration, and party size
Cross-nested Destination & Mode Choice
INPUTS/OPTIONSNetworks/travel times and costs
Autos, Bus, Rail, AirPossibility for many policy forecasts
Slide5Commodity Flow Model
Slide6ATRI Memphis 1000 Truck Sample
24 hours
Slide748 Hours
Slide872 Hours
Slide95 Days
Slide10AssignmentImproved model run timesAADT priority, some peak volume scenarios
Solution: Daily assignment & optional AM/PM peak period assignment
Slide11ChallengesExisting models produce daily tripsSegmenting for AM & PM period assignments
Long Distance trip travel time can vary greatly
Slide12Solution OverviewStatic Assignment – DTA is not feasiblePredict period trips based on their mid-point
Apportion long trips travelling within periods
Slide13Truck PeriodsFixed factors based on classification count data
Slide14Time of Day Choice Models
: trip share in time
m
, zone
i
to zone
j
with purpose
k
: alternative specific constant of time
m
: distance from zone
i to zone j, and
: General Accessibility difference between zone i and j
&
: model parameters (varied by purpose k)Combined survey data – over 10k householdsPurposes: HBW, HBO, NHB, Long DistanceEstimated to survey, calibrated to counts
Slide15General Accessibility
Slide16Apportioning Long Distance TripsAssume uniform distribution of midpointsWith midpoints within period, 3 outcomes
Case 1: 6+ hour trip completely within periodCase 2A: < 6 hour trip completely within period
Case 2B: < 6 hour partial trip
Slide17Case 1: 6+ hour trip
Peak
Non-peak
180 min
L
Slide18Case 2A: <6 hour trip, completely in period
Peak
Non-peak
180 min
½ L
½ L
Slide19Case 2B: <6 hour trip, partially within period
Peak
Non-peak
180 min
½ L
¼ L
Slide20Thanks, Uniform Distribution!
Long Distance Trips < 6 hours:
Long Distance Trips 6+ hours:
Slide21Conclusions
Trip Type
AM Share
PM Share
OP Share
Home-Based Work
38.7%
33.5%
27.8%
Home-Based Other
24.8%
33.7%
41.5%
Non-Home-Based
17.4%
32.4%
50.2%
Long Distance
20.3%
34.7%
45.0%
Passenger Survey
24.5%
33.3%
42.3%
Auto Counts
18.6%
29.3%
52.1%
Passenger Model
18.1%
25.5%
56.5%
Slide22Conclusions
Daily Volume
Stations
% Error
MAPE
% RMSE
Model
Standard
< 5,000
7,864
9.02
82.6
103.3
101.4
5,000 - 10,000
2,343
-1.71
24.2
35.8
56.3
10,000 - 20,000
1,691
-4.95
15.6
22.0
51.4
20,000 - 30,000
486
-5.57
10.8
16.2
35.7
30,000 - 40,000
199
-4.72
10.9
14.5
32.0
> 40,000
253
-3.99
8.0
10.8
21.6
Total
12,833
-2.00
57.8
36.5
60.0
Slide23Long Distance Trip Generation / Frequency
MODELS VS. AIRSAGENo “long distance” purpose in AirSage
or in previous model
Compare trip rates between TSTM3 /
rJourney
and AirSageOld model based on NCHRP 735 was 17% low
Slide24Trip Distribution – State Border Crossings INBOUND/OUTBOUND VS WITHIN STATE
AirSage
shows much more balanced within-state and inbound/outbound long distance trips
Previous version’s gravity models (NCHRP 735) cannot reproduce pattern
rJourney
able to reproduce pattern given inclusion of psychological boundary in destination choice models
Trip Type
AirSageTSTM v2TSTM v3
I-I Trips 110,779
11,597 112,741
I-E and E-I Trips
88,422 177,468
78,790Total
199,201189,065
191,531
Slide25Trip Length Distribution
WITHIN STATE + INBOUND/OUTBOUNDTSTM3 /
rJourney
generally tracks
AirSage
Model expected to be lower at extreme long distances b/c AirSage data districts do not cover whole country
Slide26Conclusions
NATIONAL LONG DISTANCE MODEL
Capable of integration with statewide models
Feasible implementation structures, runtimes (~2.5
hrs
)Capable of calibration to new dataCalibration to regional data important
May need new psychological boundaries, etc.AIRSAGE LONG DISTANCE DATAValuable new data capable of supporting long distance modelingData reveals patterns significantly different than NCHRP 735 national defaults
Possible trip length bias makes scaling/expansion challenging – and important
Slide27Network Skimming
INTEGRATIONStatewide 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
Slide28Synthetic Population / Socioeconomics
EXPANSIONRather than synthesize new/alternative
synthetic populations based on future
control variables, expand (or sample)
base population
Based on TAZ households within TN Separate county household totals file outside TNNo need for detailed future demographic control variables for whole country
Faster than re-synthesizing population for whole nationSIZE VARIABLESSimilarly, socioeconomic size variables for destination choice are scaled Based on TAZ employment, etc., within TN
Separate county employment totals file outside TN
Slide29www.rsginc.com
Contact
Steven Trevino
Analyst
Steven.Trevino@rsginc.com
812.200.2352