Transcript Presentation - 15th TRB National Transportation Planning

Session 6:
Ohio Statewide Travel Model:
Framework, Freight, and Initial
Calibration
11th National Transportation Planning Applications Conference
May 6-10, 2007, Daytona Beach, Florida
Andrew Stryker| PB | 503-417-9360 | [email protected]
Acknowledgements
 This presentation was primarily developed by Pat
Costinett.
Topics
 Ohio Statewide Modeling Framework
 Micro-simulation
 Integrates:



Economic
Land use
Transport Models
 Aggregate Commercial Model (ACOM)
 Preliminary calibration results
General Model Structure
 Integrated micro-simulation based
 Model economic activity & land use
 Build synthetic population
 Tour-based
 Home tours
 Establishment/Work tours
 Aggregate commodity movements
Model Components & Flows
Model Area
Households by
Category
ISAM
JobData
GS
GridCellData
SPG1
LD
Floorspace
Inventory by TAZ
AA
Activity Locations by TAZ,
Labor Flows &
Commodity Flows by AMZ
ISAM
Activity Data
TAZ Data
VM
SPG2
Model Area
Synthetic
Population
Visitor Synthetic
Population
ISAM E-E Flows,
Import/Export
Region Shares
Auto & Transit
Skims
PT
ACOM
DCOM
Truck Vehicle
Trips by Type
ASSIGN
Next Time Period
Weekday Trips/Tours
Employment by Type
Household/Person Data
MC Logsums
Employee Trips
Loaded Networks
Auto & Transit Skims
Model Components & Flows
Model Area
Households by
Category
ISAM
JobData
GS
GridCellData
SPG1
LD
Floorspace
Inventory by TAZ
AA
Activity Locations by TAZ,
Labor Flows &
Commodity Flows by AMZ
ISAM
Activity Data
Economic Activity by
TAZ Data
Geography
VM
SPG2
Model Area
Synthetic
Population
Visitor Synthetic
Population
ISAM E-E Flows,
Import/Export
Region Shares
Auto & Transit
Skims
PT
ACOM
DCOM
Truck Vehicle
Trips by Type
ASSIGN
Next Time Period
Weekday Trips/Tours
Employment by Type
Household/Person Data
MC Logsums
Employee Trips
Loaded Networks
Auto & Transit Skims
ISAM
 Input-output economic model

Represents trading commodities

Exogenous to the model system
1 = Model Area
ISAM
 Input-output economic model

Represents trading commodities

Exogenous to the model system
 Region to region commodity flows
 Shares of commodity flows from the model area to
regions
Economic Activity & Land Development
 Approximately 700 districts and 4000 zones
 Distribution of economic activities & flows by sector to
analysis districts




Production of goods & services by zone
Consumption demand for goods & services by zone
Flows of commodities (goods, services & labor) among zones
In response to exchange prices
 Interacting with a grid-based representation of land
supply, develop types, zoning, water & sewer service,
flood plains, steep slopes, other protected land uses and
land prices
Economic Activity & Land Development
 Results:
 Flows of commodities between districts
 Floor space allocated to activities by zone
Model Components & Flows
Model Area
Households by
Category
ISAM
JobData
GS
GridCellData
SPG1
LD
Floorspace
Inventory by TAZ
AA
Activity Locations by TAZ,
Labor Flows &
Commodity Flows by AMZ
Transport Models
ISAM
Activity Data
TAZ Data
VM
SPG2
Model Area
Synthetic
Population
Visitor Synthetic
Population
ISAM E-E Flows,
Import/Export
Region Shares
Auto & Transit
Skims
PT
ACOM
DCOM
Truck Vehicle
Trips by Type
ASSIGN
Next Time Period
Weekday Trips/Tours
Employment by Type
Household/Person Data
MC Logsums
Employee Trips
Loaded Networks
Auto & Transit Skims
Types of Trip Making Modeled
 Personal Travel /Household Travel (PT):


person movements arising from household (or population)
production and consumption,
separated into short distance (50 mi or less) and long distance
 Visitor Travel (VM):

person movements made by non-residents staying at locations
in the internal model area
 Business/Services Travel (DCOM):

movements arising as part of the rest of the ‘business cycle’
apart from the physical delivery of commodities
 Goods Transport (ACOM):

shipments of commodities arising from economic activity
production and consumption
Commercial Travel
 Incorporates long-haul commodity shipment, localized
goods delivery, service provision & work-related tours
 Long-haul shipment related directly to commodity flows
 Establishment survey of goods delivery, service provision
& work-related tours
 Micro-simulation of commercial tours for each employee
(a first at this scale)
Why a freight model?
 Need to be consistent with economic models
 Freight movements are important to Ohio:
 Interest in impact of Turnpike tolls on trucks.
 Interest in road-rail diversion.
 Relatively large impact on traffic LOS
Underlying “Theory”
 Commodities are carried by trucks, rail, and other
modes
 Commodity flow patterns determine truck flow patterns
 Truck characteristics vary substantially by commodity
type and shipment distance




Mode share
Average value per ton
Size mix
Average payload weight
 Unlike personal travel, commodity shipment choices are
influenced very little by network LOS measures
What does it do?
 ACOM translates dollar flows of commodities from
ISAM and AA into truck trips by four size categories



ISAM for E-E
AA for I-I
Both for E-I and I-E
ACOM and Economic Models Relationships
AA
ISAM
AA
Internal to Internal
Internal to
External
ISAM
External to Internal
External to
External
General Model Flow
ISAM
Production and
Consumption Weights
AA
Distribute to
Commodity Flow
Matrices
Convert to
Truck Trips
by STCC
Distance
External to External Flow
ISAM
Expand regions
to ETAZs
Expand using
distance
Production and
Consumption Weights
Distribute to
Commodity Flow
Matrices
Convert to
Truck Trips
by STCC
AA
Region to region
E-E flows
by commodity
Distance
Internal to Internal Flow
ISAM
Allocate districts
to TAZs
Production and
Consumption Weights
AA
Expand using
distance
Distribute to
Commodity Flow
Matrices
Convert to
Truck Trips
by STCC
Distance
Districts to
districts flows
and floor space by TAZ
by commodity
Internal to External Flow
ISAM
Allocate districts
to TAZs,
regions to
ETAZs
Distribute using
singly constrained
gravity model
Production and
Consumption Weights
AA
Distribute to
Commodity Flow
Matrices
Convert to
Truck Trips
by STCC
Region shares of
commodities
Distance
District exports
and floor space by TAZ
by commodity
$ Flows to Truck Trips by Size
Factors
Total $’s to
Truck $’s
Distance
Truck $’s to
Truck tons
Split Truck tons
by Truck Size
Convert to
Truck trips
by STCC
Convert to
time periods
Calibration
 Each of the models uses a gamma function to calculate
deterrence as a function of distance and three
parameters
 The parameters can be adjusted up or down to match
trip lengths and distribution shapes
 Calibration Targets



Ohio county to external state for Statewide Cordon
Roadside Survey
Selected MPO County to other Ohio counties truck trips
from MPO Roadside Surveys
Average trip lengths by area from CFS97 and Transearch
Average Truck Trip Lengths
STCC DESCRIPTION
1
8
9
10
11
13
14
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
48
Farm products
Forest products
Fish/marine products
Metallic ores
Coal
Petroleum/natural gas/gasoline
Non-metallic minerals
Ordnance
Food and kindred products
Tobacco products
Textile mill products
Apparel
Lumber and wood products
Furniture and fixtures
Pulp/paper/allied products
Printed matter
Chemicals and allied products
Petroleum and coal products
Rubber and plastics
Leather and leather goods
Clay/concrete/glass/stone
Primary metal products
Fabricated metal products
Machinery
Electrical machinery
Transport equipment
Instruments and precision goods
Misc manufactured products
Waste or scrap
Miscellaneous freight shipments
Hazardous materials or waste
TOTAL
Intra-MA
140
163
139
269
126
142
141
123
132
128
146
149
131
152
140
120
134
139
123
143
100
123
-
MA Origin
645
355
654
874
566
1038
621
809
749
631
788
411
786
874
585
604
712
908
759
584
1403
925
-
MA
Destination
1370
315
619
543
624
738
575
673
672
570
717
375
706
1274
479
467
535
788
984
694
1435
1117
-
Non-MA
2318
417
1212
1043
1362
2037
1016
1404
1184
1322
1164
825
1184
2363
908
953
1088
1335
1371
1058
1944
1710
-
Average Truck Trip Lengths
STCC
1
8
9
10
11
13
14
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
48
DESCRIPTION
Farm products
Forest products
Fish/marine products
Metallic ores
Coal
Petroleum/natural gas/gasoline
Non-metallic minerals
Ordnance
Food and kindred products
Tobacco products
Textile mill products
Apparel
Lumber and wood products
Furniture and fixtures
Pulp/paper/allied products
Printed matter
Chemicals and allied products
Petroleum and coal products
Rubber and plastics
Leather and leather goods
Clay/concrete/glass/stone
Primary metal products
Fabricated metal products
Machinery
Electrical machinery
Transport equipment
Instruments and precision goods
Misc manufactured products
Waste or scrap
Miscellaneous freight shipments
Hazardous materials or waste
TOTAL
OHIO
35
38
55
89
29
28
56
52
50
67
67
58
49
55
24
55
32
77
67
44
76
56
60
44
56
52
63
56
63
29
43
ADJACENT
STATES
220
152
176
54
169
202
220
193
193
185
192
182
225
230
195
164
193
186
221
202
192
286
218
219
236
162
153
138
186
ALL
OTHER
STATES
739
683
333
464
844
645
849
849
693
1114
748
653
811
682
821
849
813
606
844
858
1142
718
896
879
426
378
200
783
ALL
STATES
254
38
164
80
100
30
31
386
228
46
404
404
133
398
267
112
383
52
422
404
154
243
386
380
717
350
446
453
86
93
36
175
S3 Calibration OD Checks
 Total auto and total truck trips crossing model area
and Ohio cordons versus counts
 Ohio county to external state auto and truck trips
versus roadside survey for Ohio cordon
 For counties entirely within MPO roadside survey
cordon, OD flows to counties entirely outside MPO
cordon versus MPO roadside survey
MPO Roadside Survey Cordons
OD Analysis Districts
Initial results for auto vehicle trip OD (1)
MODEL versus OBSERVED AUTO VEHICLE OD FLOWS
300000
250000
200000
M 150000
O
D
E
L 100000
50000
0
0
50000
100000
150000
-50000
OBSERVED
200000
250000
300000
Initial results for auto vehicle trip OD (2)
MODEL versus OBSERVED AUTO VEHICLE OD FLOWS
150000
125000
100000
M
O
D
E
L
75000
50000
25000
0
0
25000
50000
75000
-25000
OBSERVED
100000
125000
150000
S3 Calibration Global Assignment Checks
 VMT by FUNCLASS
 Model Area
 Ohio
 MPO county groups
 Major Screenline Volumes by FUNCLASS
 Model Area cordon
 Ohio cordon
 MPO cordons
 Source of independent VMT estimates? Counts versus
“counts”
Initial Unconstrained Auto Assignment Results
Sum of Link Flows for Links with Actual Year 2000 Counts (20,751 links)
ADT
ASSIGN
17034
30096
4657
4323
2752
2279
1335
702
559
134
708
166
41459
48791
18190
16009
8250
7426
5493
3090
2807
1000
1420
910
5098
5622
60000
50000
ADT
Sum of Link Flows
Fed FC
1
2
6
7
8
9
11
12
14
16
17
19
22
ASSIGN
40000
30000
20000
10000
0
0
5
10
15
Federal Functional Class
20
25
Initial Unconstrained Auto Assignment Results
Sum of Link Flows for Links with Actual Year 2000 Counts (20,751 links)
Fed FC COUNT SUM_ADT SUM_ASN
1
143
17,034
30,096
2
1,012
4,657
4,323
6
1,610
2,752
2,279
7
4,410
1,335
702
8
742
559
134
9
660
708
166
Rural Sum
8,577
27,045
37,700
11
12
861
41,459
48,791
14
528
18,190
16,009
16
3,753
8,250
7,426
17
3,901
5,493
3,090
19
2,266
2,807
1,000
22
865
1,420
910
Urban Sum
12,174
77,620
77,227
Total
20,751
104,664
114,927
PDIF
77%
-7%
-17%
-47%
-76%
-77%
39%
VMT_ADT
2,386,481
3,667,264
2,444,532
3,652,151
288,970
403,467
12,842,865
VMT_ASN
4,218,090
3,443,332
2,137,080
1,912,224
64,982
81,879
11,857,586
PDIFV
77%
-6%
-13%
-48%
-78%
-80%
-8%
18%
-12%
-10%
-44%
-64%
-36%
-1%
10%
14,190,190
4,029,882
9,568,824
6,618,715
2,372,230
454,124
37,233,965
50,076,830
15,964,581
3,450,107
8,034,644
3,240,280
655,208
193,429
31,538,249
43,395,835
13%
-14%
-16%
-51%
-72%
-57%
-15%
-13%
Initial Unconstrained Auto Assignment Results
All Links
Fed FC
# Links SUM_ADT SUM_ASN
1
2,032
51,948
81,225
2
7,764
13,997
12,267
6
12,516
7,956
6,499
7
46,989
4,994
2,187
8
23,360
1,744
295
9
93,460
1,381
518
Rural Sum 186,121
82,021
102,990
11
4,869
119,668
139,624
12
3,145
49,018
40,894
14
21,636
22,793
20,060
16
33,105
12,721
7,586
17
34,679
8,183
2,957
19
42,234
4,193
3,085
22
6,426
9,660
10,225
Urban Sum 146,094
226,238
224,432
Total
332,215
308,259
327,422
PDIF
56%
-12%
-18%
-56%
-83%
-63%
26%
17%
-17%
-12%
-40%
-64%
-26%
6%
-1%
6%
VMT_ADT
24,334,774
17,733,928
12,518,167
20,006,755
1,810,714
1,263,701
77,668,040
59,121,970
14,868,693
32,061,908
18,644,515
10,948,984
4,389,536
4,279,929
144,315,534
221,983,574
VMT_ASN
38,854,206
16,397,958
10,701,876
11,048,497
471,872
439,466
77,913,875
64,664,572
12,167,979
26,567,182
10,182,826
3,190,649
2,795,015
4,186,428
123,754,651
201,668,527
PDIFV
60%
-8%
-15%
-45%
-74%
-65%
0%
9%
-18%
-17%
-45%
-71%
-36%
-2%
-14%
-9%
Conclusions
 This framework allows us to be consistent.
 Calibration results look good so far.
 More work to be done.
Questions for Pat?