A Comparison of
CEMDAP Activity-Based Model
With
DFWRTM 4-Step Model
Arash Mirzaei P.E.
NCTCOG - Arlington, Texas and
Naveen Eluru
UT Austin - Austin, Texas for
11 th TRB National Transportation Planning Application
Conference
Daytona Beach, Florida
May 2007

Credit and Acknowledgement


Credit
 CEMDAP Research Team

 Chandra Bhat, Jessica Guo, Siva Srinivasan
(Faculty)
 Abdul Pinjari, Rachel Copperman, Ipek Sener
(Graduate Students)
NCTCOG
 Ken Cervenka, Bin Chen, Arash Mirzaei
Acknowledgement
 TxDOT
 Janie Bynum, Bill Knowles

Content
 Introduction
 The Models
 Test Method
 Comparisons
 Closing Thoughts

Introduction

Regional 4-Step Model Use






Long Range Plan, Mobility Plan 2030
 Number of lanes


Cost estimate
Benefit analysis
 Project selection
Air Quality Conformity Analysis


Emission - budget analysis
Project AQ conformity
Project Specific Analysis
 Alternative analysis in Roadway (including HOV, Toll, and
Managed Lane)
 Transit (system planning, New Starts)
TIP and Congestion Management
Platform for communication and storage of various data
Used as a guide for policy making in various transportation issues

Is 4-Step Model Adequate?
 For many current uses, 4-step model has been adequate
 Some areas of shortfall:
 Peak spreading
 Treatment of NHB trips
 Evaluation of demand management strategies
 Environmental justice

Expectation of ABM
 At least as good as 4-step in areas 4-step has proven adequate
 Noticeably better in areas 4-step has not been adequate
 Focus on person vs. transportation facility (e.g. who parked and why vs. how many parked). A much harder calibration process.

Road From 4-Step to ABM
6.
7.
4.
5.
1.
2.
3.
Have a production line 4-step model
Create an ABM in parallel of the production line model
Clear up uncertainties about random seed, run time, and other operational issues
Check validation designed for 4-step vs. ABM
Check validation designed for ABM
Perform sensitivity tests designed for 4-step vs. ABM
Perform sensitivity tests designed for ABM

Validation Designed for 4-Step





Production/attraction by purpose
Trip length distribution (TLD) by purpose/income
Mode choice by market segments
Transit ridership by route, stops, route groups, Park-and-
Ride, mode of access
Traffic volume by functional class, area type, geographic area, and facility type (HOV, Toll, HOT)


VMT, VHT, LOS by functional class and time periods
Facility and corridors
ABM should show at least the same performance in these tests

Validation Designed for ABM







Distribution of income by HH employment, size in zonal level
Trip production rates by purpose, HH type, time-ofday, …
Trip attraction rates by type of employment in zonal level
Trip chain patterns by time of departure, duration, …
TLD by purpose/income/HH type/employment type, …
Transit ridership by type of riders (income) by route (rail, bus)
Traffic volumes by type of riders for HOV, Toll, Managed lane
 More to be determined
ABM should show reasonable results on these tests. The tests should be comprehensive enough to help understand the limitation of the ABM model, even though the model structure may support the test.

Sensitivity Designed for 4-Step




Controlled supply changes


IVTT
OVTT


Toll/fare/cost
Number of lane
Controlled demand changes
 Production/Attraction
 Population/Employment
Project level tests (scenario analysis)
 Transit New Starts
 Traffic/Revenue analysis
Model setup tests
 Transit path builder parameters
 Traffic assignment parameters

Sensitivity Designed for ABM






Seed variation
Number of iterations at different levels
Assumptions in HH synthesis
Assumptions in activity generator
Assumptions in time of departure
More TBD based on different models

Scope of This Comparison



 Controlled supply changes
 IVTT, effect of 25% change on aggregate results


OVTT
Toll/fare/cost
 Number of lane
Controlled demand changes
Project level tests (scenario analysis)
Model setup tests

The Models

DFWRTM Structure






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4874 zones
Trip generation cross-classification
Trip distribution doubly constrained gravity
Mode choice: HBW(6 segments), HNW(6 segments),
NHB
Time-of-day factors: AM peak(2.5 hours), PM peak(3.5 hours), off peak(18 hours)
Transit assignment for daily trips (TransCAD pathfinder)
Non-transit UE

Conceptual Overview of CEMDAP
Forecast Year Outputs
Synthetic population generator ( SPG )
Dynamic Traffic
Assignment
( DTA )
Aggregate sociodemographics
(base year)
Activity-travel environment characteristics (base year)
Policy actions
Model parameters
Base Year Inputs
Detailed individuallevel sociodemographics
(base year)
Link volumes and speeds
Socio-economics, landuse and transportation system characteristics simulator
( CEMSELTS )
Socio-demographics and activity-travel environment
CEMDAP
Individual activity-travel patterns
Activity-travel simulator

CEMDAP Features




Generic Design
 Can be applied to any metropolitan area
Temporal Resolution


Continuous time scale (1 min. for DFW application)
Level-of-service data can be provided at any temporal resolution
(5 time-of-day periods for DFW application)
Spatial Resolution
 Allows for any number of zones (4874 for DFW application)
Graphical User Interface
 Standard Window-based user interface


Allows user to modify model parameters
Provides a friendly diagrammatic interface to help the user understand the logic of the system and the underlying models

Test Method

DFWRTM 4-step Process
DEMOGRAPHI
C
INFORMATION
TRIP
GENERATION ROADWAY
NETWORK
ROADWAY
SKIMS
TRIP
DISTRIBUTION
TRANSIT
NETWORK
TRANSIT SKIMS
NO
MODE
CHOICE
TRAFFIC
ASSIGNMENT
TRAVEL TIME
CONVERGENC
E
YES
TRANSIT
ASSIGNMENT
ZONE LAYER
INPUT
PROCESS
DECISION

DFWRTM with CEMDAP
DEMOGRAPHI
C
INFORMATION
TRIP
GENERATION ROADWAY
NETWORK
ROADWAY
SKIMS
CEMSELTS
TRANSIT
NETWORK
TRANSIT SKIMS
NO
CEMDAP
TRAFFIC
ASSIGNMENT
TRAVEL TIME
CONVERGENC
E
YES
TRANSIT
ASSIGNMENT
ZONE LAYER
INPUT
PROCESS
DECISION

Warning!

We are constraining a “Continuous Time” model by using three broad time periods for a traditional static traffic assignment.
 CEMDAP has a lot more input variables for describing households.

Comparisons

Characteristics Of Modeling Area
5,000 Square Miles
 4,874 Zones (4,813 Internal + 61 External)
Population


4.848 Million in 1999
7.952 Million in 2025 (64% Increase From 1999)
Coded Lane Miles


27,000 in 1999
38,000 in 2025 (41% Increase)

% RMSE by Functional Class
4 0
3 5
3 0
2 5
2 0
1 5
1 0
5
0
8 0
7 5
7 0
6 5
6 0
5 5
5 0
4 5
E x p r e s s w a y P r i n c i p a l A r t e r i a l M i n o r A r t e r i a l
DFW99 w/KFAC
C o l l e c t o r R a m p s
DFW99 No KFAC
F r o n t a g e A l l
CEMDAP00

% RMSE by Count Range
90
85
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
< 10,000 10,000 to
19,999
20,000 to
29,999
30,000 to
39,999
DFW99 w/KFAC
40,000 to
49,999
50,000 to
59,999
60,000 to
69,999
DFW99 No KFAC
70,000 to
79,999
80,000 to
89,999
90,000 +
CEMDAP00
ALL

0
-5
-10
-15
-20
10
5
15
% Volume Error: (Vol-Cnt)/Cnt
Expressway Principal Arterial Minor Arterial Collector Ramps Frontage
DFW99 w/KFAC DFW99 No KFAC CEMDAP00
All

Base Year Vehicle Trips
0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000
DFW-Drive Alone
12,000,000 14,000,000
CEM-Drive Alone
DFW-Shared Ride
CEM-Shared Ride
DFW-Trucks
CEM-Trucks
DFW-All
CEM-All
AM Peak Period (2.5 Hours) PM Peak Period (3.5 Hours) Offpeak Period (18 Hours)
16,000,000

Vehicle Miles of Travel (Including
Connectors)
DFW99 CEMDAP00 CEM/DFW
AM Peak (2.5 Hours) 24,090,807 27,863,030 1.16
PM Peak (3.5 Hours) 33,749,427 39,724,904
Off Peak (18 Hours) 73,724,981 77,623,830
Weekday 131,565,215 145,211,764
1.18
1.05
1.10

Forecast Comparisons
DFW Model CEMDAP CEM/DFW
Vehicle Trips
Base (99/00)
2025
2025/Base
14,341,592 14,101,064
23,056,356 23,123,062
1.608
1.640
Vehicle Miles
Base (99/00) 131,565,215 145,211,764
2025
2025/Base
237,717,217
1.807
240,807,368
1.658
0.983
1.003
1.020
1.104
1.013
0.918
Trip Length
Base (99/00)
2025
9.17
10.31
10.30
10.41

Vehicle Trips to Dallas CBD
DFW Model 1999
CEMDAP 2000
CEM00/DFW99
DFW Model 2025
CEMDAP 2025
CEM25/DFW25
DFW 2025/1999
CEM 2025/2000
Drive Alone
150,550
175,220
1.16
Shared Ride
44,153
55,860
1.27
195,369
261,340
1.34
1.30
1.49
57,813
68,280
1.18
1.31
1.22
2025 Jobs/1999 Jobs = (156,825/130,286) = 1.20
2025 Pop/1999 Pop = (15,316/1,620) = 9.45
2025 Pop + Emp / 1999 Pop + Emp = 1.31
Total
194,703
231,080
1.19
253,182
329,620
1.30
1.30
1.43

VMT Impact of 25% Increase in
Auto and Transit IVTT
CEMDAP 2000
Assume No Change To Worker
Locations
Assume Changes To Worker
Locations
DFW Model 1999
Assume No Change to HBW Trips
Assume Changes To All Trip
Purposes
DFW Model Forecasts
2010--Assume Changes To All Trip
Purposes
2025--Assume Changes To All Trip
Purposes
AM Peak PM Peak Offpeak Weekday
-7.8
-12.3
-2.6
-7.3
-6.9
-17.1
-14.9
-20.1
-4.6
-7.9
-7.5
-14.5
-14.9
-17.1
-5.8
-8.1
-7.7
-12.6
-13.5
-17.0
-4.9
-7.9
-7.5
-13.9

VMT Sensitivity to 25% Increase in IVTT






- 7.5% = DFW Model (2010)
- 7.9% = DFW Model (1999)
- 8.2% = Vancouver (Washington) Base Year Model
- 8.4% = Puget Sound (Washington) Base Year Model
- 13.9% DFW Model (2025)
- 17.0% CEMDAP (2000)

CEMDAP Sensitivity Test (with
Home-Worker Location fixed)
25% Increase in IVTT
25% Increase in IVTT
25% Increase in IVTT
25% Increase in IVTT
25% Increase in Auto Costs
25% Increase in Auto/Transit Costs
$2.00 increase in Auto Costs
To/From Dallas CBD
25% Increase in Auto Costs
25% Increase in Auto Costs
25% Decrease in IVTT
Modes
Auto
All
All
Auto
Auto
All
Auto
All
Auto
All
Time Periods
All
All
AM and PM Peak
AM and PM Peak
All
AM and PM Peak
AM and PM Peak
All
AM and PM Peak
All
Change
-13.8
-13.5
-5.1
-5.0
-1.3
-1.0
-0.8
-0.7
-0.5
9.3

Closing Thoughts

Concerns in the Method



Employment Differences


CEMDAP uses census 2000 Jobs
DFWRTM uses BEA workers
Singly constrained trip-end attraction in CEMDAP
 Possibly causes more or less attraction to zones with specific number of jobs?
Effects of restricting CEMDAP to DFWRTM limitation
 Limitation on time periods
 Static traffic assignment

Interim Conclusions
 Very encouraging 4-step validation results in non-transit
 VMT Sensitivities for base years change in IVTT is higher in CEMDAP but in the same direction.
 4-step sensitivities across different regional models were very close to each other.

Next Steps




Understanding of 4-step models needs improvements
 These models have been in practice for many years but formal validation process and sensitivity tests have not established yet.
ABM validation tests
 These models should be validated beyond 4-step models to deliver the reasons of their creation
ABM operational issues (run time, seed, number of feedbacks, more)
 These issues are not clearly stated or tested yet.
Current ABM’s performance in projects can be compared with
4-step models



Can peak spreading be properly handled?
Can NHB trips be analyzed better?
Can market segments be looked at in more detail?