Transcript Activity-Based Model For Atlanta

Activity-Based Model For Atlanta (Day 1)
by Guy Rousseau, Atlanta Regional Commission
Based on the CT-RAMP (Coordinated Travel – Regional Activitybased Modeling Platform) Family of Activity-Based Travel Demand
Models
•
• Main features:
• Explicit intra-household interactions
• Continuous temporal dimension (Half-hourly time periods)
• Integration of location, time-of-day, and mode choice
models
•Java-based package for AB model implementation
Features of CT-RAMP for ARC
• Combination of best features of developed AB models:
– Fully disaggregate micro-simulation of daily patterns
– Consistent treatment of travel tours
• Addresses specific planning needs of ARC:
– Dynamic / changing population
– Toll facilities and managed lanes
• Innovations:
–
–
–
–
Intra-household interactions
Toll choice
“Available” time influences travel generation
Parking location choice
Treatment of Space
• 2027 TAZs
Hall
Bartow
Cherokee
• TAZs subdivided into
transit accessibility:
Forsyth
• Short walk (1/3 mi)
• Long walk (2/3 mi)
• No walk (> 2/3 mi)
Barrow
Gwinnett
Paulding
Cobb
Walton
DeKalb
Douglas
• All origins and
destinations identified
by TAZ and sub-zone
Fulton
Rockdale
Carroll
Clayton
• 6081 total
alternatives in
destination choice
Newton
Henry
Fayette
Coweta
Spalding
¯
ARC ABM Evolution / History
• 2001 -> 2002: Household Travel Survey Data Collection & Analysis
• 2003  2006
– Models estimated, population synthesizer developed (as presented
@ ITM 2006 in Austin TX)
• 2007  2008
– Model implementation, calibration started
• 2009  2010
– Calibration/validation completed, documentation,
deployment at ARC, and sensitivity testing
• 2011
– Enhanced data reporting and visualization of outputs
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Person Types
NUMBER
PERSON-TYPE
AGE
WORK
STATUS
SCHOOL
STATUS
1
Full-time worker
18+
Full-time
None
2
Part-time worker
18+
Part-time
None
3
Non-working adult
18 – 64
Unemployed
None
4
Non-working senior
65+
Unemployed
None
5
College student
18+
Any
College +
6
Driving age student
16-17
Any
Pre-college
7
Non-driving student
6 – 16
None
Pre-college
8
Pre-school
0-5
None
None
Activity Types
TYPE
PURPOSE
DESCRIPTION
CLASSIFICATION
ELIGIBILITY
1
Work
Working at regular workplace
or work-related activities
outside the home.
Mandatory
Workers and students
2
University
College +
Mandatory
Age 18+
3
High School
Grades 9-12
Mandatory
Age 14-17
4
Grade School
Grades K-8
Mandatory
Age 5-13
5
Escorting
Pick-up/drop-off passengers
(auto trips only).
Maintenance
Age 16+
6
Shopping
Shopping away from home.
Maintenance
5+ (if joint travel, all
persons)
7
Other Maintenance
Personal business/services,
and medical appointments.
Maintenance
5+ (if joint travel, all
persons)
8
Social/Recreational
Recreation, visiting
friends/family.
Discretionary
5+ (if joint travel, all
persons)
9
Eat Out
Eating outside of home.
Discretionary
5+ (if joint travel, all
persons)
10
Other Discretionary
Volunteer work, religious
activities.
Discretionary
5+ (if joint travel, all
persons)
Treatment of Time
• Time-of-day choice models work on hourly
periods
• AM and Midday skims used in choice models
• Output trips assigned by 5 time periods for
highway, 3 for transit
NUMBER DESCRIPTION BEGIN TIME END TIME
1
Early
3:00 A.M.
5:59 A.M.
2
A.M. Peak
6:00 A.M.
9:59 A.M.
3
Midday
10:00 A.M.
2:59 P.M.
4
P.M. Peak
3:00 P.M.
6:59 P.M.
5
Evening
7:00 P.M.
2:59 A.M.
Treatment of Modes
• Explicit toll versus non-toll choice in mode choice
• Local versus Premium (express bus, BRT, rail) transit
Implementation Design Goals
• Overnight run time  Model Relevance
•
•
Around 12 to 16 hours
Requires distributed processing and threading via Cube Cluster
• Commodity hardware  Minimize total lifetime cost
•
Hardware available today from common vendors; reasonably priced
• Easy to Setup and Use  Staff acceptance
•
Not too complicated to setup, run, debug, etc
ARC ABM Run Times (min)
0
Network Prep, Truck Model, Initial Skims
II Demand with CT-Ramp (33% Sample)
Convert Trip Lists to Demand Matrices
Highway & Transit Assignment & Skimming
II Demand with CT-Ramp (50% Sample)
Convert Trip Lists to Demand Matrices
Highway & Transit Assignment & Skimming
1000
Highway & Transit Assignment & Skimming
Highway Assignment (AM, PM, MD, NT)
Total
3000
4000
5000
6000
7000
8000
9000
33
25
No Threading/Distribution (8 processors, 16GB RAM, 1 Computer)
1400
112
Threaded and Distributed (24 processors, 48GB RAM, 3 computers)
24
6
170
80
2100
165
36
9
170
75
4200
II Demand with CT-Ramp (100% Sample)
Convert Trip Lists to Demand Matrices
2000
310
52
13
173
75
437
100
8795
970
10000
Hardware and Software Setup
• Three Windows Server 2003
64bit Machines:
• Dual Quad Core Intel Xeon
X5570 2.93 GHz with HyperThreading  16 threads
• 32 GB of RAM
• Cube Voyager + 8 seat Cube
Cluster license
• Total cost ~ $30,000 in 2009
Hardware and Software Setup
•
•
•
•
•
64 bit OS for large memory addresses
64 bit Java for CT-RAMP
32 bit Java to integrate with Cube’s native matrix I/O DLL
Cube Base for the GUI
Cube Voyager + Cluster for running the
model, assignment, etc
• Java CT-RAMP software
• 64 bit R for reporting/visualization
Overall System Setup
• Cube runs the
show and calls
all Java
processes
• User starts the
remote
processes on the
2nd and 3rd
machine (for
now)
• Everything talks
to one mapped
network folder
location
Model Validation
Highway
Trip-Based Model
Year 2005 Volume/Count
Scatterplot
200000
180000
Year 2005 Volume/Count
Scatterplot
200000
180000
R² = 0.9489
160000
160000
140000
140000
Model Volumes
Model Volumes
Activity-Based Model
120000
100000
80000
60000
R² = 0.941
120000
100000
80000
60000
40000
40000
20000
20000
0
0
20000400006000080000100000120000140000160000180000200000
Counts
X…
0
0
20000 40000 60000 80000100000120000140000160000180000200000
Counts
X…
New Measures
New Measures
Persons Not At Home by TAZ
New Measures
Persons by TAZ
New Measures
ABM Visualization & Reporting System
Activity-Based
Model
(Java, Cube)
Database
(SQL Server)
Custom Analysis
Data Access Layer
(IIS, ASP.Net)
Visualization
Dashboard
(Flash)
Reports
(Excel)
ABM VIZ – Time Use
•
•
New time use (person activity over the day)
Can select different person types (the above is showing Full-time workers)
Radar Charts