1. Making Activity-Based Models Easier to Use
SE Florida Model User’s Group Meeting
March 16, 2018

2. Making ABMs Easier To Use
Population Synthesis
Intelligent Sampling and Zoning
Model Dashboards

3. Population Synthesis

4. A new population synthesis tool
PopulationSim
A new population synthesis tool
Easy to set up & use
Fast
Robust algorithm
Very flexible geographic definition
Repopulate feature
Open-source
Well-documented

5. Ease of use Installation No SQL database Easy to configure Python
Either download entire install from website or download required libraries and install manually
Installation comes with test data
No SQL database
Reads and writes comma-separated value text files
Easy to configure

6. A geographic cross-walk file Input household and person ‘seed’ data
Inputs
A geographic cross-walk file
Input household and person ‘seed’ data
Controls for each geography
Can be just one control file for TAZs
Can be separate controls for Districts, TAZs, MAZs, Blocks, etc.
Any control can be used (so long as it can be mapped back to seed data)
A settings file
A file that describes how to map controls to seed data

7. Example Settings File geographies: [REGION, PUMA, TRACT, TAZ]
seed_geography: PUMA
input_table_list:
- tablename: households
filename : seed_households.csv
index_col: hh_id
- tablename: persons
filename : seed_persons.csv
- tablename: geo_cross_walk
filename : geo_cross_walk.csv
- tablename: TAZ_control_data
filename : control_totals_taz.csv
- tablename: TRACT_control_data
filename : control_totals_tract.csv
- tablename: REGION_control_data
filename : scaled_control_totals_meta.csv …
output_synthetic_population:
household_id: household_id
households:
filename: synthetic_households.csv
columns:
- NP
- AGEHOH
- HHINCADJ
- NWESR
persons:
filename: synthetic_persons.csv
- per_num
- AGEP
- OCCP

8. Example control mapping file

9. The RePopulate feature does this Two options
For traffic impact studies and other sub-area applications, often you only want to create new population in a small part of the region
You don’t want to change the rest of the population
…and you might only have a few controls, like number of households by type of dwelling
The RePopulate feature does this
Two options
Add to existing population in selected geography
Replace existing population in selected geography

10. Start from an existing population
RePopulate Feature
Start from an existing population
# Input Data Tables for repop mode
# (other required tables will already have been read into the
# pipeline by the input_pre_processor step if the initial run) #
input_table_list:
- filename : repop_control_totals_taz.csv
tablename: TAZ_control_data
# Control Specification File Name for repop mode
repop_control_file_name: repop_controls.csv
The tool will output new household and person files with the modified population in the selected zones and the same exact population in the rest of the zones

11. Documentation and downloads
Wiki is here:
Includes introduction to population synthesis, user guide, links to test data, references, etc.
Code is here:
Open source!
Get a github account
Fork the repository
Hack away
Issue a pull request

12. Sub-Area Model

13. MTC “Travel Model Two” CT-RAMP Model
4,800 TAZs
40,000 MAZs
6,200 Transit Access Points (~stops)
All-streets network
7.4M persons in 2015
~20 hour runtime for 100% sample (monolithic)

14. MTC Travel Model – Marin County
103k households, 245k persons
County
TAZs
MAZs
TAPs
SanFrancisco
633
4,148
775
SanMateo
410
4,458
782
SantaClara
1,011
8,519
1,299
Alameda
1,093
8,635
1,439
ContraCosta
621
5,921
826
Solano
268
2,823
303
Napa 99
963
113
Sonoma
351
2,894
480
Marin
202
1,424
197
Total
4,688
39,785
6,214

15. Relies on MTC Travel Model Two for input data, core model structure
Marin County Model
Relies on MTC Travel Model Two for input data, core model structure
Same software
Same coefficients (currently)
Same network (currently)
Same land-use data
Uses intelligent sampling to reduce runtime and reduce Monte Carlo variance
Aggregates geography outside of Marin County to reduce runtime

16. Flows into/out of Marin County
Work Tours
Non-Work Tours
All Tours
Marin Residents
Marin Workers
Marin Origin
Marin Dest.
San Francisco
26% 2% 5% 4%
10% 3%
San Mateo 1% 0%
Santa Clara
Alameda 7%
Contra Costa
11%
Solano
Napa
Sonoma
19%
Marin
57%
59%
87%
92%
80%
84%
Greatest interactions:
Marin residents commuting to work in San Francisco & Sonoma Counties
Sonoma and Contra Costa County residents working in Marin

17. Transit Trip Flows

18. Household Sampling For Non-Marin TAZs
Household Sample Rate Follows Work Tour Frequency Distribution
Sampling Applied by Household Size and Income Bin in each TAZ

19. Distance to Marin (miles)
Household Sampling
Distance to Marin (miles)
Total Households
Total Population
Sample Rate
Sample Households
Sample
Population
From To 3
22,287
58,173
50%
11,144
29,087 5
2,607
5,767
1,304
2,884 10
305,699
688,315
40%
122,280
275,326 15
212,816
577,672
30%
63,845
173,302 20
234,863
586,827
20%
46,973
117,365 30
385,399
1,043,574
10%
38,540
104,357 40
402,924
1,132,612
40,292
113,261 50
379,713
1,041,735 5%
18,986
52,087 60
373,566
1,078,749
18,678
53,937
Over 60 miles
184,937
590,313
9,247
29,516
Marin County
103,205
245,610
300%
309,615
736,830
Total
2,711,221
7,294,957
680,902
1,687,951
25% of total households, with 3x oversampling in Marin

20. Uses 1:1 representation of MAZs for Marin County and San Francisco
Aggregated Geography
Uses 1:1 representation of MAZs for Marin County and San Francisco
important for transit to/from SF
Collapses MAZs to TAZs outside those counties to speed up accessibility and destination choice calculations

21. User provides input sampling probability file by TAZ
Implementation
User provides input sampling probability file by TAZ
Change token in batch file to indicate county application
Code does the rest!
Collapses land-use data
Collapses geography
Creates new network centroids, connectors
Samples households in synthetic population
Runs model

22. Procedure implemented MTC TM2 still being calibrated
Project Status
Procedure implemented
MTC TM2 still being calibrated
Possible calibration adjustments required
Testing runtime improvements
Assignment still long runtime - may require network trimming
Harder to automate – maybe regional network versus local network designations
Location sampling may be truncated
Work shadow pricing may need to be adjusted
Target completion date end of 2018!

23. Scenario Comparison Tool

24. AB Model Scenario Comparison Tool
Use requirements
Compare two different model runs
Compare a model run to observed data (calibration)
No database, no web hosting
Solution
Lightweight AB dashboard written in R
Creates static HTML file that can be opened with a browser
Runs automatically every time model is run (need to specify a reference run)
Developed for Oregon DOT, applied to MTC and SANDAG models

25. Contacts www.rsginc.com Joel Freedman Joel.freedman@rsginc.com
Director