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

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

Population Synthesis

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

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

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

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

Example control mapping file

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

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

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

Sub-Area Model

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)

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

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

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

Transit Trip Flows

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

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

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

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

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!

Scenario Comparison Tool

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

Contacts www.rsginc.com Joel Freedman Joel.freedman@rsginc.com Director Joel.freedman@rsginc.com 503-200-6602 www.rsginc.com