ARC Activity-Based Model - Features, Visualization & QA/QC

ARC Activity-Based Model - Features, Visualization & QA/QC For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

Today’s Menu • Overall features of ARC’s Activity-Based Model (ABM) • ABM Visualization Software, ABMVIZ • Quality Assurance/Quality Control of ABM

Daily Travel • Trip-Based Model • - Home-Work: 2 trips • - Work-Eat: 2 trips • - Home-Gym: 2 trips • Activity-Based Model • - Follows daily activity • patterns (departure • time, duration, location, • frequency, mode)

What is Activity-Based Model? • ABM aims at predicting which activities are conducted where, when, for how long, with whom, the transportation mode involved and ideally also the implied route decisions • Disaggregate, Micro-simulation, Behavioral, Tour-based • ABM reflects the scheduling of activities in time and space

Structure of ARC Models: TBM vs. ABM Trip Generation Trip Distribution Mode Choice Route Choice Trip-Based Model Activity-Based Model Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Demand Supply

Population Synthesizer • Generates synthetic population to represent actual households and population • Base year • Input: Census data (marginal distributions of various household control variables), PUMS • Control variables: householder age, HH size, HH income, presence of children in HH, number of workers in HH • Joint distribution through Frata • PUMS 5% sample as seed matrix, control totals from Census • Draws from PUMS households from the joint distribution • 1 record/hh and 1 record/person

Population Synthesizer (cont’d) • Forecast year • Input: ARC lane-use forecast, PUMS • Control variables: HH size, HH income, householder’ age, number of workers in HH • Joint distribution through Frata • Base year distribution as seed matrix, control totals from land-use forecasts • Draws from PUMS households from the joint distribution • 1 record/hh and 1 record/person

Long-Term Choices • Mandatory activity location choice • Work/school/university locations for the synthesized population • Work location choice for workers • Grade school for persons age 5-12 • University for university students • Multinomial logit: [subzones]=[person characteristics, size terms, mc logsums, distance, etc.] • Car ownership model • Number of vehicles owned by each household • Multinomial logit: [# cars]=[hh size, income, parking cost, mc logsums, etc.]

Coordinated Daily Activity Pattern • Generates personal DAPs and individual tours by purpose for all synthesized population • DAPs • Mandatory, Non-mandatory & At-home patterns • Decision-making unit: Households • Multinomial logit: [# DAPs]=[person/hh characteristics, accessibility measures, intra-household interaction terms, etc.] • 363 alternatives: 3 (1-p hh), 9 (2-p hh), 27 (3-p hh), 81 (4-p hh), 243 (5-p hh)

Tour Models • Predicts the number and purpose of tours for each person, destinations, and time-of-day choices • Four different tours Individual Mandatory Residual Time Joint Non-Mandatory At-Work Sub-Tours Individual Non-Mandatory

Individual Mandatory Tour • Tour Frequency • Number and purpose of tours for each person • Multinomial logit: [# of work/school tour]=[hh composition, income, car ownership, location of work/school activities, accessibility, etc.] • Tour Time-of-Day • Select the combinations of tour departure/arrival time • Multinomial logit: [combination of tour departure/arrival hours]=[household and personal characteristics, network LOS variables, etc.] • Alternatives: 190 combinations of tour departure hour and arrival hour back at home

Joint Non-Mandatory Tour • Joint tours by household members after mandatory tours have been generated and scheduled • Joint Tour Frequency • Generates the number/purposes of joint tours • Multinomial logit: [0, 1 or 2 tours by purpose]=[household variables, accessibility, overlapping time windows, etc.] • Joint Tour Composition • Determines the person types participating in the tour • Multinomial logit: [combination of adults & children]=[household characteristics, purpose of joint tour, overlapping time windows]

Joint Non-Mandatory Tour (cont’d) • Joint Tour Primary Destination Choice • Location of the tour primary destination • Multinomial logit: [subzones]=[household/person characteristics, tour purpose, size variables, mc logsum, distance, etc.] • Joint Tour Time-of-Day Choice • Tour departure time from home and arrival time back at home • Multinomial logit: [combination of tour departure/arrival hours]=[household and personal characteristics, network LOS variables, etc.] • Alternatives: 190 combinations of tour departure hour and arrival hour back at home

Other Tours • Individual Non-Mandatory Tour • Tour Frequency • Tour Primary Destination Choice • Tour Time-of-Day Choice • At-Work Sub-Tour • Tour Frequency • Tour Primary Destination Choice • Tour Time-of-Day Choice

Tour Mode Choice • Tour mode choice: main tour mode used from origin to primary destination and back • Two-level mode choice in ARC ABM • Tour mode level (upper-level choice) • Trip mode level (lower-level choice conditional on the upper-level)

Tour Mode Choice (cont’d) • Tour MC models • Work, University, K-12, Non-mandatory, At-work • 12 Alternatives • Nested logit: [tour mode]=[household and personal characteristics, urban form variables, network LOS variables, etc.] • Use the round-trip LOS between the tour anchor location and the primary destination

Intermediate Stop Models • Stop Frequency Model • Number of intermediate stops on the way to/from the primary destination by tour purpose • Multinomial logit: [# of stops]=[household and personal characteristics, tour duration, tour distance, accessibility, etc.] • Stop Location Choice Model • Location of stops along the tour other than the primary destination • Multinomial logit: [Subzones]=[mc logsum, distance, size variables, etc.]

Trip Mode Choice • Determines the mode for each trip along the tour • Constrained by the main tour mode • Correspondence rules to determine which trip modes are available for which tour modes • E.g., drive-alone pay trip is only available for drive-alone pay tour • E.g., transit tours can include auto shared-ride trips for particular legs

Route Choice • Same routine as the trip-based model • Multimodal User Equilibrium Time-of-Day Assignment • Bi-Conjugate Frank-Wolfe for both TBM and ABM, departing from the traditional Frank-Wolfe

Run Environment • Java-Package • Cube/TP+ • Three 64-bit Windows machines • Each machine with 32GB of RAM • Base year run: approx. 30 hours • 2040 run: approx. 50 hours

Visualization • Model generates huge database • Model visualization system, ABMVIZ • Primary starting point for most model analysis questions • Interactive/dynamic visualization of model estimates/results • Some unique visualization types • Tables, Bar Charts/Maps • Time Use • Tour Tracing • Tree Map • Radar Charts

Quality Assurance/Quality Control • Quality Assurance (QA): a systematic review process by personnel not directly involved in model development • Quality Control (QC): a technical routine to control quality of the model performed in model development • Full understanding of the models’ capabilities/limits • ARC initiated internal a year-long QA/QC process for both ABM and TBM • New QA/QC guidelines

QA/QC • Overall processes • Reasonableness checking for EVERY modeling step • Temporal validation between base and forecast years • Comparability between ABM and TBM • Components • Modeling flows/Scripts • Socioeconomic data • Transportation network data • External trips • Trip generation, Trip distribution, Mode choice, & Traffic assignment

QA/QC (cont’d) • Tools • SQL Express Management Studio • STATA • ABMVIZ • Custom scripts • Voyager/TP+ • R • Our brain

Thank You!

ARC Activity-Based Model - Features, Visualization & QA/QC