Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) Model

ADAPTS scheduling process model:– Simulation of how activities are planned and scheduled– Extends concept of “planning horizon” to activity attributes– Time-of-day, location, mode, party composition

Fits within overall framework of activity-based microsimulation model

– Constraints from long-term simulation (land-use model)– Simulates 28 days of activity scheduling and execution

Combined with disaggregate Dynamic Traffic Assignment model to provide continuous time and dynamic representation of travel demand

Models being generated for Chicago region– Datasources: UTRACS (GPS) Survey, CMAP household travel survey,

CMAP land-use database, Census 2000, CHASE, etc.

Kouros Mohammadian, UIC

ADAPTS Simulation Framework

Information Flow

Simulation Flow

This process is repeated for each individual for 15 minute timesteps for 28 simulated days.

This process is repeated for each individual for 15 minute timesteps for 28 simulated days.

Household Planning

Individual Planning

Household Schedule

Household Memory

Social Network

Individual Schedules

Individual Memory

Land Use

Network LOS

InstitutionalConstraints

Initialize Simulation•Initialize World•Synthesize Population•Generate routines

For each timestep

Write Trip Vector

Traffic Assignment

Information FlowSimulation Flow

Dynamic traffic assignment with detailed network representation

Dynamic traffic assignment with detailed network representation

More detailed representation of region needed here

More detailed representation of region needed here

Kouros Mohammadian, UIC

Results and Visualization

ADAPTS gives a (near) continuous time representation– Origin-destination flows at 15 minute intervals and,

– Trip purposes, mode types, etc. for each trip

– Continuous time representation of link loads

Results are highly disaggregate– Sensitive to many policies impacting behavior

– Makes visualization and interpretation difficult

Therefore, need visualization techniques to communicate results of analysis effectively– 3D city model combined with disaggregate activity data gives high

quality, detailed picture of regional travel, policy implications, etc.

Kouros Mohammadian, UIC

Current Results Visualization Movie

Kouros Mohammadian, UIC

UTRACS: Urban Travel Route and Activity Choice Survey

Internet enabled and entirely automated– Participants upload data to central server– Survey completed on same day as data acquisition

Scans data to generate interactive PR survey– Utilize Google Maps API– Activity timeline

Participants validate activity/travel episodes

Survey activity-travel attributes– Who with, planning horizons, location choices, route and mode

choice decisions

Incorporate learning algorithms to reduce survey burden– Suggest answers known with some confidence– Remove questions when answers known with high confidence– Proactively identify likely upcoming activities and prompt for

planning data– Pre-populate planning items for learned recurrent activities

Kouros Mohammadian, UIC

Demonstration:Activity-travel verification

Kouros Mohammadian, UIC

Demonstration:Activity Episode Questions

Kouros Mohammadian, UIC

Demonstration:Travel Episode Questions

Kouros Mohammadian, UIC

Current Status of UTRACS Survey

Completed initial implementation of survey on 100 households between April and December 2009 in Chicago– Data on over 4500 trips and nearly 5000 individual activities– Detailed data regarding activity planning and scheduling process

Updates to UTRACS design– Port survey code to smartphones / PDAs to remove need for

separate data acquisition device– Real-time transmission of data to server to reduce processing

time

Potential future applications of UTRACS– Route choice, wayfinding behavior observations– Evaluation of how LBS, mobile, targeted ads, etc. can influence

behavior

Kouros Mohammadian, UIC