Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) Model ADAPTS scheduling process model: –Simulation of how activities are planned.
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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
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