1. Problem Statement and Motivation Key Achievements and Future Goals Technical Approach Kouros Mohammadian, PhD and Yongping Zhang (PhD Candidate), CME, UIC Prime Grant Support: Federal Highway Administration (FHWA) Household travel data is critical to transportation planning and modeling Surveys are expensive tools Emerging modeling techniques (e.g., microsimulation) need much richer datasets that do not exist in most metropolitan areas Transferring or simulating data seems to be an attractive solution A new travel forecasting modeling approach is designed and validated The new approach significantly improves the process of travel demand forecasting Using synthetically derived data found to be appealing The appeal of the approach lies in its low-cost, relative ease of use, and freely available sources of required data Improved Bayesian updating and small area estimation techniques for non-normal data Improved travel data simulation techniques Used synthesized and transferred data for model calibration and validation. Considered a large set of socio-demographic, built environment, and transportation system variables to identify clusters of households with homogeneous travel behavior Transferred cluster membership rules and cluster-based travel attributes to local areas Calibrated/Validated travel data transferability model Synthesized population for 5 counties of New York City with all their attributes Updated parameters of the transferability model using a small local sample and Bayesian updating Simulated travel attributes for the synthetic population Validated the simulated data against actual observed data

2. Problem Statement and Motivation Key Achievements and Future Goals Technical Approach Kouros Mohammadian, PhD, S. Yagi, J. Auld, and T.H. Rashidi (PhD Candidates), CME, UIC Source of Funding: NIPC/CMAP, FACID, and IGERT (NSF) Traditional four step travel demand models are widely criticized for their limitations and theoretical deficiencies These problems lead the model to be less policy sensitive than desired Travel is derived from participation in activities. This fact is not accounted for in 4-step models. Therefore, there is a need for a better modeling approach An activity-based microsimulation travel demand model is considered that simulates activity schedules for all individuals A comprehensive multi-tier activity-based microsimulation modeling system is developed. A new population synthesizer is developed. Activity scheduling/rescheduling decision rules are developed and applied to adjust the simulated daily activity patterns. Intra-household interaction rules are developed and applied to account for joint activity generation and household maintenance activity allocation problems. Transferability of activity scheduling/rescheduling decision rules across different spatial and temporal contexts are evaluated. The microsimulation model is applied to evaluate future transportation policy scenarios. The modeling framework utilizes both econometric and heuristic (rule- based) approaches All human activities are related to broad project categories which have a common goal (e.g., Work, School, Entertainment, etc.) and tasks and activity episodes that are required to reach that goal are modeled Activity participation is modeled at household/individual level (microsimulation) Explicit representation of time/space of occurrence for all travel episodes, linked to associated activities Activity scheduling model is linked to a population synthesizer, rescheduling and resource allocation models, and a regional network microsimulation and emission models

3. Problem Statement and Motivation Key Achievements and Future Goals Technical Approach Investigators: Kouros Mohammadian and Joshua Auld, CME Primary Grant Support: CTS IGERT, NSF Congestion, environmental effects and other negative impacts of transportation system are growing Mitigation needs no longer met with construction alone New solutions are generally behavioral in nature – TDM strategies, congestion pricing, etc. New generation of models which replicate decision making behavior of travel needed to evaluate next generation mitigation strategies The framework will relax the fixed order assumption in activity planning inherent in other activity-based models First of its kind long term planning dataset collected through GPS will be used to develop learning and planning models In the future, the model should incorporate a traffic simulation module directly in the travel microsimulation In the linked activity planning and traffic simulation model, route learning models should be used for individual route choices Develop activity based microsimulation model of travel behavior which directly simulates decision making process. Incorporate learning behavior and group interactions into decision making The decision making model is based on decision planning which will be observed in long-term GPS-based travel demand survey. Internet-based survey will be used to track participants movements and gain insight into activity planning