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Travel Data Simulation and Transferability of Household Travel Survey Data

Travel Data Simulation and Transferability of Household Travel Survey Data. 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

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Travel Data Simulation and Transferability of Household Travel Survey Data

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  1. Travel Data Simulation and Transferability of Household Travel Survey Data KourosMohammadian, 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 • 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 • 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.

  2. Activity-Based Microsimulation Model of Travel Demand KourosMohammadian, 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 • 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 • 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.

  3. Dynamic Scheduling Process Model: Model Framework and Data Collection Investigators: KourosMohammadian 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 • 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 • 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

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