BMC Regional Travel Demand Model Update: Development of Synthetic Population Generator

Project: Research project

Project Details


BMC Regional Travel Demand Model Update: Development of Synthetic Population Generator BMC Regional Travel Demand Model Update: Development of Synthetic Population Generator The Arizona State University (ASU), in collaboration with Resource Systems Group, Inc. (RSG) and the University of Maryland National Center for Smart Growth (NCSG), is pleased to present a proposal in response to the Request for Proposals (RFP) issued by the (BMC) for the development of a Synthetic Population Generator (SPG) as part of its Regional Travel Demand Model Update. Professor Chandra Bhat of the University of Texas at Austin and Professor Konstadinos Goulias of the University of California at Santa Barbara are on the team in the role of expert consultants with a view to tap into their expertise on the development of multimodal activity-based microsimulation model systems in large-scale urban contexts. The team that has been assembled brings a wealth of knowledge and experience in implementing cutting-edge user-friendly transportation modeling software, developing innovative transportation modeling methods and tools to solve emerging planning needs, and providing transportation data management and support services. Over the past two decades, the members of the team have developed an impressive portfolio of accomplishments in the development of state-of-the-art activity-based travel demand modeling tools, including a Synthetic Population Generator software package called PopGen, which is an open source user-friendly model system being used by planning agencies and consultants around the country in conjunction with advanced model development and deployment initiatives. The team looks forward to the opportunity to work closely with BMC in advancing its goals of implementing state-of-the-art activity-based transportation modeling and data enterprise systems. The move towards advanced activity- and tour-based travel microsimulation model systems has largely been motivated by three fundamental developments in the field over the past few decades. First, there has been an increasing need to use models for addressing a range of emerging policy and modal scenarios, including for example, variable road pricing scenarios, greenhouse gas (GHG) emission reduction strategies, alternative work arrangements (telecommuting and flexible work hours), investments in rail and bus rapid transit systems, land use policies that encourage transit-oriented and mixed use development patterns, and the aging of the population. Behavioral responses to these emerging policy scenarios depend on a myriad of factors including a range of constraints and interactions that shape activity-travel patterns of individuals. Individuals are constrained by work schedules, household obligations and interactions, modal system availability, and spatio-temporal bounds that inevitably affect emergent activity-travel patterns. The substantive presence of behavioral heterogeneity in the population, which at least partially arises from the fact that constraints and interactions that govern peoples choices vary considerably across individuals, calls for the disaggregate representation of behavioral units (agents) in the simulation of activity-travel demand. A synthetic population generator is able to provide the disaggregate population data needed to apply an activity-based travel microsimulation model. Thus, the development of a synthetic population generator is a necessary and critical first step in the transition to activity-based travel model systems. The second and third developments that have motivated the move towards activity-based travel microsimulation models are related to the availability of computing power to run such models with reasonable computational efficiency, and the availability of rich data that aids in the calibration and validation of disaggregate microsimulation models of trip chaining (tour formation) behavior in the time-space domain. Over the past few years, advances in computational algorithms, analytical methods, and hardware and software technology have made it possible to run advanced mic
Effective start/end date11/8/106/30/11


  • US Department of Transportation (DOT): $115,000.00


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