Trip Generation Socioeconomic Variables. TAZ variables for trip generation in FLSWM include population, percept households by size (i.e., persons per household, from 1 to 5+), dwelling units by types (i.e., single family, multi-family, hotel/motel), percept households by auto-ownership (i.e., autos per household, 0, 1, and 2+), and employment by types (i.e., retail, service, and total employment). These variables are effective for evaluating future development scenarios involving difference assumptions of population and employment growth. However, emerging socioeconomic trends suggest that socioeconomic variables other than population, households by size and auto-ownership, and employment may significantly change trip generation in the future. NCHRP report 750 (2014b) identified eight new socioeconomic trends, including slow population growth, aging population, structural changes in population by ethnicity, older and more diverse workforce, blurring of city and suburb, slow growth in households, increasing users of communication technologies, and salience of environmental concerns. Some of these trends share common drivers, such as aging population, longer life span, lifestyle choices of younger generations (e.g., delaying marriage and childbearing and urban lifestyle preference, etc.), and immigration. These sociodemographic trends may result in declining trip rate and VMT per capita, decreased auto ownership, increases in carpooling, and increases in non-motorized trips. While other forces may lead to contradicting effects, such as the use of transit, which may decrease with age, but can increase as Hispanics (Xxx and Xxxxxxx, 2012) and Millennials (Xxxxxxxx et al., 2016) become a larger portion of the population. Lack of consideration for sociodemographic variables such as age and income level in the process of trip generation also limits the value for a travel demand model to be used for evaluation of ACES scenarios because adoption of these technologies is likely to be heavily influenced by the ages and income levels of the household heads. Future updates of FLSWM may consider re- structuring of the trip generation model to include other critical socioeconomic variables in the model. These additional variables have the potential of increasing both the precision and capability of future traffic prediction for FLSWM.
