Using Work Location and Industry Classification in the Weighting of Household Surveys
Corresponding Author: Anurag Komanduri, Cambridge Systematics, Inc.
Presented By: Karthik Konduri , Cambridge Systematics, Inc.
Abstract
Activity-based models (ABM) that simulate travel are becoming commonplace owing to their value in supporting policy-driven scenario analyses. Due to the complex nature of these ABMs, only a limited number of data sources provide the detail necessary for the estimation and calibration of these models. Among them, household travel surveys are becoming increasingly central to the development and calibration of ABMs. ABMs mimic rational decision-making and use a hierarchical decision-making framework that prioritizes mandatory travel and activities first which results in a constrained time interval for other non-mandatory activities. Therefore, it is critical that expanded household surveys represent mandatory travel and activity characteristics accurately.
Traditionally, household surveys have been expanded to match household-level demographics such as household size, and number of vehicles. More recent weighting frameworks have included person-level demographics such as worker status, and age. However, there is limited research (if any) looking into the role of employment-level attributes (e.g. journey to work flow data) within the weighting procedure.
The authors build on the body of work in the areas of survey expansion, and synthetic population generation to incorporate two employment-level variables: industry-level employment totals, and home-to-work flow patterns in the expansion process. Further, the employment-level variables are matched at different spatial resolutions, namely, (a) industry-level employment totals at the region-wide level, and (b) home-to-work patterns at a sub-regional level to improve travel duration distributions. The resulting weights are then contrasted against results from traditional expansion methods by summarizing a variety of variables suitable for model validation.
This procedure was applied to three different areas: (a) Memphis - which is a mid-to-large city with limited transit usage, (b) Minneapolis - which is a large city with high transit ridership compared to other cities of that size, and (c) The State of Connecticut which has considerable job interchange with New York City, and where residents rely on a combination of transit and auto modes to commute both within the State and also outside.
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