Modeling and Forecasting Household Workers by Occupation in Metropolitan Areas--A Mesoscopic Framework

The need for activity based models to provide micro, disaggregate simulations of travel patterns have become increasingly important to understand the complexity involved with travel behavior. Traveler occupation is one of the factors that are determinative of a trip end. To fully model how travel behavior will be influenced in the future, it is imperative to be able to estimate future occupation. The current literature does not provide suitable methods to model and forecast occupation. Two methods have primarily been used in the past to model occupation; the cohort-component method or a population synthesizing approach. The cohort-component method requires a significant amount of detailed birth, aging, death and migration information and the results obtained are at an aggregate geographic level. Such data at larger geographies (macro level) may not be suitable for advanced travel demand modeling purposes. Occupation synthesizers are used to obtain individual information at any geographic level (micro-level), but suffer from a limitation of evolution of occupation over time while considering other depend variables such as employment, and other household characteristics. In this paper, the authors propose a mesoscopic approach where occupation by employment type evolves over a time period using a logistic regression technique. Five types of occupation: management, sales, service, other and unemployed is modeled. The methodology is presented in three steps: coefficient estimation, forecast and validation. First, the occupation evolution trend from 1990 to 2000 is analyzed. The estimation result is applied to forecast 2010 and 2030 occupation composition. This evolutionary model is applied to the Baltimore Metropolitan Council (BMC) region based on 1990 and 2000 Census data then validated with 2010 Census data. The results show that the proposed model produces a forecast that reliable and accurate. The important insights gained from this study are: (1) this model provides a good estimation and forecast for management, sales and unemployment; (2) service and other occupation prove less predictable as evolution trends among these groups are not consistent over time. The proposed tool can be adapted for use by small and large scale planning agencies to prepare detailed socio-economic and demographic profiles for input data into a population synthesizer or activity based model.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADB40 Transportation Demand Forecasting.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Zhu, Xiaoyu
  • Mishra, Sabyasachee
  • Welch, Timothy F
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 15p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Activity choices; Demographics; Forecasting; Households; Logistic regression analysis; Occupations; Travel behavior; Travel demand; Travel patterns
• Identifier Terms: Baltimore Metropolitan Council
• Subject Areas: Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01516086
• Record Type: Publication
• Report/Paper Numbers: 14-1227
• Files: TRIS, TRB, ATRI
• Created Date: Feb 27 2014 9:05AM