Disaggregate Journey-to-Work Data

Implications for Excess Commuting and Jobs-Housing Balance

Bibliographic Data

ID4139876
AuthorsMorton E O’kelly (0000-0002-8967-9771, Department of Geography, Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210-1361, USA, corresponding author), Morton E O'Kelly, Wook Lee (0000-0003-4646-7012, Department of Geography, Texas Center for Geographic Information Science, Texas State University—San Marcos, 601 University Drive, San Marcos, TX 78666-4616, USA, corresponding author)
Year2005
Volume37
Issue12
Pages2233-2252
Publication date2005-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning A Economy and Space (JOURNAL)
Journal identifiersISSN: 0308-518X • E-ISSN: 1472-3409
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1068/a37312
OpenAlexW2163705730
LanguageEN
Citations received52
References cited30

Much of the analysis to date on the topic of excess commuting and jobs-housing balance deals with total commuting flow, undifferentiated with respect to worker and job characteristics. In this paper we explicitly address the disaggregation issue in terms of job and worker heterogeneity and show how to incorporate such details into the analysis of excess commuting. The objectives of this paper are (1) to develop a trip-distribution model disaggregating journey-to-work data according to type of occupation in order to estimate actual commutes; (2) to develop a disaggregated version of a linear program to measure theoretical minimum and maximum commutes; and, (3) to verify variations in excess commuting and jobs-housing balance according to type of occupation. Results of actual trip-length distributions for each occupation vary from 3.72 to 5 miles for Boise, Idaho, and from 4.27 to 7.78 miles for Wichita, Kansas. Minimum commutes vary from 0.95 to 3.58 miles and from 1.5 to 3.79 miles for Boise and Wichita, respectively. These results imply nonuniform levels of excess commuting and jobs/workers ratios. The proposed models are expected to have a wide range of uses in measurement and assessment of empirical patterns of commuting. The scope of the disaggregation can be extended to other targets, such as different types of industry, household structure, income level, ethnic background, education level, transportation mode, and gender. Further dimensions of disaggregation can address spatial interactions of different socioeconomic groups in urban areas, and, more generally, contribute to exploring urban sprawl according to job characteristics and industries

Demographic economics · Econometrics · Economics · Geography · Population · Socioeconomic status · Sociology · Demography · Engineering · Mathematics · Psychology · Transportation Planning and Optimization · Urban Transport and Accessibility · Urban, Neighborhood, and Segregation Studies

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