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Explaining Intraurban Variations of Commuting by Job Proximity and Workers' Characteristics

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  • Fahui Wang

    (Department of Geography, Northern Illinois University, DeKalb, IL 60115-2854, USA)

Abstract

Based on the Census for Transportation Planning Package (CTPP) 1990, in this paper I will attempt to explain intraurban variations of commuting time and distance in Columbus, OH. Whereas commuting times are directly available from the CTPP, commuting distances are recovered by GIS network techniques as the shortest-time distances through the urban road network. Explanations of commuting are first attempted by measures of job proximity such as distances from employment centers and a gravity-based index accounting for job competition among workers. The influence of employment centers on commuting sheds new light on polycentric urban models. The variation of commuting distance is well explained by the gravity-based job proximity index. That is to say, how far people commute is explainable to a large extent by urban land use. However, how much time people spend on commuting is complicated by their socioeconomic status, which affects their choice of commuting modes. Finally the research demonstrates the promise of explaining commuting times by workers' characteristics such as percentage of black workers, female labor participation ratio, percentage of multiple-worker households, home-ownership status, educational attainment, and wage rate. In the analyses, spatially lagged dependent variables are introduced to control for the effect of spatial autocorrelation.

Suggested Citation

  • Fahui Wang, 2001. "Explaining Intraurban Variations of Commuting by Job Proximity and Workers' Characteristics," Environment and Planning B, , vol. 28(2), pages 169-182, April.
    • Handle: RePEc:sae:envirb:v:28:y:2001:i:2:p:169-182
    DOI: 10.1068/b2710
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    References listed on IDEAS

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    1. Keith R. Ihlanfeldt & David L. Sjoquist, 1991. "The Effect of Job Access on Black and White Youth Employment: A Cross-sectional Analysis," Urban Studies, Urban Studies Journal Limited, vol. 28(2), pages 255-265, April.
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    3. David Merriman & Toru Ohkawara & Tsutomu Suzuki, 1995. "Excess Commuting in the Tokyo Metropolitan Area: Measurement and Policy Simulations," Urban Studies, Urban Studies Journal Limited, vol. 32(1), pages 69-85, February.
    4. Wachs, Martin & Taylor, Brian D. & Levine, Ned & Ong, Paul, 1993. "The Changing Commute: A Case Study of the Jobs/Housing Relationship over Time," University of California Transportation Center, Working Papers qt7424635r, University of California Transportation Center.
    5. repec:cdl:uctcwp:qt2ss7x5b1 is not listed on IDEAS
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    Cited by:

    1. Hu, Yujie & Wang, Fahui, 2015. "Decomposing excess commuting: a Monte Carlo simulation approach," Journal of Transport Geography, Elsevier, vol. 44(C), pages 43-52.
    2. Zhao, Pengjun & Lü, Bin & Roo, Gert de, 2011. "Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era," Journal of Transport Geography, Elsevier, vol. 19(1), pages 59-69.
    3. Andrew R. Watkins, 2016. "Commuting Flows and Labour Market Structure: Modelling Journey to Work Behaviour in an Urban Environment," Growth and Change, Wiley Blackwell, vol. 47(4), pages 612-630, December.
    4. Motte, Benjamin & Aguilera, Anne & Bonin, Olivier & Nassi, Carlos D., 2016. "Commuting patterns in the metropolitan region of Rio de Janeiro. What differences between formal and informal jobs?," Journal of Transport Geography, Elsevier, vol. 51(C), pages 59-69.

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