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Decomposing excess commuting: a Monte Carlo simulation approach

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  • Hu, Yujie
  • Wang, Fahui

Abstract

Excess or wasteful commuting is measured as the proportion of actual commute that is over minimum (optimal) commute when assuming that people could freely swap their homes and jobs in a city. Studies usually rely on survey data to define actual commute, and measure the optimal commute at an aggregate zonal level by Linear Programming (LP). Travel time from a survey could include reporting errors and respondents might not be representative of the areas they reside; and the derived optimal commute at an aggregate areal level is also subject to the zonal effect. Both may bias the estimate of excess commuting. Based on the 2006–2010 Census for Transportation Planning Package (CTPP) data in Baton Rouge, Louisiana, this research uses a Monte Carlo approach to simulate individual resident workers and individual jobs within census tracts, estimate commute distance and time from journey-to-work trips, and define the optimal commute based on simulated individual locations. Findings indicate that both reporting errors and the use of aggregate zonal data contribute to miscalculation of excess commuting.

Suggested Citation

  • Hu, Yujie & Wang, Fahui, 2015. "Decomposing excess commuting: a Monte Carlo simulation approach," Journal of Transport Geography, Elsevier, vol. 44(C), pages 43-52.
    • Handle: RePEc:eee:jotrge:v:44:y:2015:i:c:p:43-52
    DOI: 10.1016/j.jtrangeo.2015.03.002
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    References listed on IDEAS

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    2. Yang Zhang & Yongping Zhang & Jiangping Zhou, 2021. "A novel excess commuting framework: Considering commuting efficiency and equity simultaneously," Environment and Planning B, , vol. 48(1), pages 151-168, January.
    3. Li, Peilin & Zhao, Pengjun & Schwanen, Tim, 2020. "Effect of land use on shopping trips in station areas: Examining sensitivity to scale," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 969-985.
    4. Jiangping Zhou & Enda Murphy & Jonathan Corcoran, 2020. "Integrating road carrying capacity and traffic congestion into the excess commuting framework: The case of Los Angeles," Environment and Planning B, , vol. 47(1), pages 119-137, January.
    5. Saadi, Ismaïl & Boussauw, Kobe & Teller, Jacques & Cools, Mario, 2016. "Trends in regional jobs-housing proximity based on the minimum commute: The case of Belgium," Journal of Transport Geography, Elsevier, vol. 57(C), pages 171-183.
    6. Zhang, Hong & Xu, Shan & Liu, Xuan & Liu, Chengliang, 2021. "Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities," Journal of Transport Geography, Elsevier, vol. 91(C).
    7. Hu, Yujie & Downs, Joni, 2019. "Measuring and visualizing place-based space-time job accessibility," Journal of Transport Geography, Elsevier, vol. 74(C), pages 278-288.
    8. Yujie Hu & Joni Downs, 2020. "Measuring and Visualizing Place-Based Space-Time Job Accessibility," Papers 2006.00268, arXiv.org.
    9. Jing, Yue & Hu, Yujie, 2022. "The unequal commuting efficiency: A visual analytics approach," Journal of Transport Geography, Elsevier, vol. 100(C).
    10. Chengliang Liu & Qinchang Gui, 2016. "Mapping intellectual structures and dynamics of transport geography research: a scientometric overview from 1982 to 2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 159-184, October.
    11. Mohammad Maghrour Zefreh & Adam Torok, 2021. "Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Environmental External Costs," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    12. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.
    13. Zhou, Jiangping & Murphy, Enda, 2019. "Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 74(C), pages 223-232.
    14. Fan Gao & Jinjun Tang & Zhitao Li, 2022. "Effects of spatial units and travel modes on urban commuting demand modeling," Transportation, Springer, vol. 49(6), pages 1549-1575, December.
    15. Hu, Yujie & Wang, Changzhen & Li, Ruiyang & Wang, Fahui, 2020. "Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach," Journal of Transport Geography, Elsevier, vol. 86(C).
    16. Yujie Hu & Fahui Wang & Chester Wilmot, 2020. "Commuting Variability by Wage Groups in Baton Rouge 1990-2010," Papers 2006.03498, arXiv.org.

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