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Modeling the endogeneity of lane-mean speeds and lane-speed deviations: a structural equations approach

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  • Shankar, Venkataraman
  • Mannering, Fred

Abstract

This paper attempts to macroscopically address endogeneity issues related to lane-mean traffic speeds and lane-speed deviations. Methodologically, we seek to provide a better understanding of mean speeds and speed deviations across the lanes of a multilane highway. In so doing, the work may eventually be applied to better understand highway safety and the effects that lane-mean and lane speed deviations have on highway safety. We propose a structural model that relates mean speed and speed deviations by lane and is contemporaneously influenced by environmental, temporal, and traffic flow factors. Spot speed and vehicle classification data measured by lane in both the eastbound and westbound directions of Interstate 90 (I-90) in Washington State are used to develop the empirical relationships. The findings show that lane-mean speeds are endogenously related with adjacent lane speeds and exogenously related with associated environmental, traffic flow and temporal factors, while lane-speed deviations are endogenously related not only with adjacent lane speed deviations but also, through forward causality, lane-mean speeds and exogenously related with environmental, traffic flow and temporal factors as well. The approach shows significant promise in unraveling cause-effect relationships affecting macroscopic traffic flow continuums.

Suggested Citation

  • Shankar, Venkataraman & Mannering, Fred, 1998. "Modeling the endogeneity of lane-mean speeds and lane-speed deviations: a structural equations approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(5), pages 311-322, September.
    • Handle: RePEc:eee:transa:v:32:y:1998:i:5:p:311-322
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    Cited by:

    1. Haruna Sekabira & Shamim Nalunga, 2020. "Farm Production Diversity: Is it Important for Food Security, Dietary Diversity and Nutrition? Panel Data Evidence from Uganda," Working Papers 396, African Economic Research Consortium, Research Department.
    2. Coifman, Benjamin A. & Mallika, Ramachandran, 2007. "Distributed surveillance on freeways emphasizing incident detection and verification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 750-767, October.
    3. Park, Young-Jin & Saccomanno, Frank F., 2006. "Evaluating speed consistency between successive elements of a two-lane rural highway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(5), pages 375-385, June.
    4. Tremblay, Jean-Michel & Cirillo, Cinzia & Bassani, Marco, 2021. "Updating and transferring Random Effect models: The case of operating speed percentile estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 286-304.
    5. Cheng, Wen & Gill, Gurdiljot Singh & Sakrani, Taha & Ralls, Dennis & Jia, Xudong, 2018. "Modeling the endogeneity of lane-mean speeds and lane-speed deviations using a Bayesian structural equations approach with spatial correlation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 220-231.
    6. Haruna Sekabira & Shamim Nalunga, 2020. "Farm Production Diversity: Is It Important for Dietary Diversity? Panel Data Evidence from Uganda," Sustainability, MDPI, vol. 12(3), pages 1-21, January.
    7. Ponnu, Balaji & Coifman, Benjamin, 2017. "When adjacent lane dependencies dominate the uncongested regime of the fundamental relationship," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 602-615.
    8. Himes, Scott C. & Donnell, Eric T. & Porter, Richard J., 2013. "Posted speed limit: To include or not to include in operating speed models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 23-33.

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