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A Probabilistic Travel Time Modeling Approach Based on Spatiotemporal Speed Variations

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Smart Cities, Green Technologies and Intelligent Transport Systems (SMARTGREENS 2018, VEHITS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 992))

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Abstract

The rapid development and deployment of Intelligent Transportation Systems (ITSs) require the development of data driven algorithms. Travel time modeling is an integral component of travel and transportation management and travel demand management functions. Travel time has a massive impact on driver’s route choice behavior and the assessment of the transportation system performance. In this paper, a mixture of linear regression is proposed to model travel times. The mixture of linear regression models has three advantages. First, it provides better model fitting compared to simple linear regression. Second, the proposed model can capture the bi-modal nature of travel time distributions and link it to the uncongested and congested traffic regimes. Third, the means of the bi-modal distributions are modeled as functions of the input predictors. This last advantage allows for the quantitative evaluation of the probability of each travel time state as well as the uncertainty associated with each state at any time of the day given the values of the predictors at that time. The proposed model is applied to archived data along a 74.4-mile freeway stretch of I-66 eastbound to connect I-81 and Washington D.C. The experimental results show the ability of the model to capture the stochastic nature of the travel time and gives good travel time predictions.

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Author information

Authors and Affiliations

  1. Centre for Accident Research and Road Safety, University of Technology at the Queensland, Queensland, Australia

    Mohammed Elhenawy

  2. Bradley Department of Electrical and Computer Engineering, Electrical and Computer Engineering, MC 0111, 1185 Perry St., Blacksburg, 24061, VA, USA

    Abdallah A. Hassan & Hesham A. Rakha

  3. Charles E. via, Jr. Department of Civil and Environmental Engineering, Blacksburg, VA, 24061, USA

    Hesham A. Rakha

  4. Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA, 24061, USA

    Hesham A. Rakha

Authors
  1. Mohammed Elhenawy
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  2. Abdallah A. Hassan
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  3. Hesham A. Rakha
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Corresponding author

Correspondence to Hesham A. Rakha .

Editor information

Editors and Affiliations

  1. School of Business, Maynooth University, Maynooth, Ireland

    Brian Donnellan

  2. CT RDA SSI, Siemens AG, Munich, Bayern, Germany

    Cornel Klein

  3. Dublin City University, Dublin, Ireland

    Markus Helfert

  4. Ford Research and Advanced Engineer, Dearborn, MI, USA

    Oleg Gusikhin

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Elhenawy, M., Hassan, A.A., Rakha, H.A. (2019). A Probabilistic Travel Time Modeling Approach Based on Spatiotemporal Speed Variations. In: Donnellan, B., Klein, C., Helfert, M., Gusikhin, O. (eds) Smart Cities, Green Technologies and Intelligent Transport Systems. SMARTGREENS VEHITS 2018 2018. Communications in Computer and Information Science, vol 992. Springer, Cham. https://doi.org/10.1007/978-3-030-26633-2_17

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  • DOI: https://doi.org/10.1007/978-3-030-26633-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26632-5

  • Online ISBN: 978-3-030-26633-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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