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Accident Prediction Model Applied to Motorway A29 in Portugal

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Testing and Experimentation in Civil Engineering (TEST&E 2022)

Part of the book series: RILEM Bookseries ((RILEM,volume 41))

Abstract

Road accidents are still one of the biggest public health problems in the world, and research must be stepped up to tackle the issue. The present study describes the development of a statistical model for predicting accidents occurring on motorway segments over the period 2015–19. The case study comprises 218 tangents and curves, separately for either direction, of motorway A29 in Portugal, which is operated by Ascendi under a concession awarded by the Portuguese state. Exposure, geometric design variables, and annual trend were incorporated to the model, considering a negative binomial probability distribution, and a flexible function form allowing non-constant elasticity values was utilized. In addition to the exposure variables, the geometric design risk factors found to be significant were the horizontal curvature, and the presence of deceleration lane. A significant decreasing in accident annual trend was also observed. The fitted model was validated using cumulative residual plots. The results obtained allowed the prediction of accident frequency as well as the identification of the design risk factors.

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Acknowledgements

The authors gratefully acknowledge the financial support of Universidad Politécnica de Madrid (PROGRAMA PROPIO DE I+D+I 2021, short-term research stay September–December 2021). The authors would also like to thank the support of ASCENDI IGI—Inovação e Gestão de Infraestruturas, S.A. for providing the data used in this study.

Author information

Authors and Affiliations

  1. CITTA—Centro de Investigação do Território Transportes e Ambiente, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    Sara Ferreira, António Couto & António Lobo

  2. Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    Suellen Souza

  3. Departamento de Estructuras y Física de Edificación, Universidad Politécnica de Madrid, Madrid, Spain

    César De Santos-Berbel

  4. Ascendi IGI—Inovação e Gestão de Infra-Estruturas S.A., Porto, Portugal

    João Neves

Authors
  1. Sara Ferreira
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  2. António Couto
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  3. António Lobo
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  4. Suellen Souza
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  5. César De Santos-Berbel
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  6. João Neves
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Corresponding author

Correspondence to Sara Ferreira .

Editor information

Editors and Affiliations

  1. CERIS and Department of Civil Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    Carlos Chastre

  2. CERIS and Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    José Neves

  3. CONSTRUCT and Department of Civil Engineering, School of Engineering, Polytechnic of Porto, Porto, Portugal

    Diogo Ribeiro

  4. CERIS and Department of Civil Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    Fernando F. S. Pinho

  5. UNIDEMI and Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    Hugo Biscaia

  6. CERIS and Department of Civil Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    Maria Graça Neves

  7. CERIS and Department of Civil Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    Paulina Faria

  8. CERIS and Department of Civil Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal

    Rui Micaelo

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Ferreira, S., Couto, A., Lobo, A., Souza, S., De Santos-Berbel, C., Neves, J. (2023). Accident Prediction Model Applied to Motorway A29 in Portugal. In: Chastre, C., et al. Testing and Experimentation in Civil Engineering. TEST&E 2022. RILEM Bookseries, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-031-29191-3_28

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  • DOI: https://doi.org/10.1007/978-3-031-29191-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29190-6

  • Online ISBN: 978-3-031-29191-3

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