IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7580-d1139892.html
   My bibliography  Save this article

A Review of Surrogate Safety Measures Uses in Historical Crash Investigations

Author

Listed:
  • Dimitrios Nikolaou

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Heroon Polytechniou Str., GR-15773 Athens, Greece)

  • Apostolos Ziakopoulos

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Heroon Polytechniou Str., GR-15773 Athens, Greece)

  • George Yannis

    (Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Heroon Polytechniou Str., GR-15773 Athens, Greece)

Abstract

Historical road crash data are the main indicator for measuring road safety outcomes. Over the past few decades, significant efforts have been made in obtaining and exploiting Surrogate Safety Measures (SSMs). SSMs have the potential to provide excellent sustainable road safety indicators and proxy measurements which can complement traditional historical crash analyses or even substitute them. By using SSMs, crash data collection demands can be bypassed and areas can be investigated before crashes occur. Due to such advantages, the objective of the present research is to provide a review of the scientific literature regarding studies exploiting SSMs for historical crash record investigations. Specifically, 34 studies were examined, providing insights on the different types of SSMs collected under real road environment conditions, the way they are collected, their connection with specific road crash types, and the type of the developed statistical models are examined and discussed. Particular focus is also placed on the temporal dimension of the collection period of both SSMs and road crashes. Finally, the overall trends deriving from the reviewed studies are summarized and future research directions are provided.

Suggested Citation

  • Dimitrios Nikolaou & Apostolos Ziakopoulos & George Yannis, 2023. "A Review of Surrogate Safety Measures Uses in Historical Crash Investigations," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7580-:d:1139892
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7580/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7580/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Xiaohua & Yang, Haiyi & Yao, Ying & Qi, Hang & Guo, Miao & Su, Yuelong, 2022. "Factors affecting traffic risks on bridge sections of freeways based on partial dependence plots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    4. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    5. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    6. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andre Python & Andreas Bender & Marta Blangiardo & Janine B. Illian & Ying Lin & Baoli Liu & Tim C.D. Lucas & Siwei Tan & Yingying Wen & Davit Svanidze & Jianwei Yin, 2022. "A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 202-218, January.
    2. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.
    3. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    4. Paige, John & Fuglstad, Geir-Arne & Riebler, Andrea & Wakefield, Jon, 2022. "Bayesian multiresolution modeling of georeferenced data: An extension of ‘LatticeKrig’," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    5. Aaron Osgood‐Zimmerman & Jon Wakefield, 2023. "A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling," International Statistical Review, International Statistical Institute, vol. 91(2), pages 318-342, August.
    6. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    7. André Victor Ribeiro Amaral & Elias Teixeira Krainski & Ruiman Zhong & Paula Moraga, 2024. "Model-Based Geostatistics Under Spatially Varying Preferential Sampling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 766-792, December.
    8. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    9. Dong Liang & Genevieve Nesslage & Michael Wilberg & Thomas Miller, 2017. "Bayesian Calibration of Blue Crab (Callinectes sapidus) Abundance Indices Based on Probability Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 481-497, December.
    10. Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
    11. Carlos Díaz-Avalos & Pablo Juan & Somnath Chaudhuri & Marc Sáez & Laura Serra, 2020. "Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State," IJERPH, MDPI, vol. 17(23), pages 1-18, December.
    12. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    13. Janine B. Illian & David F. R. P. Burslem, 2017. "Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 495-520, October.
    14. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
    15. Mauricio Campos & Bo Li & Guillaume Lafontaine & Joseph Napier & Feng Sheng Hu, 2024. "Integrating Different Data Sources Using a Bayesian Hierarchical Model to Unveil Glacial Refugia," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 576-600, September.
    16. Humphreys, John M. & Elsner, James B. & Jagger, Thomas H. & Pau, Stephanie, 2017. "A Bayesian geostatistical approach to modeling global distributions of Lygodium microphyllum under projected climate warming," Ecological Modelling, Elsevier, vol. 363(C), pages 192-206.
    17. Ingrid Sandvig Thorsen & Bård Støve & Hans J. Skaug, 2023. "A TMB Approach to Study Spatial Variation in Weather-Generated Claims in Insurance," SN Operations Research Forum, Springer, vol. 4(4), pages 1-27, December.
    18. Peter A. Gao & Hannah M. Director & Cecilia M. Bitz & Adrian E. Raftery, 2022. "Probabilistic Forecasts of Arctic Sea Ice Thickness," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 280-302, June.
    19. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    20. G. Mateo, Rubén & Aroca-Fernández, María José & Gastón, Aitor & Gómez-Rubio, Virgilio & Saura, Santiago & García-Viñas, Juan Ignacio, 2019. "Looking for an optimal hierarchical approach for ecologically meaningful niche modelling," Ecological Modelling, Elsevier, vol. 409(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7580-:d:1139892. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.