IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i10p5670-d557267.html
   My bibliography  Save this article

Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison

Author

Listed:
  • Gholamreza Shiran

    (Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 8174673441, Iran)

  • Reza Imaninasab

    (Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Razieh Khayamim

    (Department of Transportation Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran)

Abstract

The classification of vehicular crashes based on their severity is crucial since not all of them have the same financial and injury values. In addition, avoiding crashes by identifying their influential factors is possible via accurate prediction modeling. In crash severity analysis, accurate and time-saving prediction models are necessary for classifying crashes based on their severity. Moreover, statistical models are incapable of identifying the potential severity of crashes regarding influencing factors incorporated in models. Unlike previous research efforts, which focused on the limited class of crash severity, including property damage only (PDO), fatality, and injury by applying data mining models, the present study sought to predict crash frequency according to five severity levels of PDO, fatality, severe injury, other visible injuries, and complaint of pain. The multinomial logistic regression (MLR) model and data mining approaches, including artificial neural network-multilayer perceptron (ANN-MLP) and two decision tree techniques, (i.e., Chi-square automatic interaction detector (CHAID) and C5.0) are utilized based on traffic crash records for State Highways in California, USA. The comparison of the findings of the relative importance of ten qualitative and ten quantitative independent variables incorporated in CHAID and C5.0 indicated that the cause of the crash (X 1 ) and the number of vehicles (X 5 ) were known as the most influential variables involved in the crash. However, the cause of the crash (X 1 ) and weather (X 2 ) were identified as the most contributing variables by the ANN-MLP model. In addition, the MLR model showed that the driver’s age (X 11 ) accounts for a larger proportion of traffic crash severity. Therefore, the sensitivity analysis demonstrated that C5.0 had the best performance for predicting road crash severity. Not only did C5.0 take a shorter time (0.05 s) compared to CHAID, MLP, and MLR, it also represented the highest accuracy rate for the training set. The overall prediction accuracy based on the training data was approximately 88.09% compared to 77.21% and 70.21% for CHAID and MLP models. In general, the findings of this study revealed that C5.0 can be a promising tool for predicting road crash severity.

Suggested Citation

  • Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5670-:d:557267
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/10/5670/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/10/5670/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamid Behbahani & Amir Mohamadian Amiri & Reza Imaninasab & Meysam Alizamir, 2018. "Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 767-780, November.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    3. Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.
    4. Fang Zong & Huiyong Zhang & Hongguo Xu & Xiumei Zhu & Lu Wang, 2013. "Predicting Severity and Duration of Road Traffic Accident," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, December.
    5. Lukuman Wahab & Haobin Jiang, 2019. "A comparative study on machine learning based algorithms for prediction of motorcycle crash severity," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-17, April.
    6. Song Wang & Zhixia Li, 2019. "Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fu Wang & Jing Wang & Xianfeng Zhang & Dengjun Gu & Yang Yang & Hongbin Zhu, 2022. "Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    2. Alicja Wolny-Dominiak & Tomasz Żądło, 2021. "The Measures of Accuracy of Claim Frequency Credibility Predictor," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    3. Mubarak Alrumaidhi & Hesham A. Rakha, 2022. "Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
    4. Miaomiao Yan & Yindong Shen, 2022. "Traffic Accident Severity Prediction Based on Random Forest," Sustainability, MDPI, vol. 14(3), pages 1-13, February.
    5. Debela Jima & Tibor Sipos, 2022. "The Impact of Road Geometric Formation on Traffic Crash and Its Severity Level," Sustainability, MDPI, vol. 14(14), pages 1-25, July.

    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. Francisco Javier Rondán-Cataluña & Patricio E. Ramírez-Correa & Jorge Arenas-Gaitán & Muriel Ramírez-Santana & Elizabeth E. Grandón & Jorge Alfaro-Pérez, 2020. "Social Network Communications in Chilean Older Adults," IJERPH, MDPI, vol. 17(17), pages 1-17, August.
    2. Arnold Tóth & Tímea Juhász & Botond Kálmán, 2020. "Determinants Of Financial Habits," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 14(1), pages 237-256.
    3. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    4. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    5. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    6. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    7. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    8. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    9. H Seol & H Lee & S Kim & Y Park, 2008. "The impact of information technology on organizational efficiency in public services: a DEA-based DT approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 231-238, February.
    10. Vanhoucke, Mario & Maenhout, Broos, 2009. "On the characterization and generation of nurse scheduling problem instances," European Journal of Operational Research, Elsevier, vol. 196(2), pages 457-467, July.
    11. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    12. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    13. Adrien Ehrhardt & Christophe Biernacki & Vincent Vandewalle & Philippe Heinrich, 2019. "Feature quantization for parsimonious and interpretable predictive models," Papers 1903.08920, arXiv.org.
    14. Onur Doğan & Hakan Aşan & Ejder Ayç, 2015. "Use Of Data Mining Techniques In Advance Decision Making Processes In A Local Firm," European Journal of Business and Economics, Central Bohemia University, vol. 10(2), pages 6821:10-682, January.
    15. Jae-Dong Kim & Tae-Hyeong Kim & Sung Won Han, 2023. "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks," Mathematics, MDPI, vol. 11(3), pages 1-10, January.
    16. Agapito, Dora & Mendes, Julio & Valle, Patricia, 2011. "The Sea as a Connection between Residents and Tourists in Coastal Destinations: A Case in Algarve," Spatial and Organizational Dynamics Discussion Papers 2011-13, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.
    17. Timmins, Christopher & Vissing, Ashley, 2022. "Environmental justice and Coasian bargaining: The role of race, ethnicity, and income in lease negotiations for shale gas," Journal of Environmental Economics and Management, Elsevier, vol. 114(C).
    18. Rike Stotten & Michaela Maurer & Hannes Herrmann & Markus Schermer, 2019. "Different Forms of Accommodation in Agritourism: The Role of Decoupled Farmer-Based Accommodation in the Ötztal Valley (Austria)," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    19. A. Israëls & J. Driel, 1983. "Use of the chi-square statistic for selecting explanatory variables in multiway tables," Quality & Quantity: International Journal of Methodology, Springer, vol. 17(2), pages 103-116, April.
    20. Nocella, Giuseppe & Stefani, Gianluca & Romano, Donato, 2011. "Preferences, trust and willingness to pay for food information: An analysis of the Italian Market," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114606, European Association of Agricultural Economists.

    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:13:y:2021:i:10:p:5670-:d:557267. 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.