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Predicting and Analysing Pedestrian Injury Severity: A Machine Learning-Based Approach

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Proceedings of the Seventh International Conference on Mathematics and Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1412))

Abstract

Pedestrians have a higher risk of casualties on the road. To improve the situation, it is essentially needed to understand the factors contributing to fatal accidents and their contributions to frame better regulations. Yet there is a dearth of substantial superior indexes that classifies the severity of the pedestrian injury. Machine learning (ML) has been successfully implemented in many domains but it is not widely utilized in the development of pedestrian road safety rules and regulations. In this paper, an ML approach is presented to generate a set of rules for predicting the severity of an accident based on several factors, including the number of vehicles, speed of vehicles, and environment. Out of thirty-four variables, 20 variables are selected using the Chi-square technique. Three popular ML algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) are used for the prediction of injury severity. From experiments, RF and DT are found to have similar accuracy scores, and hence, DT has been selected since it is simple to interpret and to generate a set of ‘if-then’ rules. A total of 20 safety decision rules are retrieved from the dataset, which explains the factors behind the severity of the pedestrian injury.

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Notes

  1. 1.

    https://www.nhtsa.gov/road-safety/pedestrian-safety.

  2. 2.

    Link: www.gov.uk.

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, IIT Kharagpur, Kharagpur, 721302, India

    Anjali Rao, Anima Pramanik & J. Maiti

  2. Business School, University of Edinburgh, 29 Buccleuch Pl, Edinburgh, EH8 9JS, UK

    Sobhan Sarkar

Authors
  1. Anjali Rao
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  2. Sobhan Sarkar
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  3. Anima Pramanik
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  4. J. Maiti
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Corresponding author

Correspondence to Sobhan Sarkar .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Haringhata, India

    Debasis Giri

  2. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Department of Mathematics, Indian Institute of Technology Madras, Chennai, India

    Saminathan Ponnusamy

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  5. Department of Computer Engineering, Ondokuz Mayis University, Atakum, Turkey

    Sedat Akleylek

  6. Department of Information Technology, Indian Institute of Engineering Science and Technology, West Bengal, India

    Santi Prasad Maity

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rao, A., Sarkar, S., Pramanik, A., Maiti, J. (2022). Predicting and Analysing Pedestrian Injury Severity: A Machine Learning-Based Approach. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_36

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