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An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

Author

Listed:
  • Wenlong Tao

    (Department of Automotive Technology, Zhejiang Agricultural Business College, Shaoxing 312000, China)

  • Mahdi Aghaabbasi

    (Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Wilayah Persekutuan Kuala Lumpur, Malaysia)

  • Mujahid Ali

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Abdulrazak H. Almaliki

    (Civil Engineering Department, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Rosilawati Zainol

    (Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Wilayah Persekutuan Kuala Lumpur, Malaysia)

  • Abdulrhman A. Almaliki

    (Independent Researcher, Jeddah 12462, Saudi Arabia)

  • Enas E. Hussein

    (National Water Research Center, Shubra El-Kheima 13411, Egypt)

Abstract

More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.

Suggested Citation

  • Wenlong Tao & Mahdi Aghaabbasi & Mujahid Ali & Abdulrazak H. Almaliki & Rosilawati Zainol & Abdulrhman A. Almaliki & Enas E. Hussein, 2022. "An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2436-:d:754076
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    References listed on IDEAS

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    Cited by:

    1. Te Ma & Mahdi Aghaabbasi & Mujahid Ali & Rosilawati Zainol & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    2. Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    3. Lei Yang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Belgacem Bouallegue & Muhammad Faisal Javed & Nermin M. Salem, 2022. "Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestri," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    4. Katarzyna Sosik-Filipiak & Oleksandra Osypchuk, 2023. "Identification of Solutions for Vulnerable Road Users Safety in Urban Transport Systems: Grounded Theory Research," Sustainability, MDPI, vol. 15(13), pages 1-19, July.

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