An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety

Tao, Wenlong and Aghaabbasi, Mahdi and Ali, Mujahid and Almaliki, Abdulrazak H. and Zainol, Rosilawati and Almaliki, Abdulrhman A. and Hussein, Enas E. (2022) An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety. Sustainability, 14 (4). ISSN 2071-1050, DOI

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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.

Item Type: Article
Funders: Taif University researchers support project, Taif University, Taif, Saudi Arabia [TURSP-2020/252]
Uncontrolled Keywords: Pedestrian fatality; Road accident; Bayesian neural network; Bayesian theorem; Sustainable road network development; Machine learning
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
Divisions: Faculty of the Built Environment > Department of Urban & Regional Planning
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 15 Aug 2022 01:24
Last Modified: 15 Aug 2022 01:24

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