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 Pedestrians

Lei Yang (), Mahdi Aghaabbasi (), Mujahid Ali, Amin Jan, Belgacem Bouallegue, Muhammad Faisal Javed and Nermin M. Salem
Additional contact information
Lei Yang: Department of Computer Science and Technology, Lyuliang University, Lvliang 033000, China
Mahdi Aghaabbasi: Transportation Institute, Chulalongkorn University, Bangkok 10330, Thailand
Mujahid Ali: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Amin Jan: Faculty of Hospitality, Tourism and Wellness, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Malaysia
Belgacem Bouallegue: College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
Muhammad Faisal Javed: Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
Nermin M. Salem: Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cario 11835, Egypt

Sustainability, 2022, vol. 14, issue 17, 1-18

Abstract: Over the past three decades, more than 8000 pedestrians have been killed in Australia due to vehicular crashes. There is a general assumption that pedestrians are often the most vulnerable to crashes. Sustainable transportation goals are at odds with the high risk of pedestrian fatalities and injuries in car crashes. It is imperative that the reasons for pedestrian injuries be identified if we are to improve the safety of this group of road users who are particularly susceptible. These results were obtained mostly through the use of well-established statistical approaches. A lack of flexibility in managing outliers, incomplete, or inconsistent data, as well as rigid pre-assumptions, have been criticized in these models. This study employed three well-known machine learning models to predict road-crash-related pedestrian fatalities (RCPF). These models included support vector machines (SVM), ensemble decision trees (EDT), and k-nearest neighbors (KNN). These models were hybridized with a Bayesian optimization (BO) algorithm to find the optimum values of their hyperparameters, which are extremely important to accurately predict the RCPF. The findings of this study show that all the three models’ performance was improved using the BO. The KNN model had the highest improvement in accuracy (+11%) after the BO was applied to it. However, the ultimate accuracy of the SVM and EDT models was higher than that of the KNN model. This study establishes the framework for employing optimized machine learning techniques to reduce pedestrian fatalities in traffic accidents.

Keywords: road-crash-related pedestrian fatalities; Bayesian optimization algorithm; hyperparameters; sustainable safety of pedestrians (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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