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Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

Ul Arifeen Siyab, Ali Mujahid, Macioszek Elżbieta

Archives of Transport

2023

Vol. 68, iss. 4

91--116

In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents.

Could an isolated human body lower limb model predict leg biomechanical response of Chinese pedestrians in vehicle collisions?

Ma Huaxing, Mao Zhengyu, Li Guibing, Yan Lingbo, Mo Fuhao

Acta of Bioengineering and Biomechanics

2020

Vol. 22, no. 3

117--129

Purpose: The purpose of the current study was to investigate whether an isolated human body lower limb FE model could predict leg kinematics and biomechanical response of a full body Chinese pedestrian model in vehicle collisions. Methods: A human body lower limb FE model representing midsize Chinese adult male anthropometry was employed with different upper body weight attachments being evaluated by comparing the predictions to those of a full body pedestrian model in vehicle-to-pedestrian collisions considering different front-end shapes. Results: The results indicate that upper body mass has a significant influence on pedestrian lower limb injury risk, the effect varies from vehicle front-end shape and is more remarkable to the femur and knee ligaments than to the tibia. In particular, the upper body mass can generally increase femur and knee ligaments injury risk, but has no obvious effect on the injury risk of tibia. The results also show that a higher attached buttock mass is needed for isolated pedestrian lower limb model for impacts with vehicles of higher bonnet leading edge. Conclusions: The findings of this study may suggest that it is necessary to consider vehicle shape variation in assessment of vehicle pedestrian protection performance and leg-form impactors with adaptive upper body mass should be used for vehicles with different front-end shapes, and the use of regional leg-form impactor modeling the local anthropometry to evaluate the actual lower limb injury of pedestrians in different countries and regions.