Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost

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.

show abstract

“…Guo et al [57] To simulate the issue of categorizing three levels of severity in older pedestrian traffic crashes.…”

Section: Rf Dtmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…It outperforms with advantages of parallel learning, high flexibility, built-in cross-validation, etc. Previous studies have proved the successful use in traffic crash severity analysis and risk prediction [45,46].…”

Section: Methodsmentioning

confidence: 99%

Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

Yuan

Yan

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.

show abstract

“…The advantage of XGBoost in this study is that decision tree-based machine learning has no issues with the numerical encoding of categorical variables. Moreover, XGBoost requires much less training time than neural network and often produce remarkable prediction results in crash-related studies [ 32 , 33 , 34 ].…”

Section: Methodsmentioning

confidence: 99%

Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach

Zhu

Wang

2021

IJERPH

View full text Add to dashboard Cite

In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic crash injury severity and to test its performance over existing machine learning classification methods. First, we compare the performance of the neural network, XGBoost, and SVM classifiers in injury severity prediction. Second, we utilize a severity category-combination method with oversampling to relieve the class-imbalance problem prevalent in crash data. Third, we take advantage of probability calibration and the optimal probability threshold moving to improve the prediction ability of ordinal classification. The proposed approach can satisfy the rank consistency and rank monotonicity requirement and is proved to be superior to other ordinal classification methods and nominal classification machine learning by statistical significance test. Important factors relating to injury severity are selected based on their permutation feature importance scores. We find that converting severity levels into three classes, minor injury, moderate injury, and serious injury, can substantially improve the prediction precision.

show abstract

Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost

Cited by 46 publications

References 43 publications

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

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

Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach

Contact Info

Product

Resources

About