Introduction: Road traffic crashes represent a major public health concern, so it is of
significant importance to understand the factors associated with the increase of injury …

Training of dendritic neuron model artificial neural networks is generally achieved by using
nonlinear least square methods. The distribution of random error terms is ignored in training …

The evaluation of the severity of the factors influencing road accidents with a detailed
severity distribution is critical to plan evidence-based road safety improvements and …

Driven by advancements in data-driven methods, recent developments in proactive crash
prediction models have primarily focused on implementing machine learning and artificial …

To study the synergistic hierarchical arrangement effect, three hierarchical cubic lattice
structures and six kinds of hybrid hierarchical lattice structures are designed based on the …

Association rule mining (ARM) is one of the core techniques of data mining to discover
potentially valuable association relationships from mixed datasets. In the current research …

Artificial neural network (ANN) is modeled to predict and classify problems. However, in the
training phase of ANNs discovering faultless values of the weights of a network is extremely …

This study used methodologies of descriptive and quantitative statistics to identify the
contributing factors most affecting occupational accident outcomes among electrical …

With the continuous increase in highway mileage and vehicles in China, highway accidents
are also increasing year by year. However, the on-site disposal procedures of highway …

Motorcycle road traffic accidents represent a big concern, as vulnerable road users account
for more than half of all road deaths worldwide. The estimation of the influential factors …