A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis

Abstract

Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors. Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face. Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability. It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood. In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.

Analysis of Factors Contributing to the Severity of Large Truck Crashes

“…Thus, in this study, the mixed logit model was chosen as the baseline regression model to compare with the ML models. Besides, the classification tree-based ML methods have been widely employed for crash risk prediction and identification of contributing factors [ 10 , 11 , 25 ]. Note that a number of traffic safety studies have used classification tree-based ML methods for preselecting the independent variables for the mixed logit model to efficiently determine the random parameters and improve model accuracy [ 13 , 14 ].…”

“…For ML-based techniques, the techniques applied to traffic safety studies include classification tree-based models, neural networks, and support vector machine models. In recent years, the classification tree-based ML methods have been widely employed for crash risk prediction and identification of contributing factors [ 10 , 11 ]. A classification tree-based ML method decides which crash risk factors should be chosen as the decision nodes and which features can provide more information or reduce more uncertainty about the severity of traffic crashes based on information gain and entropy.…”

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

“…In order to verify the reliability of the proposed method, we compare the performance of the MLP to that of other benchmark methods, namely, gradient boosting (GB) and decision trees (DT). e gradient boosting model has benefited from being popular in many research areas and from the ability to handle overfitting, [47]. Decision tree models are quick to build and easy to interpret and understand.…”

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

“…ML methods are more flexible with no or fewer model assumptions for input variables, and also have better fitting characteristics. Some of the commonly used ML approaches used in crash injury severity prediction include artificial neural networks (ANN) [ 58 , 59 , 60 ], random forest [ 54 , 61 , 62 ], support vector machines (SVM) [ 51 , 63 , 64 ], naïve Bayes [ 65 , 66 , 67 ], K-means clustering (KC) [ 68 , 69 , 70 ], and decision trees (DT) [ 71 , 72 , 73 ].…”