Fatal Crash Occurrence Prediction and Pattern Evaluation by Applying Machine Learning Techniques

2.1. Factors Contributing to Injury Severity and Fatal Crash

Cerrelli quantified the impact of various driver and crash factors on the likelihood of fatal crashes, including driver age and sex, time of day, speed limit, vehicle type, and type of collision [7]. The study found that male drivers have 1.33 times higher odds of being involved in a fatal crash than female drivers, while drivers over 65 have odds 2.59 times higher than their younger counterparts. Similar findings were established by Pitta et al., who conducted a meta-analysis of 14 published studies between 2001 to 2018 and found that older drivers are more likely to be involved in fatal crashes [10].

Altwaijri et al. investigated the factors that contribute to the severity of road injury crashes in Riyadh, Saudi Arabia, using crash data collected over five years [11]. The study employed two logit models (multinomial and mixed) to analyze injury severity, with results suggesting the age and nationality of the driver, excessive speed, wet road surface, dark lighting conditions, and single-vehicle crashes to be associated with an increased probability of fatal crashes. Crashes at night-time, on roads with higher speed limits, and involving certain types of collisions (e.g., head-on or side) have significantly higher odds of being fatal [8]. The logit model was also applied by Macioszek et al., and the results have highlighted the importance of addressing factors, such as driving under the influence of alcohol, exceeding the speed limit, and the involvement of heavy vehicles in pedestrian accidents [3]. Ahmad et al. developed an ordered probit (OP) model using four levels of injury severity (property damage only, minor injury, major injury, and fatal injury) to investigate the risk factors behind crash severity. They found that the risk factors that increase the propensity of crash severity are speeding, drowsiness, head-on-collision, driving in the wrong direction, illegal pedestrian crossing, and increasing age of the drivers [12].

Tamakloe and Park conducted a study on fatal crashes in the hotspot zones in South Korea. They used GIS for spatio-temporal analysis of the crash hotspot zones and implemented machine learning models to understand the factors underlying fatal crashes. The study concluded minibusses/vans and construction vehicles to be responsible for single-vehicle pedestrian-involved crashes. Other factors, including nighttime variables and reckless driving, were also found to be significant in fatal crashes in the hotspot areas [13]. A study by Macioszek et al. concluded that the installation of traffic lights in the transportation network increases the safety level [14].

Gu et al. conducted a study to explore the contributing factors of multi-fatality crashes using a novel framework combining association rules mining and rules graph structures using 1,068 severe fatal crashes in China from 2015 to 2020. Their findings indicated that improper operations, passenger overload, fewer lanes, mountain terrains, and run-off-the-road crashes are the key variables for multi-fatality crashes. Moreover, they also concluded that human-vehicle-environment-road factors create more severe crashes than normal crashes [15]. A study aimed to identify factors contributing to injury severity in run-off-road (ROR) crashes using crash data from Ohio between 2008 and 2012. The study used a decision tree model that identified eight factors, including road condition, alcohol and drug use, road curves and grades, gender, posted speed limit, ROR crash types, and vehicle type, as significant predictors of injury severity [9].

In a study by Ghandour et al., a hybrid machine-learning model was created to examine the factors linked to fatal crashes. The model used data consisting of 8,482 road crashes and utilized variables related to the timing of crashes (hour, day, month) and the types of roads. The study revealed crash type, injury severity, and crash time (hour) as the main factors contributing to fatal crashes [16]. Hossain et al. used unsupervised learning techniques and found that alcohol impairment, exceeding the posted speed limits, and adverse weather conditions are associated with fatal crashes, severe collisions, and moderate injury crashes, respectively [17].

In summary, from the previous literature, various factors, such as driver’s demographic characteristics (age, gender), speeding, human factors (impaired driving, drowsiness), driving conditions (adverse weather conditions, night-time, dark lighting, etc.), type of collision (single vehicle, head-on, roadway departure), presence of traffic lights and heavy vehicles, etc., have been found to be crucial factors for fatal crashes. It is important to mention that the fatal crash-associated factors can vary across different regions and periods as well as with the availability of rich and accurate datasets.

2.2. Crash Pattern Recognition

Cluster analysis subjects the data to multivariate segmentation employing conceptual grouping techniques. In a study conducted by Saha et al., various clustering techniques and their corresponding parameters were examined to identify operational scenarios for different levels of crash severity [18]. The clustering techniques included K-means, K-prototypes, K-medoids, four different hierarchical methods, and a combination of Principal Component Analysis (PCA). The study analyzed six variables, namely traffic volume, speed, occupancy, travel lane blockage due to incidents, incident severity, and precipitation. Based on the results, the K-means clustering technique combined with PCA was found to be the most effective.

Implementing a hybrid approach combining cluster analysis and a Bayesian hierarchical model was another direction for investigating driver injury severity patterns. Li et al. used this approach to examine intersection-related crashes based on two-year crash data in New Mexico [19]. The results indicated that K-means cluster analysis was performed based on weather, roadway, and environmental conditions to reveal possible instability patterns of drivers under diverse external environments.

Rahimi et al. used the block clustering technique to detect crash patterns involving large trucks [20]. The analysis divided the heterogeneous crash dataset into subgroups. The resulting clusters were classified based on whether the crashes occurred in the same direction, in an opposing direction, or involved a single vehicle.

Yuan et al. conducted a study to understand the risk factors associated with truck-involved fatal crashes for different groups of truck drivers. They used the latent class clustering method to classify the truck drivers into three groups and found that adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights, and various collision manners were significant in all driver groups [21].

Using Joint Correspondence Analysis (JCA) and Association Rule Mining (ARM), Hossain et al. found some behavioral patterns in teenagers, such as distracted driving, alcohol intoxication, using a cell phone while driving, and so on that can also contribute to fatal crashes [17].

2.3. Crash Severity Prediction Models

Although traditional statistical models are frequently used to analyze crash injury severity, they rely on assumptions about the data distribution and often use a linear function to connect the dependent and explanatory variables. If these assumptions are violated, the parameter estimations generated will be incorrect [22]. According to a study by Savolainen et al., the Ordered Probit (OP) model is the most commonly used modeling technique for analyzing crash injury severity [23]. Other common statistical models, such as the Multi-Nomial Logit (MNL) model and the Binary Logit (BL) model are being utilized for crash severity analysis [24]. Abdulhafedh (2017) suggested that traffic-related crash prediction models, such as the logit model and Artificial Neural Network (ANN), should be used by transportation agencies and researchers to better understand traffic crashes and the associated risk factors [24].

In the past few years, multiple ML models have been created to predict different levels of crash severity. Since crash severity is a heterogeneous variable having non-linear relationships with the most studied factors, it is a challenge to choose essential variables that are significantly correlated to detect and forecast crash severity. To address this issue, Machine Learning (ML) approaches are often employed to map the non-linear relationship between the variables. Iranitalab and Khattak compared four statistical and ML models to predict crash severity [7]. The study utilized data related to roadways, drivers, vehicles, crashes, and the environment, and concluded that the Nearest Neighbor Classification (NNC) approach was the most accurate in forecasting severe injury crashes. The statistical approach, Multinomial Logit (MNL), was found to be the weakest and least reliable method. A study focused on predicting rear-end crashes and found that the Support Vector Machine (SVM) method outperformed statistical models, such as the multinomial logit and mixed multinomial logit models [25]. Another study developed three ML algorithms to predict motorcycle crashes [26]. They found that all ML models showed greater accuracy than statistical models, with the Random Forest (RF) model performing the best among the other ML models in terms of accuracy.

Injury severity prediction was also investigated using the OP and ML models [Multi-Layer Perceptron (MLP) and fuzzy Adaptive Resonance Theory (ART), [27]. Among all the models, MLP produced the highest accuracy of 73.5% and the OP model provided the lowest accuracy. The study found that an accident's severity is influenced by factors, such as gender, vehicle speed, seat belt use, vehicle type, point of contact, and location type. Another study compared Neural Network (NN) models with other ML models, such as KNN, decision tree, RF, and SVM to predict real-time crashes using a dataset containing 284 crashes and 592 non-crash data [28]. The study revealed that Deep Neural Networks (DNN) produced the highest accuracy while evaluating crash severity with overall

accuracy, sensitivity, and specificity of 68.95%, 0.52, and 0.77, respectively. Rahim and Hassan (2021) conducted a comparison study between Deep Learning (DL) and ML techniques (SVM) to predict the crash severity based on crash data between 2014 and 2018 in Louisiana. They used the image transformation DL tool, CNN, to convert the variables into images. The study explored that the deep learning technique provided better accuracy than ML methods based on precision and recall [29]. Shiran et al. implemented Multiple Logistic Regression (MLR), Decision Tree (DT) techniques, and Artificial Neural Network (ANN) to predict the crash severity at five severity levels, including property damage only, fatality, severe injury, other visible injuries, and complaint of pain based on the traffic crash records for State Highways in California. The study found that a DT model, C5.0, produced the best performance among all the models (ANN, MLR, and MLP) [30]. Hossain et al. used unsupervised learning techniques (JCA and ARM) to investigate the injury and fatal crash patterns of teenagers [17]. According to the study, unsupervised learning techniques have the benefit of effectively managing noisy and missing data without decreasing the size of the dataset.

3.1. Feature Selection and Data Preparation

Feature selection offers several benefits, including the reduction of overfitting, improved model accuracy, and decreased training time, which makes it a highly valuable process for our research. We utilized feature selection techniques to identify the most significant factors that contribute to fatal crashes. Our approach involved implementing three distinct techniques: the SelectKBest method, the chi-squared test, and the correlation matrix. By utilizing these methods, we were able to select the most appropriate independent variables, which served as the most accurate predictors for our dependent variable, namely, fatal crashes.

The scikit-learn library's SelectKBest method is a useful resource for obtaining the top features from a given dataset. With this classifier approach, features are chosen based on the k highest scores. This algorithm features a scoring function that employs the chi-squared formula. Speed limit, lane number, vehicle body type, collision with another vehicle - first harmful event (FHE), accident site, light condition, maneuver type, surface condition, surface type, and weather condition were among the top 10 variables generated by this approach. Fig. (1) depicts a correlation matrix to clearly explain how the variables are interconnected. The correlation coefficients were computed using the Pearson method. With the help of this correlation matrix, additional validation for the SelectKBest classifier was carried out.

The raw data require preprocessing and normalizing before feeding into the model so that the machine can effectively learn information from the data and predict accordingly. All the independent variables were categorical and were formed into several dummy variables. The crashes involving property damage had been removed from consideration for the three accident

severity groups (PDO, injury, and death crashes). This implies that the algorithms that have been used could clearly distinguish between crashes leading to injury and those leading to death, further allowing the models to distinguish the contributing factors behind fatal crashes and injury crashes. After feature selection and engineering, the dataset was imbalanced as the number of data points available for prediction classes (fatal and non-fatal crashes) was different. Synthetic Minority Over-sampling TEchnique (SMOTE), a resampling approach, had been used to address the issue of the unbalanced dataset. By combining over-sampling, the minority class, with under-sampling, the majority class, Chawla et al. developed this method to enhance classifier performance [31]. From SMOTE analysis, 3,634 observations were obtained, and they were equally distributed with positive and negative outcomes. After cleaning and preprocessing the data, 65%, 15%, and 20% of the total dataset were used for training, validation, and testing, respectively.

4.1. Prediction Models

In this study, a statistical model (multiple logistic regression), different types of ML models (random forest, XGBoost, support vector machine), and two neural network models (deep neural network, multilayer perceptron) were developed to classify the fatal crashes, and their prediction performances were compared.

4.1.4. Deep Neural Network

Neural Networks (NN) are a form of AI that imitates the human brain's ability to identify complex patterns using algorithms. These networks use neurons, layers, and activation functions to cluster and classify labeled data. The conventional NN consists of three layers: an input layer, a hidden layer, and an output layer. In this study, the selected 10 important variables from the feature selection process have been fed forward to the input neurons (Fig. 3) and the output neuron has been set to a binary outcome, which indicated whether a crash is fatal or not (Fig. 3). When multiple hidden layers are employed, the model becomes a Deep Neural Network (DNN), which can learn more complex patterns and enhance prediction performance. Fig. (3) portrays the process of how information flows through a DNN. The DNN begins with input data that is directed toward neurons in the input layer. These neurons send the output to neurons in the hidden layers. The hidden layers then pass on their outputs to the output layer to produce the result as a binary number or probability. Each layer is equipped with an activation function and the connection between two neurons in consecutive layers has a weight assigned to it. During each training epoch, the learning rate and optimizer adjust these weights to reduce the loss function.

4.1.5. Multi-layer Perceptron

A Multi-Layer Perceptron (MLP) is a supervised learning algorithm. It involves an activation function of by training on a dataset, where m and 0 represent the number of dimensions for input and output, respectively. The network is composed of an input layer, one or more hidden layers, and an output layer, each containing multiple neurons (Fig. 4). Similar to the NN, in MLP, the input neurons represent the 10 selected independent variables and the output layer (f(X) in Fig. 4) indicates the binary outcome of a crash being fatal or not. During training, the algorithm adjusts the weights between neurons to minimize the loss function, using backpropagation to propagate the error signal through the network. The activation function of each neuron introduces nonlinearity to the model, allowing for the detection of more complex patterns in the data. Despite its effectiveness, MLP classifiers require careful tuning of hyperparameters and can be computationally expensive, especially for large datasets.

4.2. Model Selection Rationale

There are several reasons for choosing certain models. Firstly, the Multiple Logistic Regression (MLR) model was employed to assess the adequacy of the model in terms of capturing intricate and non-linear relationships in the crash dataset. Secondly, ML models were implemented to compare their performance with the BLR model in terms of accuracy and interpretability. Thirdly, the crash dataset generally consists of high-dimensional data with non-linear correlations. To address such data complications, the SVM model can be used, as it is effective for handling datasets with many features, capable of extracting non-linear relationships, and is less prone to overfitting [38, 39]. Fourthly, randomness and irregularities are common in crash occurrence data where true relationships between the variables can be obscured at times. The ensemble methods (RF and XGBoost) can be robust to irregularities and randomness and often produce higher accuracy as they combine multiple models [40-43]. RF can handle complex datasets, is less prone to overfit, and enables efficient parallelization, rendering it well-suited for the analysis of large datasets [31, 43-45]. Boosting algorithms (XGBoost) not only have the capability to integrate several models, but also possess gradients that enable the model to iteratively rectify the mistakes made by preceding models [41, 46-48]. Lastly, the neural network models are subsets of ML that have the capacity to capture complex patterns and relationships, are known for excelling in tasks involving large datasets and intricate patterns as they contain neurons, and are trained using the stochastic gradient descent optimization algorithm [49].

4.3. Performance Metrics

This study involved testing and evaluating various machine learning models using different performance metrics obtained from the confusion matrix. The models were developed for binary classification of fatal crashes. The performance metrics used in the study included accuracy, sensitivity, recall, and F1-score, which were calculated based on true positive (TP), true negative (TN), false positive (FP), and false negative (FN) outcomes (Fig. 5).

From the confusion matrix, the overall accuracy (ACC), Precision (P), Recall (R), and F1-score (F) have been calculated (equations 2-5) to use as the performance metrics for the developed models (Eqs 1-4).

(1)

(2)

(3)

(4)

4.4. Clustering Algorithm

A popular technique for grouping data based on both quantitative and qualitative variables is the K-means algorithm. The process is started by setting a set of centroids, and the method is based on an iterative algorithm. A squared Euclidian distance metric is then used to assign each data point to be clustered to its nearest centroid. The objective is to minimize the sum of average pair-wise distances within cluster dissimilarity before assigning a point to a cluster. The centroids are updated by averaging all the points assigned to each cluster. Therefore, the centroids are established by reducing the sum of squared errors. This process is repeated until there is no significant change in the assignment of data points to each centroid.

Principal Component Analysis (PCA) is a dimension-reduction technique that can be combined with K-means clustering [18]. After dividing the dataset into clusters, running the PCA can reduce the dimension of the clusters to better visualize the clusters. PCA-based dimension reduction picks up the dimensions with the largest variances [50]. The elbow method was used to determine the optimal number of clusters. The point where the elbow curve began to bend or lower the slope was considered the optimum number of clusters.

5.1. Model Performances and Comparison

The ML models contain various hyperparameters that require fine-tuning. Thus, we optimized these hyperparameters through the grid search method and multiple iterations. Our evaluation of model performance relied on previously established performance metrics. Among all the models, MLR performed poorly in detecting a fatal crash as the dataset contained complex non-linear relationships between the variables. Table 2 shows that the F1 score for the predicted fatal crashes was 0.49 for the MLR model, which is very low compared to other models.

Among the three ML models, XGBoost showed the best performance with an overall accuracy of 0.82. The F1 scores of this model were 0.83 and 0.81, both being higher than the other ML models. Random Forest (RF) exhibited the second-best performance in terms of accuracy and F1 score. However, the shortcoming of RF is that it takes a long time to train as it builds, analyzes, and combines a bunch of decision trees. SVM performed a bit faster than XGBoost, but XGBoost outperformed SVM in terms of overall accuracy and F1 scores.

Neural network models performed better than statistical and ML models. Deep Neural Network (DNN) outperformed all the models with an average accuracy of 0.85. Not only the overall accuracy but also the precision and F1-scores were greater than the rest of the models. This may be because deep neural networks have several deep layers and neurons that can refine information to produce more accurate output. This kind of model can extract the complex interrelationship between the predictor variables and the target variable and predict accordingly. As DNN can have loops and consists of a greater number of neurons and hidden layers than MLP, it takes a bit longer time to train, but produces higher overall accuracy than MLP.

5.2. Prominent Contributing Factors of Crash Fatality (SHAP Analysis)

The interpretability of ML models has been a limitation in understanding their predictions. To address this limitation, Lundberg and Lee (2017) recently introduced a new approach called “SHapley Additive exPlanations (SHAP) analysis,” which aims to explain the impact of input variables on the model's output [50]. In this study, the SHAP analysis was performed on the XGBoost to interpret the model prediction.

Fig. (6) illustrates the relative effect of some important variables on the model output (crash fatality). The blue and red colors represent the lower and higher values of a feature, respectively, and the x-axis shows the SHAP value of the features. From Fig. (6), it can be observed that the speed limit of 65 mph has the highest impact on prediction where the relationship between this feature and crash fatality is proportional. On the contrary, the reciprocal relationship between the speed limit of 30 mph and crash fatality indicates that, when the speed limit is 65 mph, there is a higher probability of a fatal crash occurrence than driving on a posted speed limit of 30 mph.

Rear-end collision has been found to have a lower probability of leading to a crash fatality, whereas head-on collision has been found to be one of the most prominent factors for crash fatality. The two-way road has been found as the second most influential feature for fatality occurrence. In addition, in the middle of an interchange, the chances of fatal crash occurrence increase compared to the average model prediction. Fig. (6) shows that even though a small amount of data were present involving bike accidents (lower red and blue dots for motorcycles), the probability of fatal crash occurrences was higher in number than non-fatal crashes. This indicates that, when a motorcycle or tractor-trailer is involved in a crash, it is more likely to be a fatal crash. It can also be seen that the daylight and the presence of streetlights lower the chance of getting into a fatal crash. This visual representation describes, on a global scale, how each characteristic contributes to the average model forecast of fatality occurrences.

5.3. K-means Clustering with PCA

Prediction of a fatal crash and recognizing the underlying pattern is a fundamental need with the advent of numerous data. The underlying pattern of the crash might reveal the characteristics of the crash, which may be related to those variables that can aid traffic safety measures.

Based on the feature selection results, eight important variables (road surface type, number of lanes, weather condition, accident location, vehicle body type, light condition, vehicle maneuver, and on-the-road speed limit) were selected to conduct K-means clustering. Clusters were further visualized based on each level of the variables in the data. The optimum number of clusters was determined using the elbow method shown in Fig. (7).

Fig. (7) shows a bend in the curve approximately after 5 clusters. Therefore, 5 clusters were taken for analysis. The clusters are further visualized in Fig. (8). A practitioner can get an idea from visualization like this about which variables are prominent in which cluster.

Based on Fig. (8), the representative scenario from each cluster can be described below:

Cluster 0 implies mostly blacktop or concrete surface, two to six lanes, both intersections and roadways, and a wide range of vehicles. Since this cluster contains mostly usual conditions, this cluster does not imply much about special conditions or indications.

Cluster 1 implies both blacktop and concrete surfaces, two to six lanes, both intersections, and roadways, but mostly on intersections, a wide range of vehicles, daylight or with streetlights, straight or left turns, and a speed limit of 30 to 45 mph. This cluster emphasizes the fatal crashes at intersections. Also, the speed limit is between 30 to 45 mph, which emphasizes local streets. It can be observed that left turns are one of the crucial maneuvers in terms of the occurrence of fatal crashes. Streetlights have also been found to be very significant.

Cluster 2 implies mostly blacktop surface, 2 lanes, drizzle or normal conditions, non-intersection roadway, and intersections, straight following a road, a speed limit of 65 mph, and dark with no streetlights. This cluster gives a notion that a blacktop surface during drizzle conditions on a two-lane road has more probability of a fatal crash. The cluster indicates dark with no street light conditions, which are vital for two-lane highways since the speed limit is 65mph. Also, the maneuvers include avoiding and negotiating a curve, which is very crucial on two-lane highways that may lead to a fatal crash. Considering these factors, practitioners can take specific countermeasures to mitigate fatal crashes on two-lane highways.

Cluster 3 implies blacktop or concrete surface, four to six lanes, straight following road, non-intersection, intersection on roadways, and a speed limit above 70 mph. It focuses on high-speed limits with four to six lanes, which may represent interstate highways.

Cluster 4 implies a blacktop or concrete surface on two-lane highways, with special conditions, like drizzle, strong winds, or snow. This emphasizes adverse weather conditions on two-lane highways. A clear difference is that cluster 2 mostly involves dark conditions, whereas cluster 4 represents daylight.

Each cluster emphasizes certain aspects of the variables that make them unique.