Improved Traffic Crash Modeling through Accuracy and Response Time Using Classification Algorithms: A Model Comparison Approach

ABSTRACT: This research focuses on predicting the severity of freeway traffic crashes by employing two different dataset including Iranian and Cyprus data. In Iranian data, twelve variables related to crash parameters were used by considering genetic algorithm, combined genetic algorithm and pattern search, and artificial neural network methods. The genetic algorithm evaluated eleven equations to obtain the best equation, and then the genetic algorithm and pattern search methods were combined using the best genetic algorithm equation. The neural network used a multi-layer perceptron architecture that consisted of a multi-layer feed-forward network with hidden sigmoid and linear output neurons that can also fit multidimensional mapping problems arbitrarily well. In Cyprus data, seven variables were selected to compare two fuzzy clustering algorithms—fuzzy subtractive clustering and fuzzy C-means clustering— with a multi-layer perceptron neural network. Four clustering algorithms—hierarchical, K-means, subtractive clustering, and fuzzy Cmeans clustering—were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The selected models used in Iranian and Cyprus dataset were able to predict the severity of crash injuries and to estimate the response time on the traffic crash data in which the prediction accuracy was determined according to R-value, root mean square errors, mean absolute errors, and sum of square error. Based on the results obtained from Iranian data, the highest R-value and the highest amount of time were obtained for the artificial neural network around 0.87 and 7.627 seconds, respectively. The results demonstrated that the artificial neural network provided the best prediction accuracy with highest response time, while genetic algorithm had the lowest value for prediction accuracy (0.79) and response time (0.687) among the applied models. The combination of the GA and PS methods allowed for various prediction rankings ranging from linear relationships to complex equations. Based on the results obtained from Cyprus data, the highest R-value and the highest amount of time were obtained for the multi-layer perceptron around 0.89 and 2.635, respectively demonstrating that the multi-layer perceptron had a high accuracy in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 ), 9.28 times faster than the time of multi-layer perceptron. Overall, the results showed that the MLP can be the best model to predict the traffic crash severity regardless of the variables involved with crash data in which the accuracy was the important criterion. Meanwhile, more than one model can be appropriate according to the determined criteria. Considering prediction accuracy and response time could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database as well as the system may be implemented in an incident management to prevent the traffic crash or secondary traffic crash in which the model can be extended through improvements based on additional data through induction procedure. Keywords: Accuracy, Classification algorithms, Prediction, Response time, Traffic crash severity. …………………………………………………………………………………………………………