Supervised Data Synthesizing and Evolving – A Framework for Real-World Traffic Crash Severity Classification

Abstract

Traffic crashes have threatened properties and lives for more than thirty years. Thanks to the recent proliferation of traffic data, the machine learning techniques have been broadly expected to make contributions in the traffic safety community due to their triumphs in many other domains. Among these contributions, the most cited method is to classify traffic crashes in different severities since they have significantly unequal occurrences and costs. However, considering the complexity of transportation system, the traffic data are usually highly imbalanced and lowly separable (HILS), so that few proposed works report satisfactory results. In this paper, we propose a novel framework to deal with the HILS traffic crash data. The framework comprises two parts. In part I, a novel Supervised Data Synthesizing and Evolving algorithm is proposed, which can properly represent the HILS data into a more balanced and separable form without altering the original data distribution. In part II, the details of a customized Multi-Layer Perceptron (MLP) are presented, serving the purpose of learning from the represented data with fast convergence and high accuracy. A real-world traffic crash dataset, as a benchmark, is employed to evaluate the classification performances of our framework and three state-of-the-art imbalanced learning algorithms. The experimental results validate that our framework significantly outperforms the other algorithms. Moreover, the impacts of various parameter settings are studied and discussed