Incorporating General Incident Knowledge into Automatic Incident Detection: A Markov Logic Network Method
Automatic incident detection (AID) algorithms have been studied for more than 50 years. However, due to the development in some competing technologies such as cell phone call based detection, video detection, the importance of AID in traffic management has been decreasing over the years. In response to such trend, AID researchers introduced new universal and transferability requirements in addition to the traditional performance measures. Based on these requirements, the recent effort of AID research has been focused on applying new artificial intelligence (AI) models into incident detection and significant performance improvement has been observed comparing to earlier models. To fully address the new requirements, the existing AI models still have some limitations including 1) the black-box characteristics, 2) the overfitting issue, and 3) the requirement for clean, large, and accurate training data. Recently, Bayesian network (BN) based AID algorithm showed promising potentials in partially overcoming the above limitations with its open structure and explicit stochastic interpretation of incident knowledge. But BN still has its limitations such as the enforced cause-effect relationship among BN nodes and its Bayesian type of logic inference. In 2006, another more advanced statistical inference network, Markov Logic Network (MLN), was proposed in computer science, which can effectively overcome some limitations of BN and also bring the flexibility of applying various knowledge. In this study, an MLN-based AID algorithm is proposed. The proposed algorithm can interpret general types of traffic flow knowledge, not necessarily causality relationships. Meanwhile, a calibration method is also proposed to effective train the MLN. The algorithm is evaluated based on field data, collected at I-894 corridor in Milwaukee, WI. The results indicate promising potentials of the application of MLN in incident detection.