Graduation Year


Document Type




Degree Granting Department

Civil Engineering

Major Professor

Jian John Lu, Ph.D., P.E

Co-Major Professor

Ram M. Pendyala, Ph.D.


artificial neural networks, freeway, incident, detection


Non-recurring congestion caused by incidents is a major source of traffic delay in freeway systems. With the objective of reducing these traffic delays, traffic operation managers are focusing on detecting incident conditions and dispatching emergency management teams to the scene quickly. During the past few decades, a few number of conventional algorithms and artificial neural network models were proposed to automate the process of detecting incident conditions on freeways. These algorithms and models, known as automatic incident detection methods (AIDM), have experienced a varying degree of detection capability.

Of these AIDMs, artificial neural network-based approaches have illustrated better detection performance than the conventional approaches such as filtering techniques, decision tree method, and catastrophe theory. So far, a few neural network model structures have been tested to detect freeway incidents. Since the freeway incidents directly affect the freeway traffic flow, majority of these models have used only traffic flow variables as model inputs. However, changes in traffic flow may also be stimulated by the other features (e.g., freeway geometry) to a greater extent. Many AIDMs have also used a conventional detection rate as a performance measure to assess the detection capability. Yet the principle function of incident detection model, which is to identify whether an incident condition exists for a given traffic pattern, is not measured in its entirety by this conventional measure.

In this study, new input feature sets, including freeway geometry information, were proposed for freeway incident detection. Sixteen different artificial neural network (ANN) models based on feed forward and recurrent architectures with a variety of input feature sets were developed. ANN models with single and double hidden layers were investigated for incident detection performance. A modified form of a conventional detection rate was introduced to capture full capability of AIDMs in detecting incident patterns in the freeway traffic flow. Results of this study suggest that double hidden layer networks are better than single hidden layer networks. The study has demonstrated the potential of ANNs to improve the reliability using double layer networks when freeway geometric information is included in the model.