Estimation of Real-Time Crash Risk

Abstract

In-ground loop detectors have recently been used by many researchers to investigate the links with real-time crash risk and the traffic data. An issue that has been raised, but not explicitly addressed in these studies, is how the results from one freeway might transfer to another. A study was done to examine the relationship between crash risk and real-time traffic variables from a freeway corridor (eastbound I-4 in Orlando, Florida) and then to apply the models to three other freeway corridors (westbound I-4 and northbound and southbound I-95). Traffic data used in the study were collected with loop detectors as well as radar detectors already installed on these freeways. The traffic information was collected for crash as well as random noncrash cases so that a binary classification approach could be adopted. The random forest–based models provided a list of significant variables based on the average reduction in the Gini indices to the overall forest classification. The periods between 5 and 10 min before and between 10 and 15 min before the crash were taken into consideration so that these models could provide the crash risk in advance. Average occupancy of upstream station and average speed and coefficient of variation of volume for downstream stations were found to have a significant effect on crash risk. Application of multilayer perceptron neural network models showed that although the model developed for the I-4 corridor works reasonably well for the westbound I-4 corridor, the performance was not as good for the I-95 sections. This observation indicates that the same model for crash risk identification may work only for corridors with very similar traffic patterns.