Prediction of Pedestrians\u27 Red Light Violations Using Deep Learning

Pedestrians are regarded as Vulnerable Road Users (VRUs). Each year, thousands of pedestrians\u27 deaths are caused by traffic crashes, which take up 16% of the total road fatalities and injuries in the U.S. (FHWA, 2018). Crashes can happen if there are interactions between VRUs and motorized transportation. And pedestrians\u27 unexpected crossings, such as red-light violations at the signalized intersections, would expose them to motorized transportation and cause potential collisions. This thesis is intended to predict the pedestrians\u27 red-light violation behaviors at the signalized crosswalks based on an LSTM (Long Short-term Memory) neural network. With video data collected from real traffic scenes, it is found that pedestrians that crossed during the red-light periods are more in danger of being struck by vehicles, from the perspective of Surrogate Safety Measures (SSMs). Pedestrians\u27 features are generated using computer vision techniques. An LSTM model is used to predict pedestrians\u27 red-light violations using these features. The experiment results at one signalized intersection show that the LSTM model achieves an accuracy of 91.6%. Drivers can be more prepared for these unexpected crossing pedestrians if the model is to be implemented in the vehicle-to-infrastructure (V2I) communication system