Improving Pedestrian Safety Using Video Data, Surrogate Safety Measures and Deep Learning

The research aims to improve pedestrian safety at signalized intersections using video data, surrogate safety measures and deep learning. Machine learning (including deep learning) models are proposed for predicting pedestrians\u27 potentially dangerous situations. On the one hand, pedestrians\u27 red-light violations can expose the pedestrians to motorized traffic and pose potential threats to pedestrian safety. Thus, the prediction of pedestrians\u27 crossing intention during red-light signals is carried out. The pose estimation technique is used to extract features on pedestrians\u27 bodies. Machine learning models are used to predict pedestrians\u27 crossing intention at intersections\u27 red-light, with video data collected from signalized intersections. Multiple prediction horizons are used. On the other hand, SSMs (Surrogate Safety Measures) can be used to better investigate the mechanisms of crashes proactively compared with crash data. With the SSMs indicators, pedestrians\u27 near-crash events can be identified. The automated computer vision techniques such as Mask R-CNN (Region-based Convolutional Neural Network) and YOLO (You Only Look Once) are utilized to generate the features of the road users from video data. The interactions between vehicles and pedestrians are analyzed. Based on that, the prediction of pedestrians\u27 conflicts in time series with deep learning models is carried out at the individual-vehicle level. Besides, two SSMs indicators, PET (Post Encroachment Time) and TTC (Time to Collision), are derived from videos to label pedestrians\u27 near-crash events. Deep learning model such as LSTM (Long Short-term Memory) is used for modeling. To make the model more adaptive to a real-time system, the signal timing data ATSPM© (Automated Traffic Signal Performance Measures) can be used. The signal cycles that contain pedestrian phases are labeled with the SSMs indicators derived from videos and then modeled. With the above-mentioned models proposed, the decision makers can determine the possible countermeasures, or the warning strategies for drivers at intersections