  Predicting pedestrian behavior when interacting with vehicles is one of the
most critical challenges in the field of automated driving. Pedestrian crossing
behavior is influenced by various interaction factors, including time to
arrival, pedestrian waiting time, the presence of zebra crossing, and the
properties and personality traits of both pedestrians and drivers. However,
these factors have not been fully explored for use in predicting interaction
outcomes. In this paper, we use machine learning to predict pedestrian crossing
behavior including pedestrian crossing decision, crossing initiation time
(CIT), and crossing duration (CD) when interacting with vehicles at
unsignalized crossings. Distributed simulator data are utilized for predicting
and analyzing the interaction factors. Compared with the logistic regression
baseline model, our proposed neural network model improves the prediction
accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces
the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared
with the linear regression model. Additionally, we have analyzed the importance
of interaction factors, and present the results of models using fewer factors.
This provides information for model selection in different scenarios with
limited input features.
