@article{ref1,
title="Prediction of pedestrian crossing intentions at intersections based on long short-term memory recurrent neural network",
journal="Transportation research record",
year="2020",
author="Zhang, Shile and Abdel-Aty, Mohamed and Yuan, Jinghui and Li, Pei",
volume="2674",
number="4",
pages="57-65",
abstract="Traffic violations of pedestrians at intersections are major causes of road crashes involving pedestrians, especially red-light crossing behaviors. To predict the pedestrians' red-light crossing intentions, video data from real traffic scenes are collected. Using detection and tracking techniques in computer vision, some pedestrians' characteristics, including location information, are generated. A long short-term memory neural network is established and trained to predict pedestrians' red-light crossing intentions. The experimental results show that the model has an accuracy rate of 91.6% based on internal testing at one signalized crosswalk. This model can be further implemented in the vehicle-to-infrastructure communication environment and prevent crashes because of the pedestrians' red-light crossing behaviors.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/0361198120912422",
url="http://dx.doi.org/10.1177/0361198120912422"
}