TY  - JOUR
PY  - 2020//
TI  - Prediction of pedestrian crossing intentions at intersections based on long short-term memory recurrent neural network
JO  - Transportation research record
A1  - Zhang, Shile
A1  - Abdel-Aty, Mohamed
A1  - Yuan, Jinghui
A1  - Li, Pei
SP  - 57
EP  - 65
VL  - 2674
IS  - 4
N2  - 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>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/0361198120912422
ID  - ref1
ER  -