Schema crosswalk

Physics

Level crossing

Term (time)

Biology

Engineering

Artificial intelligence

Pedestrian

Transport engineering

Pedestrian crossing

Red light

Computer science

Botany

Quantum mechanics

Computer vision

Artificial neural network

Mechanical engineering

Transportation Research Record Journal of the Transportation Research Board

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.

Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network