Citation

Wang J, Wang S. IEEE Trans. Intel. Transp. Syst. 2022; 23(4): 3777-3782.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3035708

PMID

unavailable

Abstract

This correspondence discusses recognition of traffic modes and behavior patterns based on Global Navigation Satellite System (GNSS) data. The traffic modes (e.g., walk, car, train, etc.) are firstly inferred, and then their behavior patterns (e.g., left-turn, right-turn, turn-around, etc.) are further identified. Because both traffic modes and behavior patterns are strongly influenced by geographical circumstances, their recognitions are enhanced by geographical layer information (e.g., building, road, water, etc.). At one specific GNSS point, its surrounding area is uniformly sliced as grids, and the probabilities for grid centers belonging to six different geographical layers are calculated based on whether these centers lie inside or outside of the minimum rectangles containing polygons indicating different geographical objects. Finally, the six-dimensional probability matrix is processed and compressed as a geographical information vector by the convolutional neural network (CNN). The latter is then combined with kinematic metrics such as velocity, acceleration, and moving direction from GNSS data, and serially input into a long short-term memory (LSTM) network to predict traffic modes and behavior patterns. Experimental results validate that the geographical information does enhance the performances of two recognition tasks. The CNN+LSTM framework retains the powers of CNN and LSTM, and outperforms classical machine learning algorithms.

Language: en

Keywords

Acceleration; Behavior pattern; CNN; geographic layer information; Global navigation satellite system; GNSS; Kinematics; LSTM; Rail transportation; Roads; Task analysis; traffic mode; Trajectory