01764223

Component

https://doi.org/10.1177/03611981211007483

Driving maneuvers detection is an important component of proactive traffic safety management and connected vehicle systems. Most of the existing studies used supervised learning concepts to train their models with labeled data. These methods achieved promising results but were limited by the heavy dependence on the labeled data. With the development of mobile sensing technologies, massive traffic-related data can be efficiently collected by mobile devices (e.g., smartphones, tablets, etc.). Considering the high costs of labeling data, this paper proposed a semi-supervised deep learning method to learn from the unlabeled data. Data from a smartphone’s accelerometer and gyroscope were collected by different drivers with a variety of smartphones, vehicles, and locations. Three long short-term memory (LSTM) models were trained with the proposed semi-supervised learning algorithm. Experimental results indicated that the proposed semi-supervised LSTM could learn from the unlabeled data and achieve outstanding results with only a small portion of the labeled data. Using much fewer labeled data, semi-supervised LSTM could achieve similar results compared with the supervised method. Moreover, the proposed method outperformed other machine learning methods (e.g., convolutional neural network, XGBoost, random forest) on precision, recall, F1-score, and area under curve. More and more traffic data will be available in the future, the proposed method is expected to make use of the undiscovered potential from the massive unlabeled data.

All results and opinions expressed in this paper are those of the authors only and do not reflect the opinion or position of FHWA or FDOT. © National Academy of Sciences: Transportation Research Board 2021.

TRBAM-21-02836

English

pp 1386-1397

2021-9

Transportation Research Record: Journal of the Transportation Research Board

Web

Figures; References (32) ; Tables

Behavior; Detection and identification; Drivers; Driving; Machine learning; Neural networks; Smartphones

Highways; Safety and Human Factors

TRIS, TRB, ATRI

Dec 23 2020 11:22AM