Citation

Chen T, Shi X, Wong YD, Yu X. Transp. Res. C Emerg. Technol. 2020; 116: e102646.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.trc.2020.102646

PMID

unavailable

Abstract

Vehicles' risky lane-changing (LC) maneuver has significant impact on road traffic safety. As an innovation compared with the posterior LC risk prediction methods proposed in previous studies, this study develops a pre-emptive LC risk level prediction (P-LRLP) method, which is able to estimate the crash risk level of an LC event in advance before the LC car completes the LC maneuver. The basic concept of this method is to apply a machine learning classifier to predict the LC risk level based on cars' key space-series features at the beginning of the LC event. To boost the prediction performance, an innovative resampling method, namely ENN-SMOTE-Tomek Link (EST), and an advanced machine learning classifier, namely LightGBM, are proposed and employed in the development of the P-LRLP method. Meanwhile, an algorithm which can measure the stability of the selected key features in terms of the randomness and size of training samples is developed to evaluate the feature selection methods. A digitalized vehicles' trajectory dataset, the Next Generation Simulation (NGSIM) is used for method validation. The validation results manifest that the EST can achieve satisfactory resampling performance while Random Forest (RF), as an embedded FS method, achieves remarkable performance on both stability of selected features and prediction of risk level. The results also show that the LC risk level can be most accurately predicted when the LC car moves to the position where the distance between the longitudinal center line of the LC car and the marking line separating the two lanes equals 1.5ft. As an innovative LC risk level prediction technique, the P-LRLP method could be integrated with advanced driver-assistance system (ADAS) and vehicle-to-vehicle (V2V) communication to remedy potential risky LC maneuver in the future.

Language: en

Keywords

Feature selection; Machine learning; Pre-emptive lane-changing risk prediction; Resampling method