Citation

Tawfeek MH. J. Transp. Saf. Secur. 2022; 14(3): 404-429.

Copyright

(Copyright © 2022, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2020.1783414

PMID

unavailable

Abstract

This study aims at modelling unassisted drivers' speed at the yellow onset to enhance Connected and Autonomous Vehicles applications at signalised intersections and maximise drivers' comfort. For this purpose, a total of 2442 real-life vehicle trajectories were analysed to extract driver behavioural measures (i.e. speed, acceleration, and distance to intersection) at different times before the yellow onset. These behavioural measures were used to integrate drivers' perceptual ability into modelling drivers' speed at the yellow onset. To develop these models, three machine learning techniques; namely, linear regression, Support Vector Machine, and Neural Networks have been adopted. The best model was a neural network model and was selected based on the goodness-of-fit of the test dataset which has an R-squared value of 0.97. The results indicate that the speed at the yellow onset can be estimated based on behavioural measures while accounting for drivers' perceptual ability. Also, the model can contribute to a V2I application by assisting the driver in a partially autonomous vehicle to avoid trapping in the dilemma zone and stop safely at signalised intersections. Also, the model can be used to recommend a comfortable riding speed, from a rider's perspective to a fully autonomous vehicle.

Language: en

Keywords

Driver behaviour; driving assistance; neural networks; signalised intersections; support vector machine; vehicle trajectories; yellow onset