On the Impact of Prior Experiences in Car-Following Models: Model Development, Computational Efficiency, Comparative Analyses, and Extensive Applications
Artikel i vetenskaplig tidskrift, 2021

A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.

Computational efficiency

Data models

Analytical models

Prediction algorithms

fixed-radius near neighbors (FRNN) algorithm

historical traffic data

trajectory prediction

data-driven car-following model

Computational modeling

Predictive models

Real-time systems

Training

Författare

Yang Yu

University of Technology Sydney

Zhengbing He

Beijing University of Technology

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

IEEE Transactions on Cybernetics

2168-2267 (ISSN)

Vol. In Press

Ämneskategorier

Transportteknik och logistik

Bioinformatik (beräkningsbiologi)

Farkostteknik

DOI

10.1109/TCYB.2021.3095154

Mer information

Senast uppdaterat

2021-10-13