01784038

Component

https://doi.org/10.1177/03611981211015253

Car-following is considered as one of the most prevalent fundamental driving behaviors that substantially influences traffic performance as well as road safety and capacity. Drivers’ car-following behavior is affected by numerous factors. However, in practice, very few of these factors have been scrutinized, because of their latent essence and unavailability of appropriate data. Owing to its importance, drivers’ reaction time has attracted the attention of many researchers; nevertheless, it is considered as a fixed parameter in car-following models, which is far from reality. To take the variability of drivers’ reaction time into account, a flexible hybrid approach has been suggested in the present study. In the proposed structure, in the first step, the desirable acceleration of the driver is estimated by applying an equation-based car-following model. In the next step, the driver’s reaction delay in applying the calculated acceleration is estimated by an artificial neural network. The corresponding parameters are jointly estimated by applying an estimated distribution algorithm. Statistical tests indicate better performance of the hybrid model, which considers the variations of the driver’s reaction time, compared with a traditional model with fixed reaction time. Furthermore, the cross-validation results indicate better generalizability and transferability of the proposed model in action.

© National Academy of Sciences: Transportation Research Board 2021.

English

pp 1297-1311

2021-10

Transportation Research Record: Journal of the Transportation Research Board

Web

References (31)

Car following; Distributions (Statistics); Drivers; Mathematical models; Neural networks; Reaction time

Data and Information Technology; Highways; Operations and Traffic Management

TRIS, TRB, ATRI

Sep 30 2021 3:16PM