A. Khodayari, A. Ghaffari,
Volume 2, Issue 1 (1-2012)
Abstract
Car-following models, as the most popular microscopic traffic flow modeling, is increasingly being used by
transportation experts to evaluate new Intelligent Transportation System (ITS) applications. A number of factors
including individual differences of age, gender, and risk-taking behavior, have been found to influence car-following
behavior. This paper presents a novel idea to calculate the Driver-Vehicle Unit (DVU) instantaneous reaction delay of
DVU as the human effects. Unlike previous works, where the reaction delay is considered to be fixed, considering the
proposed idea, three input-output models are developed to estimate FV acceleration based on soft computing
approaches. The models are developed based on the reaction delay as an input. In these modeling, the inputs and
outputs are chosen with respect to this feature to design the soft computing models. The performance of models is
evaluated based on field data and compared to a number of existing car-following models. The results show that new
soft computing models based on instantaneous reaction delay outperformed the other car-following models. The
proposed models can be recruited in driver assistant devices, safe distance keeping observers, collision prevention
systems and other ITS applications.
R. Kazemi, M. Abdollahzade,
Volume 5, Issue 1 (3-2015)
Abstract
Car following process is time-varying in essence, due to the involvement of human actions. This paper develops an adaptive technique for car following modeling in a traffic flow. The proposed technique includes an online fuzzy neural network (OFNN) which is able to adapt its rule-consequent parameters to the time-varying processes. The proposed OFNN is first trained by an growing binary tree learning algorithm in offline mode, which produces favorable extrapolation performance, and then, is adapted to the stream of car following data, e.g. velocity and acceleration of the target vehicle, using an adaptive least squares estimation. The proposed approach is validated by means of real-world car following data sets. Simulation results confirm the satisfactory performance of the OFNN for adaptive car following modeling application.
