Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology

Zheng, Jian; Suzuki, Koji; Fujita, Motohiro

doi:10.1016/j.trc.2013.09.010

Cited by 80 publications

(45 citation statements)

References 28 publications

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“…As ANNs require no pre-settings for considering the various combination of input variables or adding new features, the contribution of other factors, including the motion features of the preceding vehicle in front of the leading vehicle, can also be examined using ANNs (30). Unlike many studies that put the reaction time of the driver-vehicle unit as a constant value, a number of modified ANNs have been built to predict the car-following behavior based on the instantaneous reaction delay (31,32). Several studies have attempted to fuse ANNs with other approaches such as fuzzy logic and local linear regression models (4,33) Notwithstanding that several data-driven approaches for modeling the car-following behavior have been postulated in literature, lack of examining ensemble algorithms in this context has encouraged us to evaluate the capability of this class of machine learning algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the Gradient Boosting of Regression Trees Method on Estimating Car-Following Behavior

Dabiri

Abbas

2018

Transportation Research Record

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Car-following models, as the essential part of traffic microscopic simulations, have been utilized to analyze and estimate longitudinal drivers' behavior since sixty years ago. The conventional car following models use mathematical formulas to replicate human behavior in the car-following phenomenon. Incapability of these approaches to capturing the complex interactions between vehicles calls for deploying advanced learning frameworks to consider the more detailed behavior of drivers. In this study, we apply the Gradient Boosting of Regression Tree (GBRT) algorithm to the vehicle trajectory data sets, which have been collected through the Next Generation Simulation program, so as to develop a new car-following model. First, the regularization parameters of the proposed method are tuned using the cross-validation technique and the sensitivity analysis. Afterward, the prediction performance of the GBRT is compared to the world-famous GHR model, when both models have been trained on the same data sets. The estimation results of the models on the unseen records indicate the superiority of the GBRT algorithm in capturing the motion characteristics of two successive vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the Gradient Boosting of Regression Trees Method on Estimating Car-Following Behavior

Dabiri

Abbas

2018

Transportation Research Record

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“…Artificial Neural Network (ANN), can be considered one of the effective candidates to model such system since it is suitable for problems which have difficulties to establish the clear link between cause and effect [14,15]. Modelling and system identification in ANN can be established by using teaching signal and input data.…”

Section: Steering Modelling Using Neural Networkmentioning

confidence: 99%

Driver`s Steering Behaviour Identification and Modelling in Near Rear-End collision

Hassan¹,

Zamzuri²,

Wahid³

et al. 2017

TELKOMNIKA

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“…2.2.1 NNs (Neural Networks) NNs (neural networks) algorithms are commonly used to observe the statistical modelling of the driving behaviours [6,7]. There are some significant advantages of NNs for monitoring driving behaviour as: (1) allowing the pattern extraction without the awareness and facts of the relation between the inputs and the outputs; (2) less demand for formal training; and (3) recognition of all probable interactions between the predictor variables.…”

Section: Artificial Intelligent Techniques For Driving Behaviour Monimentioning

confidence: 99%

“…There are some significant advantages of NNs for monitoring driving behaviour as: (1) allowing the pattern extraction without the awareness and facts of the relation between the inputs and the outputs; (2) less demand for formal training; and (3) recognition of all probable interactions between the predictor variables. On the other hand, the black-box nature and the complex computation of ANNs are the two main drawbacks of these algorithms [6,7].…”

Section: Artificial Intelligent Techniques For Driving Behaviour Monimentioning

confidence: 99%

Driving Behavior Assessment Using Fuzzy Inference System and Low-Cost Inertial Sensors

Navidi¹,

Landry²

2017

JTTE

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Continuous vehicle tracking as well as monitoring driving behaviour, is significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to model the quality of the driving behaviour based on FIS (fuzzy inference systems). The models consider vehicle dynamics as long as the human behaviour parameters, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, assessment-driving behaviour model is simulated and tested by two different FISs: Mamdani and Sugeno-TSK. The simulation results illustrate the critical distinctions between the two FISs using the proposed driving behaviour models. These differences are based on various processing times, robust behaviour of the FISs, outputs MFs (membership functions), fuzzification-techniques, flexibility in the systems design and computational efficiency.

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Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology

Cited by 80 publications

References 28 publications

Evaluation of the Gradient Boosting of Regression Trees Method on Estimating Car-Following Behavior

Evaluation of the Gradient Boosting of Regression Trees Method on Estimating Car-Following Behavior

Driver`s Steering Behaviour Identification and Modelling in Near Rear-End collision

Driving Behavior Assessment Using Fuzzy Inference System and Low-Cost Inertial Sensors

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