Abstract
The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.
Highlights
With an increase of tra c ow on highways all over the world, both the risk on tra c safety and the pressure of tra c management increase greatly. e detection and warning of abnormal driving behavior is an indispensable part of intelligent tra c management and monitoring in that it detects abnormal driving behaviors, gives an early warning to possibly a ected vehicles, and e ectively avoids tra c accidents
It is proved that the microparameters of the vehicle are predictable. en, based on R/S analysis, it is proved that the two parameters have fractal characteristics, and the prediction model of microscopic parameters is constructed based on the fractal theory. rough the quanti cation of the index of speed and acceleration time series data, neural network is used to estimate the predictable number of steps, and the prediction model of microparameter-predictable step is obtained. e multistep prediction of vehicle microtra c parameters is Journal of Advanced Transportation carried out based on fractal theory. e results show that the prediction model has a good performance
According to the predictable analysis of the characteristics of vehicle speed and acceleration, it is not necessary to contain all kinds of driving behavior when studying the prediction method of microscopic parameters. erefore, in order to reflect the general applicability of the forecasting method and avoid the particularity of the experimental vehicle, this paper chooses American NGSIM trajectory data to support the research
Summary
With an increase of tra c ow on highways all over the world, both the risk on tra c safety and the pressure of tra c management increase greatly. e detection and warning of abnormal driving behavior is an indispensable part of intelligent tra c management and monitoring in that it detects abnormal driving behaviors, gives an early warning to possibly a ected vehicles, and e ectively avoids tra c accidents. With the rapid development of computer technology, image processing technology, and video detecting technology, the accuracy and content of video detection data can provide a solid foundation for abnormal driving behavior monitoring in motor vehicles ([1,2,3,4]). Tra c microscopic parameters (instantaneous speed and acceleration) have strong nonlinear characteristics, and fractal theory is a good method to deal with time series data with self-similarity. Rough the quanti cation of the index of speed and acceleration time series data, neural network is used to estimate the predictable number of steps, and the prediction model of microparameter-predictable step is obtained. Record the vehicle’s real-time positioning data, fractal prediction, predict the driving behavior of the vehicle, identify abnormal driving behaviors such as speed of the vehicle, and provide warning
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