Short-time Traffic Flow Research Articles

Short-Time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture

Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and error analysis of experimental results showed that, the designed algorithms can accurately forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better, can be effectively applied to actual traffic engineering.

Short time traffic flow prediction based on Volterra model using multiplication-coupled configuration

An adaptive third-order Volterra filter for short-time traffic flow prediction was proposed,which was based on intrinsic nonlinearity and deterministic mechanism of chaotic time series and nonlinear expression of Volterra series.Concerning the problem that the coefficients of Volterra filter exponentially increase with its order,an approximate multiplication-coupled structure for Volterra filter was studied.At first,through properly choosing time delay and embedding dimension using mutual information method and false nearest neighbor method,respectively,the largest Lyapunov exponent was estimated by applying small data sets method so as to validate that chaos existed in traffic flow series.Then,the approximate multiplication-coupled structure for third-order Volterra filter,of which coefficients were adaptively adjusted by using an improved nonlinear Normalized Least Mean Square(NLMS) algorithm,was employed to reduce computational complexity.Finally,by applying the proposed method to the real measured traffic flow data,the experimental results show that chaos exists in traffic flow series and the proposed scheme can effectively predict traffic flow series and reduce model complexity.

Short-term traffic flow prediction for multi traffic states on urban expressway network

Short-term traffic flow prediction for multi traffic states on urban expressway network is carried out in this paper. The model for multi traffic states is proposed by integrating the spatial and the temporal distribution characteristics of traffic flow parameters under free traffic, congested traffic and jam traffic respectively. Based on the classical traffic flow conservation equation, the ideology of spatial and temporal dispersions in partial differential equations is adopted to establish short-time traffic flow prediction model. Meanwhile, the impact factors, such as on and off ramp, lane change and road slope are considered, which convertes short-term traffic flow prediction model into short-time traffic flow prediction state space model. Finally, the short-term traffic flow prediction for multi traffic states on urban expressway network is realized. The empirical research shows that compared with the classic ARMA model, the proposed method can not only realize short-term traffic flow prediction for multi traffic states on urban expressway network but also achieve an accuracy of 90.23%. In the same condition, the accuracy of ARMA model is 81%.

Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

Study on Urban Intersection Traffic Flow Forecasting and Traffic Signal Control

Short-traffic flow forecasting is an important part of ITS, and its accuracy and real-time is directly related to the effect of traffic control and traffic induce. Gathering and analyzing the real-time data of urban road network ,short-time traffic flow forecasting could estimate the state of traffic flow for a few minutes in future and provide support to intelligent transportation control, so it is one of the important premise for ITS.

Improved Hybrid Intelligent Method for Urban Road Traffic Flow Forecasting based on Chaos-PSO Optimization

Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the low recognition rate. If using them independently, the prediction accuracy would be lower than integrated use. Hence, a new hybrid intelligent prediction approach base on the combination of advanced signal processing technique and intelligent data mining method is proposed for the short time traffic flow prediction in this paper. The new method is marked as the use of empirical mode decomposition (EMD) and support vector machine (SVM) to deal with the nonlinear and stochastic characteristics of the traffic flow data. Another advantage of the proposed method is that the Chaos-particle swarm optimization (PSO) is adopted for the optimization of the combination. By doing so, the local optimization of the EMD-SVM prediction model can be avoided and the forecasting rate can be enhanced significantly. The practical traffic flow data were applied to the validation of the proposed prediction model. The analysis results show that the proposed method can extract the underlying rules of the testing data and decrease prediction error by 0.53% or better when compared with single SVM approach. Thus, the new hybrid intelligent traffic flow forecasting model can provide practical application.

The Model of Short-Time Traffic Flow Prediction on High-Grade Highway

In order to study the short-time traffic flow prediction on high-grade highway, the article proposed a model based on wavelet analysis and RBF neural network. Aiming to the traffic flow’s characteristic of highway, the study focus on three facet: network topology, the difference of continuous flow and discontinuous flow , the flow of lanes’ uplink and downlink are not equal. Thus the article use the wavelet analysis to do data preprocessing, then structure the model of short-term traffic flow prediction based on RBF neural network. The experiment result shows that the new hybrid model adapt to high-grade highway, and model considering traffic flow characteristic is better than the model which is not. Meanwhile the model has the higher precision of prediction.

A Traffic Flow Forecasting Model Based on Variable Dimension Fractal Combined with Weekly Similarity

It’s important that the short-time traffic flow forecasting has good real-time performance and high accuracy. In order to satisfy this demand, weekly similarity is imported and an improved fractal forecast model is established. In order to improve forecast accuracy furthermore, one-rank local-region forecasting principle is referenced to determine parameters which influence weekly similarity degree. Finally, the improved fractal model based on variable dimension is employed to predict the traffic flow in Hangzhou city. The experiment result shows that the improved fractal method proposed here possesses high forecast accuracy.

Short-Time Traffic Flow Prediction Based on Chaos Time Series Theory

Traffic flow prediction has become a kernel study in intelligent transportation system. A prediction model of short-time traffic flow is presented based on chaotic time series analysis method. After the phase space reconstruction using traffic flow data, a two-step optimized selection method is proposed, which considers Euclidean distance and equal coefficient between neighboring point and predicted point. Then the prediction model is educed with the local polynomial method to approximate the neighboring points. The model proposed in this paper is applied to predict the real traffic flow in Dongjiang road, Dongguan city in Guangdong province, China. Comparing the predicted traffic flow value with the flow measured in reality, the results show that the maximal relative error is 0.445%, whereas, the minimal one is 0.038%. Moreover, the single-step forward prediction only requires 38.52 seconds. As a result, it is proved that the method can significantly improve the prediction accuracy and meet the requirement of the real-time prediction.

Traffic chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model(GM model) , and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series.