Short-time prediction method based on fractal theory for traffic flow

2009 ◽  
Author(s):  
Chen Ning ◽  
Wu Jian ◽  
Wang Yifeng ◽  
Xu Juanjun ◽  
Dong Hangzao
Keyword(s):  
Traffic Flow ◽  
Fractal Theory ◽  
Prediction Method ◽  
Time Prediction ◽  
Short Time

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

A Study of the Traffic Flow Predictive Model Based on Mathematical Statistics and Stochastic Process

2014 ◽  
Vol 548-549 ◽  
pp. 1862-1868
Author(s):  
Hui Zhang ◽  
Hong Yong Zhang ◽  
Man Xia Liu
Keyword(s):  
Stochastic Process ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Flow Data ◽  
Traffic Flow Prediction ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Fluctuation Range ◽  
Improved Model ◽  
Short Time

Real-time traffic flow prediction is one of important issues of intelligent transportation system. Based on the theory of stochastic process of the traffic flow data, the prediction methods, such as grey expecting model and neural network, were applied in this paper. Then according to the actual traffic flow data, an improved model was proposed and the fluctuation range of predicted traffic flow was determined due to calculate an accurate result. Finally, the experiment shows that the designed prediction model can be able to achieve a short time prediction accurately for traffic flow.

scholarly journals Highway Traffic Flow Nonlinear Character Analysis and Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Meng Hui ◽  
Lin Bai ◽  
YanBo Li ◽  
QiSheng Wu
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Order Reduction ◽  
Volterra Model ◽  
Highway Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Nonlinear Character ◽  
Short Time ◽  
Highway Management

In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management.

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

2013 ◽  
Vol 756-759 ◽  
pp. 2785-2789
Author(s):  
Shu Zhi Nie ◽  
Yan Hua Zhong ◽  
Ming Hu
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Engineering ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Network Models ◽  
Local Optimum ◽  
Neural Network Models ◽  
Short Time ◽  
Organic Computing

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.

scholarly journals Development of short-time prediction method of sea salt concentration in the atmosphere

2009 ◽  
Vol 65 (1) ◽  
pp. 1166-1170
Author(s):  
Naoto KIHARA ◽  
Hiromaru HIRAKUCHI ◽  
Akira TAKAHASHI ◽  
Shin-ichi FUJITA
Keyword(s):  
Salt Concentration ◽  
Prediction Method ◽  
Sea Salt ◽  
Time Prediction ◽  
Short Time
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