Traffic flow short-time prediction using fractal theory

2008 ◽  
Author(s):  
Dong Hong zhao ◽  
Xu Jianjun ◽  
Chen Ning
Keyword(s):  
Traffic Flow ◽  
Fractal Theory ◽  
Time Prediction ◽  
Short Time

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 A Cellular Automaton Traffic Flow Model with Advanced Decelerations

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Yingdong Liu
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Flow Model ◽  
Stable State ◽  
Practical Significance ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
Short Time ◽  
Vehicle Deceleration ◽  
Real Traffic

A one-dimensional cellular automaton traffic flow model, which considers the deceleration in advance, is addressed in this paper. The model reflects the situation in the real traffic that drivers usually adjust the current velocity by forecasting its velocities in a short time of future, in order to avoid the sharp deceleration. The fundamental diagram obtained by simulation shows the ability of this model to capture the essential features of traffic flow, for example, synchronized flow, meta-stable state, and phase separation at the high density. Contrasting with the simulation results of the VE model, this model shows a higher maximum flux closer to the measured data, more stability, more efficient dissolving blockage, lower vehicle deceleration, and more reasonable distribution of vehicles. The results indicate that advanced deceleration has an important impact on traffic flow, and this model has some practical significance as the result matching to the actual situation.

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.

scholarly journals Earthquake forecasting and its verification

2005 ◽  
Vol 12 (6) ◽  
pp. 965-977 ◽  
Author(s):  
J. R. Holliday ◽  
K. Z. Nanjo ◽  
K. F. Tiampo ◽  
J. B. Rundle ◽  
D. L. Turcotte
Keyword(s):  
Temporal Variations ◽  
Earthquake Forecasting ◽  
Earthquake Risk ◽  
Decision Threshold ◽  
New Approach ◽  
Pi Method ◽  
Time Prediction ◽  
Short Time ◽  
Large Earthquakes ◽  
Proven Method

Abstract. No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ("hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver) operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

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