Research on the Real-Time Prediction Model of the Traffic Flow Based on Wavelet Neural Network

2012 ◽  
Vol 241-244 ◽  
pp. 2088-2094
Author(s):  
Hui Ying Wen ◽  
Gui Feng Yang ◽  
Wei Tiao Wu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Traffic Flow ◽  
Traffic Safety ◽  
Traffic Control ◽  
Wavelet Neural Network ◽  
Mountain Area ◽  
Analog Simulation ◽  
Traffic Flow Prediction ◽  
Real Time Traffic

Real-time traffic flow prediction is the core of traffic control and management, which is the basis of traffic safety in mountain area. Traffic flow, which is highly time-relevant, with the features of high non-linear and non-determinism, can be treated as the time sequence forecast. Considering these features, this paper deals specially with this issue based on Wavelet neural network. Besides, by taking a road in mountain area for example, the paper realizes the analog simulation through the Matlab software programming. And the simulation results show that the traffic flow can be precisely forecast using Wavelet neural network, and its value is close to the expectations. The MAE of the Wavelet neural network is 20.1074 and the MSE is 2.5254.

An New Approach of Real-Time Traffic Flow Prediction Based on Intelligent Transportation Technology

2014 ◽  
Vol 988 ◽  
pp. 715-718
Author(s):  
Jia Yang Li ◽  
Qin Xue ◽  
Jin De Liu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Short Term ◽  
New Approach ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
Flow Prediction ◽  
Combination Approach

Short-term traffic flow forecasting is a core problem in Intelligent Transportation System .Considering linear and nonlinear, this paper proposes a short-term traffic flow intelligent combination approach. The weight of four forecasting model is given by the correlation coefficient and standard deviation method. The experimental results show that the new approach of real-time traffic flow prediction is higher precision than single method.

scholarly journals A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shaoqian Li ◽  
Zhenyuan Zhang ◽  
Yang Liu ◽  
Zixia Qin
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Rapid Development ◽  
Prediction Method ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
De Algorithm ◽  
Flow Prediction ◽  
Bpnn Model

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction.

Neural-network-based cycle length design for real-time traffic control

2008 ◽  
Vol 35 (4) ◽  
pp. 370-378 ◽  
Author(s):  
Jin-Tae Kim ◽  
Jeongyoon Lee ◽  
Myungsoon Chang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Traffic Control ◽  
Cycle Length ◽  
Design Procedure ◽  
Target Volume ◽  
Traffic Demand ◽  
Real Time Traffic ◽  
Adaptive Traffic Control ◽  
Artificial Neural Network Ann

Adaptive traffic control systems (ATCS) are designed to calculate traffic signal timings in real time to accommodate current traffic demand changes. A conventional off-line computer-based design procedure that uses iterative evaluations to select alternatives may not be appropriate for ATCS due to its unstable searching time. Search-free analytical procedures that directly find solutions have been noted for ATCS for this reason. This paper demonstrates (i) the shortcomings of an analytical cycle-length design model, specifically COSMOS, in its ability to generate satisfactory solutions at various saturation levels and (ii) an artificial neural network (ANN) based model that can overcome these shortcomings. The ANN-based model consistently yielded cycle lengths that ensure a proper operational target volume to capacity (v/c) ratio, whereas the use of the analytical model resulted in unstable target v/c ratios that might promote congestion.

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