scholarly journals Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

2017 ◽  
Vol 29 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Anamarija L. Mrgole ◽  
Drago Sever
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Duffing Oscillator ◽  
Traffic Load ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Pattern Recognition Methods ◽  
Flow Prediction ◽  
Time Period ◽  
Specific Section

The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

scholarly journals Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network

2020 ◽  
Vol 24 (7) ◽  
pp. 900-907
Author(s):  
Songjiang Li ◽  
◽  
Wen An ◽  
Peng Wang
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Road Network ◽  
Probability Model ◽  
Prediction Method ◽  
Path Selection ◽  
Peak Period ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.

Research on Traffic Flow Redistribution Algorithm under the Traffic Accident

2012 ◽  
Vol 253-255 ◽  
pp. 1747-1750
Author(s):  
Yong Cun Zhu ◽  
Wen Yong Li ◽  
Yang Zhang ◽  
Tao Wang
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Traffic Load ◽  
Route Guidance ◽  
The Road ◽  
Allocation Method ◽  
The Right ◽  
Capacity Constrained ◽  
Dynamic Route ◽  
The Impact

To reduce traffic incident, it is imperative to take effective measures. This paper presents a dynamic route guidance method based on capacity constrained allocation method. Firstly, it analyzes the character of capacity constrained allocation method which takes into account the right of way and traffic load characteristics and used ant algorithms to optimize algorithm. Secondly, it integrates with multi-period continuous dynamic route guidance to realize the redistribution of road traffic flow . Finally, an example is took to prove that this manner can be a perfect solution to the road traffic evens on the impact of the road traffic flow.

scholarly journals FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning

2020 ◽  
Vol 24 (7) ◽  
pp. 829-836
Author(s):  
Di Yang ◽  
◽  
Ningjia Qiu ◽  
Peng Wang ◽  
Huamin Yang
Keyword(s):  
Traffic Flow ◽  
Prediction Models ◽  
Road Traffic ◽  
Transportation Systems ◽  
Natural Order ◽  
Traffic Data ◽  
Traffic Flow Prediction ◽  
Task Learning ◽  
Flow Prediction ◽  
Highly Correlated

Traffic flow prediction is one of the fundamental components in Intelligent Transportation Systems (ITS). Many traffic flow prediction models have been developed, but with limitation of noise sensitivity, which will result in poor generalization. Fused Lasso, also known as total variation denoising, penalizes L1-norm on the model coefficients and pairwise differences between neighboring coefficients, has been widely used to analyze highly correlated features with a natural order, as is the case with traffic flow. It denoises data by encouraging both sparsity of coefficients and their differences, and estimates the coefficients of highly correlated variables to be equal to each other. However, for traffic data, the same coefficients will lead to overexpression of features, and losing the trend of time series of traffic flow. In this work, we propose a Fused Ridge multi-task learning (FR-MTL) model for multi-road traffic flow prediction. It introduces Fused Ridge for traffic data denoising, imposes penalty on L2-norm of the coefficients and their differences. The penalty of L2-norm proportionally shrinks coefficients, and generates smooth coefficient vectors with non-sparsity. It has both capability of trend preservation and denoising. In addition, we jointly consider multi-task learning (MTL) for training shared spatiotemporal information among traffic roads. The experiments on real traffic data show the advantages of the proposed model over other four regularized baseline models, and on traffic data with Gaussian noise and missing data, the FR-MTL model demonstrates potential and promising capability with satisfying accuracy and effectiveness.

A spatial-temporal approach for traffic status analysis and prediction based on Bi-LSTM structure

2021 ◽  
pp. 2150481
Author(s):  
Linjia Li ◽  
Yang Yang ◽  
Zhenzhou Yuan ◽  
Zhi Chen
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Road Traffic ◽  
Performance Measurement System ◽  
Flow Data ◽  
Short Term ◽  
Temporal Differences ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Urban traffic control has become a big issue to help traffic management in recent years. With data explosion, Intelligent Transportation System (ITS) is developing rapidly. ITS is an advanced data-based method for traffic control, which requires timely and effective information supply. This research aims at providing real-time and accurate traffic flow data by intelligent prediction method. Applying multiple road traffic flow data of the Caltrans Performance Measurement System (PeMS) and separating the time series, the mechanism of spatial-temporal differences was taken into consideration. Based on the basic Long Short-Term Memory (LSTM) model, an improved LSTM model with Dropout and Bi-structure (Bi-LSTM) for traffic flow prediction was presented. In the prediction process, we applied three models including the improved Bi-LSTM model, Gated Recurrent Unit (GRU) model and Linear Regression in the experiment, and made a comparison from aspects of model structure complexity, operating efficiency and prediction accuracy. To validate the portability of the prediction model, the features of traffic flow from different datasets were further analyzed. The experimental results show that the improved Bi-LSTM model performs best in traffic flow prediction with comprehensive rationality, which reaches an accuracy of about 92% when considering temporal differences. Particularly, the specific factors of traffic situations and locations which is more applicable to be predicted by the improved Bi-LSTM model are summarized considering spatial differences. This research proposes an advanced and accurate model to provide real-time and short-term traffic flow prediction data, which is of great help to intelligent traffic control. Considering the mechanism between model and road traffic properties, the results suggest that it is more applicable in urban commercial area.

scholarly journals Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Haijing Tang ◽  
Yu Liang ◽  
Zhongnan Huang ◽  
Taoyi Wang ◽  
Lin He ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Prediction Method ◽  
Flow Equation ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model ◽  
Optimal Value ◽  
Transfer Probability

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.

Sign in / Sign up

Export Citation Format

Share Document