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.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

Forecasting Website Traffic Using Prophet Time Series Model

2019 ◽  
Vol 1 (1) ◽  
pp. 56-63
Author(s):  
A Subashini ◽  
Sandhiya K ◽  
S Saranya ◽  
U Harsha
Keyword(s):  
Time Series ◽  
Big Data ◽  
Traffic Flow ◽  
Prediction Models ◽  
Internet Traffic ◽  
Time Series Model ◽  
Traffic Data ◽  
Traffic Flow Prediction ◽  
Flow Monitoring ◽  
Flow Prediction

Web traffic is the amount of data sent and received by visitors to a website and it has been the largest portion of Internet traffic. Internet traffic flow prediction heavily depends on historical and real-time traffic data collected from various internet flow monitoring sources. With the widespread traditional traffic sensors and new emerging traffic sensor technologies, traffic data are exploding, and we have entered the era of big data internet traffic. Internet traffic management and control driven by big data is becoming a new trend. Although there have been already many internet traffic flow prediction systems and models, most of which use shallow traffic models and are still somewhat unsatisfying. This inspires us to reconsider the internet traffic flow prediction model based on deep architecture models with such rich amount of internet traffic data. ARIMA is a existing forecasting technique that predicts the future values of a series based entirely on its own inertia. Existing traffic flow prediction methods mainly use simple traffic prediction models and are still unsatisfying for many real-world applications. Now we proposed the prophet time series model to forecasting website traffic.

Deep Spatio-Temporal Modified-Inception with Dilated Convolution Networks for Citywide Crowd Flows Prediction

2019 ◽  
Vol 34 (08) ◽  
pp. 2052003
Author(s):  
Yan Kang ◽  
Bing Yang ◽  
Hao Li ◽  
Tie Chen ◽  
Yachuan Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Road Traffic ◽  
Network Models ◽  
Traffic Data ◽  
Traffic Flow Prediction ◽  
Dilated Convolution ◽  
Flow Prediction ◽  
Traffic Capacity ◽  
Spatio Temporal

Traffic flow prediction has great significance for improving road traffic capacity and traffic safety. However, traffic flow in a certain area is usually affected by some factors such as weather, holidays and neighboring areas. So, traffic situation is complicated and traffic flow prediction is difficult. How to use existing traffic data information to predict future traffic flow is the key to this problem. In this paper, we develop an accurate prediction model based on dilated convolution — ST-MINet (Deep Spatio-Temporal Modified-Inception with Dilated convolution Networks). We fully consider the complexity, nonlinearity and uncertainly of traffic network by summarizing various network models such as ResNet and Inception. So, we use the deep space-time residual network to ensure the convolution accuracy of the information’s position distribution on the basis of existing networks. Then, we add the cavity convolution to the model, which can effectively control the field of view of the convolution kernel. In the experimental part, we compare ten classical algorithms with our ST-MINet, it shows that our model has higher accuracy than others.

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

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.

Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction

2016 ◽  
Vol 20 (7) ◽  
pp. 1181-1191 ◽  
Author(s):  
Chengdong Li ◽  
◽  
Yisheng Lv ◽  
Jianqiang Yi ◽  
Guiqing Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Fuzzy Rule ◽  
Transportation Systems ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Traffic Flow Prediction ◽  
Fast Learning ◽  
Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

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