Results 241 to 250 of about 10,094,639 (283)

A weighted pattern recognition algorithm for short-term traffic flow forecasting

Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control, 2012
The k-nearest neighbor (k-NN) nonparametric regression is a classic model for single point short-term traffic flow forecasting. The traffic flows of the same clock time of the days are viewed as neighbors to each other, and the neighbors with the most similar values are regarded as nearest neighbors and are used for the prediction. In this method, only
Shuangshuang Li, Zhen Shen 0004, Fei-Yue Wang 0001   +2 more
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Kalman-LSTM Model for Short-term Traffic Flow Forecasting

2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021
This paper proposes a time prediction model based on Kalman filtering and LSTM, namely the Kalman LSTM model, which is used to predict time series data with long-term and short-term characteristics. The Kalman-LSTM model uses LSTM’s unique storage function to "store" the information contained in the pre-order data.
Weiwei Fang, Weihong Cai, Bo Fan, Jingwen Yan, Teng Zhou   +4 more
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Short Term Traffic Flow Forecast Based on CM-GRU Networks

2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2019
Intelligent transportation systems (ITS) have developed for a long time. The rise of deep learning has brought new vitality of the ITS. However, traffic flow data is usually time-correlated and highly randomized. The data distribution will also change dynamically.
Zhongzheng Guo, Weifeng Zhong, Fenghua Zhu, Xiaoshuang Li, Fei-Yue Wang 0001, Gang Xiong 0001   +5 more
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Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecasting

2008
In this paper, we apply a new method to forecast short-term traffic flows. It is kernel regression based on a Mahalanobis metric whose parameters are estimated by gradient descent methods. Based on the analysis for eigenvalues of learned metric matrices, we further propose a method for evaluating the effectiveness of the learned metrics. Experiments on
Shiliang Sun, Qiaona Chen
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Noise-Identified Kalman Filter for Short-Term Traffic Flow Forecasting

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), 2019
In this paper, we present a novel and effective technique for short-term traffic flow forecasting. Our main contribution is an extension of Kalman filter, such that it becomes to be able to identify the noise and then filter out it; we hence named the present technique as noise-identified Kalman filter.
Shuangyi Zhang, Youyi Song, Dazhi Jiang, Teng Zhou, Jing Qin 0001   +4 more
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Forecasting Traffic Flow: Short Term, Long Term, and When It Rains

2018
Forecasting is the art of taking available information of the past and attempting to make the best educated guesses of the ever unforeseen future. From the historical data, patterns can be observed and forecasting models have been developed to capture such patterns.
Hao Peng 0007, Santosh U. Bobade, Michael E. Cotterell, John A. Miller 0001   +3 more
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PSO-SVR: A Hybrid Short-term Traffic Flow Forecasting Method

2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), 2015
Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems.
Wenbin Hu 0001, Liping Yan, Kaizeng Liu, Huan Wang 0005   +3 more
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Short-Term Traffic Flow Forecasting Using Macroscopic Urban Traffic Network Model

2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008
Traffic flow forecasting provides important information for both traffic control and traffic guidance. It should be both quick and accurate. A short-term traffic flow forecasting method is given based on the macroscopic urban traffic network model. The model is established to describe the substantial mechanism of traffic flow movement and the topology ...
Shu Lin 0002, Yugeng Xi 0001, Yanfei Yang   +2 more
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Empirical study of robust combination of forecasts for short-term highway traffic flow forecast

2012 International Conference on Machine Learning and Cybernetics, 2012
In order to improve forecast accuracy and reliability of expressway traffic flows, the variance reciprocal weighting methods in linear combination of forecasts are compared numerically with the simple average. Ten individual methods for combination include the autoregression, exponential smoothing models, moving average models, and cybernetics method ...
Zheng-Ling Yang, Yan Li, Yan-Wen Song, Xi Chen, Jun Zhang   +4 more
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Soft-Computing Techniques Applied to Short-Term Traffic Flow Forecasting

Systems Analysis Modelling Simulation, 2003
Multi Linear Perceptron (MLP) neural networks, Radial Basis Function (RBF) neural networks, and Fuzzy Logic (FL) were used as soft-computing (or artificial intelligent) modelling techniques to come to short-term forecasts of traffic flow. The field data used for modelling was collected through single loop induction detectors on freeways 405 and 22 in ...
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