Research on short-term traffic flow prediction based on the tensor decomposition algorithm

2020 ◽  
pp. 1-11
Author(s):  
Mingyu Tong ◽  
Huiming Duan ◽  
Xilin Luo
Keyword(s):  
Traffic Flow ◽  
Tensor Decomposition ◽  
Decomposition Algorithm ◽  
Grey Prediction ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Verhulst Model ◽  
Type Data ◽  
Short Time

In view of the uncertainties in short-time traffic flows and the multimode correlation of traffic flow data, a grey prediction model for short-time traffic flows based on tensor decomposition is proposed. First, traffic flow data are expressed as tensors based on the multimode characteristics of traffic flow data, and the principle of the tensor decomposition algorithm is introduced. Second, the Verhulst model is a classic grey prediction model that can effectively predict saturated S-type data, but traffic flow data do not have saturated S-type data. Therefore, the tensor decomposition algorithm is applied to the Verhulst model, and then, the Verhulst model of the tensor decomposition algorithm is established. Finally, the new model is applied to short-term traffic flow prediction, and an instance analysis shows that the model can deeply excavate the multimode correlation of traffic flow data. At the same time, the effect of the new model is superior to five other grey prediction models. The predicted results can provide intelligent transportation system planning, control and optimization with reliable real-time dynamic information in a timely manner.

scholarly journals Robust-LSTM: A Novel Approach to Short-Traffic Flow Prediction Based on Signal Decomposition

2021 ◽  
Author(s):  
Erdem Doğan
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Performance Measurement System ◽  
Flow Data ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Novel Approach ◽  
Long Short Term Memory

Abstract Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenge due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways’ main lanes. The time resolution of the dataset was set to 15 minutes, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyzes for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during mid-day periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

scholarly journals Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yue Hou ◽  
Zhiyuan Deng ◽  
Hanke Cui
Keyword(s):  
Traffic Flow ◽  
Effective Means ◽  
Weather Conditions ◽  
Weather Data ◽  
Multiple Time ◽  
Traffic Data ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Short-term traffic flow prediction is an effective means for intelligent transportation system (ITS) to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable to external weather conditions, the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments.

scholarly journals Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Qichun Bing ◽  
Ciyun Lin ◽  
Nan Yang ◽  
Duo Mei
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Similarity Search ◽  
Prediction Method ◽  
Flow Data ◽  
Short Term ◽  
Similar Time ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Advanced Traffic Management System

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

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 Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Huiming Duan ◽  
Xinping Xiao
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Prediction Models ◽  
Transportation Systems ◽  
Flow State ◽  
Matrix Perturbation ◽  
Grey Prediction ◽  
Flow Data ◽  
Short Term ◽  
Proposed Model

Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

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 An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow

2021 ◽  
Vol 11 (2) ◽  
pp. 143-151
Author(s):  
Feng Yu ◽  
◽  
Jinglong Fang ◽  
Bin Chen ◽  
Yanli Shao
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Neural Network Model ◽  
Incremental Learning ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Traffic flow prediction is very important for smooth road conditions in cities and convenient travel for residents. With the explosive growth of traffic flow data size, traditional machine learning algorithms cannot fit large-scale training data effectively and the deep learning algorithms do not work well because of the huge training and update costs, and the prediction accuracy may need to be further improved when an emergency affecting traffic occurs. In this study, an incremental learning based convolutional neural network model, TF-net, is proposed to achieve the efficient and accurate prediction of large-scale and short-term traffic flow. The key idea is to introduce the uncertainty features into the model without increasing the training cost to improve the prediction accuracy. Meanwhile, based on the idea of combining incremental learning with active learning, a certain percentage of typical samples in historical traffic flow data are sampled to fine-tune the prediction model, so as to further improve the prediction accuracy for special situations and ensure the real-time requirement. The experimental results show that the proposed traffic flow prediction model has better performance than the existing methods.

scholarly journals Short-time traffic flow prediction based on immune particle swarm neural networks

2021 ◽  
Author(s):  
Xiaomo Yu ◽  
Yuheng Kang ◽  
Zhou Shen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Swarm Algorithm ◽  
Short Time

Abstract The concentration-based selection mechanism in the immune theory can avoid the shortcomings of the particle swarm algorithm in balancing population convergence and individual diversity, and enable the improved particle swarm algorithm to optimize the configuration of BP neural network parameters and improve the accuracy of short-term traffic flow prediction. The simulation experiments show that the immune particle swarm optimized BP neural network can effectively improve the prediction accuracy of short-term traffic flow and reduce the prediction error.

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