scholarly journals Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Huiming Duan ◽  
Xinping Xiao ◽  
Lingling Pei
Keyword(s):  
Mechanical Properties ◽  
Traffic Flow ◽  
Dynamic Characteristics ◽  
Flow System ◽  
Structural Parameters ◽  
Traffic Information ◽  
Gray Model ◽  
Flow State ◽  
Flow Data ◽  
Short Term

The traffic-flow system has basic dynamic characteristics. This feature provides a theoretical basis for constructing a reasonable and effective model for the traffic-flow system. The research on short-term traffic-flow forecasting is of wide interest. Its results can be applied directly to advanced traffic information systems and traffic management, providing real-time and effective traffic information. According to the dynamic characteristics of traffic-flow data, this paper extends the mechanical properties, such as distance, acceleration, force combination, and decomposition, to the traffic-flow data vector. According to the mechanical properties of the data, this paper proposes four new models of structural parameters and component parameters, inertia nonhomogenous discrete gray models (referred to as INDGM), and analyzes the important properties of the model. This model examines the construction of the inertia nonhomogenous discrete gray model from the mechanical properties of the data, explaining the classic NDGM modeling mechanism in the meantime. Finally, this paper analyzes the traffic-flow data of Whitemud Drive in Canada and studies the relationship between the inertia model and the traffic-flow state according to the data analysis of the traffic-flow state. A simulation accuracy and prediction accuracy of up to 0.0248 and 0.0273, respectively, are obtained.

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.

SHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK

Transport ◽  
2013 ◽  
Vol 30 (4) ◽  
pp. 397-405 ◽  
Author(s):  
Kranti Kumar ◽  
Manoranjan Parida ◽  
Vinod Kumar Katiyar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Volume ◽  
Flow Data ◽  
Highway Traffic ◽  
Short Term ◽  
Artificial Neural ◽  
Input Variables

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

scholarly journals Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 88
Author(s):  
Xiaoyuan Wang ◽  
Junyan Han ◽  
Chenglin Bai ◽  
Huili Shi ◽  
Jinglei Zhang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Free Flow ◽  
Traffic Information ◽  
Flow State ◽  
Simulation Framework ◽  
Car Following ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Vehicle Information

With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment.

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.

Fractal Dimension in Time Series of Expressway Traffic Flow

2013 ◽  
Vol 838-841 ◽  
pp. 2088-2091
Author(s):  
Wei Zhang ◽  
Xin He
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Traffic Flow ◽  
Flow Rate ◽  
Fractal Theory ◽  
Operation Management ◽  
Flow State ◽  
Flow Data ◽  
Input Sample ◽  
Statistical Results

The correlation and Hausdroff dimensions of traffic flow and speed series are analyzed based on the Fractal theory. Using the traffic flow data of four typical expressways in China as input sample, the data statistical results indicates that both synchronized and free flow state are fractal and chaotic. In addition, traffic flow rate and speed time series are determined by different intervals and then G-P arithmetic is applied to estimate their correlation dimensions and the Hausdroff dimension. The results also illustrates that Fractal dimension could by clearly identified, which provides a useful tool for expressway operation management and facilities provision.

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 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.

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