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

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.

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Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Journal of Advanced Transportation ◽

10.1155/2019/4145353 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 37

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.

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An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity

Archives of Transport ◽

10.5604/01.3001.0014.2968 ◽

2020 ◽

Vol 54 (2) ◽

pp. 59-73

Author(s):

Yang Wang ◽

Yu Xiao ◽

Jianhui Lai ◽

Yanyan Chen

Keyword(s):

Missing Data ◽

Traffic Flow ◽

Intelligent Transportation Systems ◽

Domain Knowledge ◽

Rapid Development ◽

Transportation Systems ◽

Nearest Neighbour ◽

Flow Data ◽

Fundamental Parameters ◽

Determination Of Parameters

Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.

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Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2018/5469428 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Jeng-Fung Chen ◽

Shih-Kuei Lo ◽

Quang Hung Do

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Intelligent Transportation Systems ◽

Traffic Congestion ◽

Mean Squared Error ◽

Transportation Systems ◽

Wavelet Neural Network ◽

Percentage Error ◽

Short Term ◽

Fuzzy Wavelet Neural Network

Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell’s method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higherR, indicating that the FWNN model is a better predictor.

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Short-Term Traffic Flow Prediction of Highway Network Based on Network-Constrained Lasso and NN

MATEC Web of Conferences ◽

10.1051/matecconf/201816007003 ◽

2018 ◽

Vol 160 ◽

pp. 07003

Author(s):

Cong Wu ◽

Zhaozheng Chen ◽

Xiaofei Li

Keyword(s):

Neural Networks ◽

Traffic Flow ◽

Intelligent Transportation Systems ◽

Back Propagation ◽

Transportation Systems ◽

Time Data ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Highway Network

Accurate and timely traffic flow prediction is important for the successful deployment of intelligent transportation systems. Most of existing methods have not made good use of information from adjacent sections to analyse the trends of the object section. A new method for traffic flow prediction of highway network, namely network-constrained Lasso (Least absolute shrinkage and selection operator) and Neural Networks, was proposed. Unlike existing methods, our approach incorporated all the spatial and temporal information available in a highway network to carry our short-term traffic flow prediction for the objective section. To capture the spatial correlation of traffic network, the Laplacian matrix was introduced to describe the highway network structure. Subsequently, a network-constrained Lasso method was applied for sparse variable selection. With the extracted historic and real-time data, the back propagation neural networks were implemented to predict traffic flow at different time intervals in future. The experimental results verified that the proposed method could achieve above 90% average accuracy in the 30-minutes speed predictions for 78 road sections.

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Graph Convolutional Network: Traffic Speed Prediction Fused with Traffic Flow Data

Sensors ◽

10.3390/s21196402 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6402

Author(s):

Duanyang Liu ◽

Xinbo Xu ◽

Wei Xu ◽

Bingqian Zhu

Keyword(s):

Traffic Flow ◽

Intelligent Transportation Systems ◽

Transportation Systems ◽

Flow Data ◽

Convolutional Network ◽

Convolutional Networks ◽

Speed Prediction ◽

Traffic Speed ◽

Real World Datasets ◽

Relationship Of

Traffic speed prediction plays an important role in intelligent transportation systems, and many approaches have been proposed over recent decades. In recent years, methods using graph convolutional networks (GCNs) have been more promising, which can extract the spatiality of traffic networks and achieve a better prediction performance than others. However, these methods only use inaccurate historical data of traffic speed to forecast, which decreases the prediction accuracy to a certain degree. Moreover, they ignore the influence of dynamic traffic on spatial relationships and merely consider the static spatial dependency. In this paper, we present a novel graph convolutional network model called FSTGCN to solve these problems, where the model adopts the full convolutional structure and avoids repeated iterations. Specifically, because traffic flow has a mapping relationship with traffic speed and its values are more exact, we fused historical traffic flow data into the forecasting model in order to reduce the prediction error. Meanwhile, we analyzed the covariance relationship of the traffic flow between road segments and designed the dynamic adjacency matrix, which can capture the dynamic spatial correlation of the traffic network. Lastly, we conducted experiments on two real-world datasets and prove that our model can outperform state-of-the-art traffic speed prediction.

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

Complexity ◽

10.1155/2017/3515272 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 6

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201873 ◽

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.

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

Journal of Advanced Transportation ◽

10.1155/2018/3876841 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

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.

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Parametric Evaluation of Internet of Things Applications to Freight Transportation Using Model-Based Systems Engineering

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211049134 ◽

2021 ◽

pp. 036119812110491

Author(s):

Aldo D. Fabregas ◽

Paul Crawford ◽

Rodrigo Mesa ◽

Agustina Calatayud

Keyword(s):

Internet Of Things ◽

Systems Engineering ◽

Intelligent Transportation Systems ◽

Management Plan ◽

Transportation Systems ◽

Interface Control ◽

Current Trends ◽

Industrial Internet ◽

Proposed Model ◽

Model Based Systems Engineering

The intended purpose of an intelligent transportation systems (ITS) project is to automate operations through device-to-device connectivity. These devices generally represent stakeholder’s endpoints and expose interfaces to automated operations. Current trends in communication allow more applications and devices to perform functions traditionally allocated to the transportation ITS infrastructure. This connected environment of industrial internet of things presents design challenges because of the diversity of stakeholders, interfaces, and the messages among them. This may require new ways in which ITS planning can handle the scale and complexity of these highly connected systems. This work focuses on the modeling of the architecture of a freight-focused ITS application, including maritime ports. The proposed model integrates stakeholders, behaviors, and messages using the Systems Modeling Language (SysML). The key contribution of this work is to demonstrate the creation of an executable SysML model for an ITS application without sacrificing the typical Systems Engineering Management Plan artifacts (e.g., requirements traceability matrices and interface control documents). At the same time, the proposed model provides a re-usable pattern to support parametric analysis of candidate architectures with respect to any measure of effectiveness. This allows establishing a single source of architecture definition and having multiple architecture specializations depending on the measure of effectiveness being evaluated. Recommendations for implementation and integration with existing ITS tools are provided.

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