A graph deep learning method for short‐term traffic forecasting on large road networks

Zhang, Yang; Cheng, Tao; Ren, Yibin

doi:10.1111/mice.12450

Cited by 83 publications

(46 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Yu et al 30 proposed a spatiotemporal graph convolutional network to tackle the graph‐structured time series prediction in transportation networks, which enabled faster training speed with fewer parameters. Zhang et al 36 also developed a spatial‐temporal graph inception residual network to achieve short‐term traffic forecasting on large road networks. Motivated by it, this paper will for the first time apply GCN to model the structural dependencies of traffic flows in an IP traffic network.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

Zhao

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Summary Through accurate network‐wide traffic prediction, network operators can agilely manage resources and improve robustness by proactively adapting to new traffic patterns, especially for traffic engineering, capacity planning and quality of service provisioning. However, due to the proliferation of backbone network traffic as well as the complexity and dynamics of network communication behavior, accurate and effective network‐wide traffic prediction is challenging. To address the challenges, this paper focuses on short‐term traffic matrix (TM) prediction in large‐scale IP backbone networks. In order to improve the prediction performance, a novel spatiotemporal graph convolutional recurrent network (SGCRN)—a deep learning framework that incorporates both spatial and temporal dependencies of traffic flows, is proposed to implement TM prediction with high accuracy and efficiency. By learning network‐wide traffic as graph‐structured TM time series, SGCRN jointly utilizes graph convolutional networks (GCN) and gated recurrent units (GRU) networks to extract comprehensive spatiotemporal correlations among traffic flows. Specifically, SGCRN employs GCNs to identify structural spatial features of traffic flows by considering topological properties, and utilizes GRUs to implement temporal features learning by considering short and long‐term dynamics of traffic flows. Extensive experimental results on the inter‐Points of Presence network traffic data from four real IP backbone networks show that SGCRN can effectively predict short‐term network‐wide TM with superior accuracy compared with other four widely used traffic prediction methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

Zhao

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…However, these models cannot accurately capture the nonlinear rules of traffic flow [5]. Short-term traffic flow has strong volatility and nonlinearity, so that it is hard to get the suitable distribution and function for traffic flow [2,5]. Thus, the support vector machine [12][13][14], neural network [15] and deep learning [16][17][18][19][20][21][22] have been widely used because of strong nonlinear fitting ability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As stated earlier, the transfer probability matrix with w steps can be given by (2). Similarly, the traffic volumes at the W time intervals before time interval t are selected, the corresponding states at these W time intervals are the initial states transferring to the state at time interval t, and the row vector denoted by…”

Section: Membership Degree-based Markov Modelmentioning

confidence: 99%

See 1 more Smart Citation

Period Division-Based Markov Models for Short-Term Traffic Flow Prediction

Yao

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

Short-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this paper proposes a membership degree-based Markov (MM) model and two period division-based Markov (PM and PW) models. The MM model introduces the membership degree to determine the state of traffic flow. The PM and PW models introduce the Fisher optimal division method to divide one day into several periods based on traffic flow data. Then, the period division-based Markov models integrate the Markov (CM) or weighted Markov (WM) model with the MM model to predict traffic volumes during different periods. The impacts of vehicle type on traffic flow prediction are also discussed. The proposed models are verified using the field data. The results show that: (1) the PM and PW models both perform better than the CM, WM, state membership degree-based Markov and weighted state membership degreebased Markov models; (2) the PW model sometimes performs better than the backward propagation (BP) neural network; (3) when traffic flow data are distinguished by vehicle type, the performance of the PM and PW models can be improved. It is suggested to adopt the proposed period division-based Markov models to predict traffic flow with the concern of vehicle type, so that more accurate traffic flow information can be provided for traffic management and route guidance. INDEX TERMS short-term traffic flow; prediction; Markov models; period division; ordered clustering; traffic flow pattern; vehicle type

show abstract

“…Deep learning models have gained increasing interest for its extraordinary mapping ability for big data. For example, Hao et al [22] constructed a sequence to sequence model embedded with the attention mechanism to predict alighting passengers in a largescale metro system; Polson et al [23] developed a deep neural network (DNN) to predict traffic flow under abnormal conditions; Tsai et al [24] combined simulated annealing (SA) algorithm and DNN to predict bus passenger demand; Zhang et al [25] utilized a spatial-temporal graph inception residual network to predict the network-based traffic flow. Deep belief network (DBN) [26], LSTM NN [27], radial basis function networks (RBFNN) [28] were also reported in literature.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Zhao

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types of passenger flows with varied characteristics in urban railway systems. The Seasonal and Trend decomposition using Loess (STL) is used to decompose the passenger flow into the seasonal, trend, and residual time series, representing the constant, long-term fluctuant, and stochastic passenger demand patterns. The approximate entropy (ApEn) is utilized to quantify the predictability of each component. After that, the Holt-Winters (HW) method is developed to predict the seasonal and trend components that both with high predictability. The long short-term memory networks (LSTM NN) are proposed to predict the residual with low predictability. The outputs from these models are combined to predict the short-term passenger flow. We assess the model performance with entry, exit, and transfer passenger flows at the target stations, which are selected via the agglomerative hierarchical clustering (AHC) algorithm in the Shanghai Metro. Compared with three representative models, the results show that 1) the decomposition-based hybrid model performs well for both one-step and multi-step predictions in terms of accuracy and robustness; 2) ApEn is useful for choosing appropriate prediction models. INDEX TERMS Short-term metro passenger flow prediction, Seasonal and Trend decomposition using Loess (STL)-based model, approximate entropy (ApEn), agglomerative hierarchical clustering (AHC) algorithm I. Yangyang Zhao received the B.E. degree and the M.E. degree in transportation engineering from Chang'an University, Xi'an, Shaanxi, China, in 2014 and 2017. He is currently pursuing the Ph.D. degree in transportation planning and management with the school of transportation and logistics,

show abstract

A graph deep learning method for short‐term traffic forecasting on large road networks

Cited by 83 publications

References 58 publications

Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

Period Division-Based Markov Models for Short-Term Traffic Flow Prediction

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Contact Info

Product

Resources

About