A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems

Diao, Zulong; Zhang, Dafang; Wang, Xin; Xie, Kun; He, Simai; Lu, Xin; Li, Yanbiao

doi:10.1109/tits.2018.2841800

Cited by 69 publications

(27 citation statements)

References 38 publications

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“…The detailed information on AHC algorithm can refer to [33]. The multi-step ahead prediction is implemented as a step-bystep prediction up to four step-ahead (i.e., 15,30,45, and 60-min), that is, using the predicted flow of the current time step to predict the flow in the next time step.…”

Section: B Target Station Selectionmentioning

confidence: 99%

“…Deep belief network (DBN) [26], LSTM NN [27], radial basis function networks (RBFNN) [28] were also reported in literature. Although deep learning is popular in predictions, many studies have provided evidence on supporting simple statistical or machine learning models [29]- [30]. Developing appropriate functions to match the time series patterns is the key to effective predictions.…”

Section: Introductionmentioning

confidence: 99%

“…The hybrid model yielded better prediction than the individual model by selecting the best 'fit' model predictions with real-time observations [20]. Diao et al [30] combined the outputs of an efficient tracking model and a novel Gaussian process model to predict the real-time passenger demand, and the ensemble prediction can improve the accuracy by 20%-50% on average. Ding et al [31] integrated the outputs of ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH) to estimate the volatility of the passenger demand.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Yang

et al. 2020

IEEE Access

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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,

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Section: B Target Station Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

Yang

et al. 2020

IEEE Access

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“…For example, Sun et al [42] decomposed flow data using WT and made predictions for each frequency component via SVM. Diao et al [43] utilized discrete wavelet transform (DWT) for decomposition, making predictions by employing a tracking model for the low-frequency component and a Gaussian process model for the high-frequency component. Zhang et al [44] employed motif-based graph convolutional recurrent neural network (Motif-GCRNN) and Autoregressive Moving Average (ARMA) to model the low-frequency and high-frequency components, respectively.…”

Section: Wavelet Transform Integration In Traffic Forecastingmentioning

confidence: 99%

A Hybrid Deep Learning-Based Traffic Forecasting Approach Integrating Adjacency Filtering and Frequency Decomposition

et al. 2020

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Traffic forecasting in urban area has attracted substantial attention in recent years due to its significant assitance for traffic dispatching and trip planning. However, this task is very challenging due to the complex dependencies inherent in the traffic process. In our study, recent and periodic dependencies are identified and used to describe the corresponding near-term and long-term effects of historical traffic data on future traffic states. Subsequently, during the recent dependency modeling, each road is found to correlate with its adjacent roads through traffic flow diffusion, then the correlation intensities are quantified and used to choose strongly correlated roads to build a critical road sequence. While for the periodic dependency modeling, since the historical speed series of a road segment exhibits multi-frequency attributes (i.e., low-frequency daily period and high-frequency stochastic fluctuation), wavelet transform is conducted to decompose the original speed series into low and high-frequency sub-series. On these bases, we propose a hybrid traffic speed forecasting model, flow and wavelet-integrated spatio-temporal network (FW-STN). In the FW-STN, the recent features are captured by the convolutional neural network (CNN) with near-term traffic data from the derived critical road sequence, and the periodic features are captured by the long shortterm memory (LSTM) with the low and high-frequency sub-series. Both the recent and periodic features are then fused to conduct the final prediction. Experimental results on real traffic data show that the proposed approach outperforms nine state-of-the-art methods (with improvements of 3% ∼15% in mean average percentage error).

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“…On the contrary, the urbanisation process and urban population expansion bring great pressure to the urban traffic management that result in traffic congestion [6]. This is a burden for both TALiSMaN and TALiSMaN-Green as the scheme requires real-time data to transfer among streetlights.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control for Distributed Smart Street Light Network

Lee¹,

Ping²,

Eng³

2019

IJACSA

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With the advent of smart city that embedded with smart technology, namely, smart streetlight, in urban development, the quality of living for citizens has been vastly improved. TALiSMaN is one of the promising smart streetlight schemes to date, however, it possesses certain limitation that led to network congestion and packet dropped during peak road traffic periods. Traffic prediction is vital in network management, especially for real-time decision-making and latency-sensitive application. With that in mind, this paper analyses three real-time short-term traffic prediction models, specifically simple moving average, exponential moving average and weighted moving average to be embedded onto TALiSMaN, that aim to ease network congestion. Additionally, the paper proposes traffic categorisation and packet propagation control mechanism that uses historical road traffic data to manage the network from overload. In this paper, we evaluate the performance of these models with TALiSMaN in simulated environment and compare them with TALiSMaN without traffic prediction model. Overall, weighted moving average showed promising results in reducing the packet dropped while capable of maintaining the usefulness of the streetlight when compared to TALiSMaN scheme, especially during rush hour.

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A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems

Cited by 69 publications

References 38 publications

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

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

A Hybrid Deep Learning-Based Traffic Forecasting Approach Integrating Adjacency Filtering and Frequency Decomposition

Predictive Control for Distributed Smart Street Light Network

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