Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis

Kolidakis, Stylianos Z.; Botzoris, George; Profillidis, Vassilios; Lemonakis, Panagiotis

doi:10.1016/j.eap.2019.08.002

Cited by 32 publications

(19 citation statements)

References 15 publications

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“…There are many statistical and machine learning tools for solving these tasks, including adaptive autoregressive models [Arnold et al, 1998], but in recent years recurrent neural networks have shown a great efficiency in solving such tasks due to their capability to analyze in real-time large volumes of nonlinear data with arbitrary longand short-term dependencies in the input sequences. Recent publications show a higher accuracy of recurrent neural networks in comparison with other models for solving the tasks of traffic flow data forecasting [Ermagun, Levinson, 2019, Kolidakis et al, 2019. Nowadays artificial neural networks (ANN) with long-short term memory are widely used for time-series data forecasting [Zhao et al, 2017;Bartlett, Han, 2018] using RMSE for evaluation of the accuracy [Yang et al, 2019;Yu et al, 2018].…”

Section: Traffic Flow Forecasting Approachmentioning

confidence: 99%

Modeling of the effective environment in the Republic of Tatarstan using transport data

Minnikhanov¹,

Anikin²,

Dagaeva³

et al. 2021

CRM

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Automated urban traffic monitoring systems are widely used to solve various tasks in intelligent transport systems of different regions. They include video enforcement, video surveillance, traffic management system, etc. Effective traffic management and rapid response to traffic incidents require continuous monitoring and analysis of information from these complexes, as well as time series forecasting for further anomaly detection in traffic flow. To increase the forecasting quality, data fusion from different sources is needed. It will reduce the forecasting error, related to possible incorrect values and data gaps. We implemented the approach for short-term and middle-term forecasting of traffic flow (5, 10, 15 min) based on data fusion from video enforcement and video surveillance systems. We made forecasting using different recurrent neural network architectures: LSTM, GRU, and bidirectional LSTM with one and two layers. We investigated the forecasting quality of bidirectional LSTM with 64 and 128 neurons in hidden layers. The input window size (1, 4, 12, 24, 48) was investigated. The RMSE value was used as a forecasting error. We got minimum RMSE = 0.032405 for basic LSTM with 64 neurons in the hidden layer and window size = 24.

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Section: Traffic Flow Forecasting Approachmentioning

confidence: 99%

Modeling of the effective environment in the Republic of Tatarstan using transport data

Minnikhanov¹,

Anikin²,

Dagaeva³

et al. 2021

CRM

View full text Add to dashboard Cite

show abstract

“…There has been a successful journey full of advancements and one of the most popular collaborator is neural network. Different sub-branches of neural network have been fused with SSA for achieving better forecasting, for instance, fuzzy/Elman/Laguerre neural network and SSA are combined for wind speed forecasting in [41][42][43], for road traffic forecasting in [44], for energy demand/load forecasting in [45], for water demand forecasting in [46], etc. The authors have proposed the Colonial Theory (CT) inspired SSA-CT approach back in [35], which incorporates CT for an improved "grouping" process of basic SSA.…”

Section: Introductionmentioning

confidence: 99%

Fusing Nature with Computational Science for Optimal Signal Extraction

Hassani

Yeganegi

Huang

2021

Stats

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Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.

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“…To increase the accuracy of time series forecasting, various methods have been developed to remove noise from raw data and to decompose any time series into its trend, its oscillatory components and its noise components. One of these methods is Singular Spectrum Analysis (SSA), which, for a given window length, decomposes any time series into various components that can either be trends, periodic oscillations or noise [4].…”

Section: Introduction 11 Rationalementioning

confidence: 99%

“…While trend is deducted, acknowledgement of subset time series with periodic components is the key mission for SSA to implement. Afterwards, residuals of leading components time series decomposition constitute the noise components [4].…”

Section: Introduction 11 Rationalementioning

confidence: 99%

Identifying the Optimum Forecasting Horizon to Apply the Singular Spectrum Analysis on Daily Road Traffic Volume Forecasts

Kolidakis¹,

Botzoris²

2021

MMEP

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The paper delivers an assessment of Singular Spectrum Analysis (SSA) forecasting ability for short- and medium-term forecasting horizon, on real time traffic volume data. The key study goal is to estimate forecasting pertinency for daily traffic volume, based upon measurements at toll station. The suggested methodology is tested on real data from Moschohorion and Pelasgia Toll Station – Greece, utilizing custom developed forecasting software toolbox. Applied research results confirm an advanced forecasting ability of proposed methodology for short-term forecasting horizon against medium term forecasting horizon, when performance is compared upon the statistical criteria of the coefficient of determination R2. The obtained results present that SSA forecasting model could provide a competent forecasting methodology for road traffic volume data.

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Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis

Cited by 32 publications

References 15 publications

Modeling of the effective environment in the Republic of Tatarstan using transport data

Modeling of the effective environment in the Republic of Tatarstan using transport data

Fusing Nature with Computational Science for Optimal Signal Extraction

Identifying the Optimum Forecasting Horizon to Apply the Singular Spectrum Analysis on Daily Road Traffic Volume Forecasts

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