Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection

Guo, Chang; Li, Demin; Chen, Xuemin

doi:10.3390/app13158973

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…In the topic's context of our research, several prominent works can be distinguished on creating models [7][8][9][10] and modeling traffic flows in urban highway network sections [11][12][13][14][15][16].…”

Section: General Models For Solving the Real-time Traffic Management ...mentioning

confidence: 99%

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Modeling of Traffic Flows Sustainability on Highway Network Stretches

Vojtov,

Muzylyov,

Karnaukh

et al. 2023

Applied Sciences

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Assessing the transport flow robustness is a significant aspect of a qualitative solution to traffic management problems. Therefore, management should be based on appropriate criteria, accounting for different factors characterizing traffic flow sustainability. That’s why it is crucial to establish the impact rate for each group of factors on the robustness criterion. Therefore, the current study aims to obtain the dependence of the criterion changes for traffic flow sustainability on the traffic jam occurrence when changing the gradients’ product of traffic flow density and its speed. The value of the robustness criterion allows for performing an impact rating for input factors on traffic flow sustainability. All factors affecting transport flow robustness are divided into three groups. Based on simulation results, factors rating that impact the robustness margin value of the traffic flow is presented. Length and weight of automobiles are at first place according to impact terms on the sustainability loss of the traffic flow. In second place of impact on sustainability loss are the temporary factors group and factors group that considers the roadway environment’s infrastructure. Hence, the results can be used to analyze sustainability traffic flows in controlled highway network stretches and develop measures to increase sustainability reserve.

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Section: General Models For Solving the Real-time Traffic Management ...mentioning

confidence: 99%

“…Prediction of traffic flow parameters is provided according to signal retrospective [16]. In this case, the model accuracy could be doubted, especially when there are no signals or have hopping values that exceed the extreme traffic flow behavior.…”

Section: General Models For Solving the Real-time Traffic Management ...mentioning

confidence: 99%

Modeling of Traffic Flows Sustainability on Highway Network Stretches

Vojtov,

Muzylyov,

Karnaukh

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Long-term time series prediction refers to the prediction of sequence changes over a longer period of time based on historical data, like predicting 24 points or more, which is indicated in Informer [1], Autoformer [2], and Fedformer [3]. It has widespread applications in fields such as electricity forecasting [1,2], traffic flow prediction [4][5][6], inventory control [7], and healthcare management [8,9]. For example, in the energy sector, long-term forecasting is used to optimize the operation and management of the power grid, improving energy efficiency and reliability.…”

Section: Introductionmentioning

confidence: 99%

DESTformer: A Transformer Based on Explicit Seasonal–Trend Decomposition for Long-Term Series Forecasting

Wang,

Zhu,

Kang

2023

Applied Sciences

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Seasonal–trend-decomposed transformer has empowered long-term time series forecasting via capturing global temporal dependencies (e.g., period-based dependencies) in disentangled temporal patterns. However, existing methods design various auto-correlation or attention mechanisms in the seasonal view while ignoring the fine-grained temporal patterns in the trend view in the series decomposition component, which causes an information utilization bottleneck. To this end, a Transformer-based seasonal–trend decomposition methodology with a multi-scale attention mechanism in the trend view and a multi-view attention mechanism in the seasonal view is proposed, called DESTformer. Specifically, rather than utilizing the moving average operation in obtaining trend data, a frequency domain transform is first applied to extract seasonal (high-frequency) and trend (low-frequency) components, explicitly capturing different temporal patterns in both seasonal and trend views. For the trend component, a multi-scale attention mechanism is designed to capture fine-grained sub-trends under different receptive fields. For the seasonal component, instead of the frequency-only attention mechanism, a multi-view frequency domain (i.e., frequency, amplitude, and phase) attention mechanism is designed to enhance the ability to capture the complex periodic changes. Extensive experiments are conducted on six benchmark datasets covering five practical applications: energy, transportation, economics, weather, and disease. Compared to the state-of-the-art FEDformer, our model shows reduced MSE and MAE by averages of 6.5% and 3.7%, respectively. Such experimental results verify the effectiveness of our method and point out a new way towards handling trends and seasonal patterns in long-term time series forecasting tasks.

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A Deep Ensemble Approach for Long-Term Traffic Flow Prediction

Cini,

Aydin

2024

Arab J Sci Eng

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In the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result, traffic flow prediction started to attract attention as an important research area. However, despite the extensive literature, traffic flow prediction still remains as an open research problem, specifically for long-term traffic flow prediction. Compared to the models developed for short-term traffic flow prediction, the number of models developed for long-term traffic flow prediction is very few. Based on this shortcoming, in this study, we focus on long-term traffic flow prediction and propose a novel deep ensemble model (DEM). In order to build this ensemble model, first, we developed a convolutional neural network (CNN), a long short-term memory (LSTM) network and a gated recurrent unit (GRU) network as deep learning models, which formed the base learners. In the next step, we combine the output of these models according to their individual forecasting success. We use another deep learning model to determine the success of the individual models. Our proposed model is a flexible ensemble prediction model that can be updated based on traffic data. To evaluate the performance of the proposed model, we use a publicly available dataset. Experimental results show that the developed DEM model has a mean square error of 0.06 and a mean absolute error of 0.15 for single-step prediction; it shows that achieves a mean square error of 0.25 and a mean absolute error of 0.32 for multi-step prediction. We compared our proposed model with many models in different categories; individual deep learning models (i.e., LSTM, CNN, GRU), selected traditional machine learning models (i.e., linear regression, decision tree regression, k-nearest-neighbors regression) and other ensemble models such as random-forest regression. These results also support the claim that ensemble learning models perform better than individual models.

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Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection

Cited by 3 publications

References 34 publications

Modeling of Traffic Flows Sustainability on Highway Network Stretches

Modeling of Traffic Flows Sustainability on Highway Network Stretches

DESTformer: A Transformer Based on Explicit Seasonal–Trend Decomposition for Long-Term Series Forecasting

A Deep Ensemble Approach for Long-Term Traffic Flow Prediction

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