A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction

Liu, Yan; Wang, Xiaokang; Hou, Wenhua; Liu, Hui; Wang, Jian‐qiang

doi:10.1016/j.knosys.2022.109760

Cited by 19 publications

(2 citation statements)

References 50 publications

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“…With the development of intelligent transportation systems, sensor technology, computer technology, and other advanced technologies have found increasing applications in the field of transportation. However, these technologies are still insufficient to meet the demands of intelligent transportation development, and traffic congestion and accidents remain major issues that need to be addressed [1,2]. Traffic flow prediction, as a crucial component of intelligent transportation systems, can help with traffic light phase control, dynamic path planning for vehicle navigation, and other aspects that can reduce accidents, alleviate traffic congestion, and improve traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Guo

Liu

et al. 2023

Applied Sciences

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Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that utilizes variational mode decomposition (VMD) and the auto-correlation mechanism for long-term prediction. In view of the periodic and stochastic characteristics of traffic flow, VMD is able to decompose the data into intrinsic mode functions with different frequencies, which in turn helps the model extract the internal features of the data and better capture the changes of traffic flow data in the cycle. Additionally, we improve the residual structure by adding a convolutional layer to propose a correction module and use it together with the auto-correlation mechanism to jointly build an encoder and decoder to extract features from different data components (intrinsic mode functions) and fuse the extracted features for output. To meet the requirements of long-term forecasting, we set the traffic flow forecast length to 4 levels: 96, 192, 336, and 720. We validated our model using the departure statistics dataset of a taxi parking lot at Beijing Capital International Airport and achieved the best prediction performance in terms of mean squared error and mean absolute error, compared to the baseline model.

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Section: Introductionmentioning

confidence: 99%

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Guo

Liu

et al. 2023

Applied Sciences

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show abstract

“…The conclusion process must be as competence as a human decision making for potential derivation. Due to the ability of fuzzy set theory to mimic human inference [23], experience could be put in the form of fuzzy rules and according to fuzzy measurement, it facilitates the diagnosis and reasoning of a complex decision [24], [25]. Deep learning methods on the other hand, do not offer such adaptability and may not be able to deal with the nuances and variations of uncertain data well [25].…”

Section: Introductionmentioning

confidence: 99%

Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera

et al. 2023

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Crime prediction in video-surveillance systems is required to prevent incident and protect assets. In this sense, our article proposes first artificial intelligence approach for Robbery Behavior Potential (RBP) prediction and detection in an indoor camera. Our method is based on three detection modules including head cover, crowd and loitering detection modules for timely actions and preventing robbery. The two first modules are implemented by retraining YOLOV5 model with our gathered dataset which is annotated manually. In addition, we innovate a novel definition for loitering detection module which is based on DeepSORT algorithm. A fuzzy inference machine renders an expert knowledge as rules and then makes final decision about predicted robbery potential. This is laborious due to: different manner of robber, different angle of surveillance camera and low resolution of video images. We accomplished our experiment on real world video surveillance images and reaching the F1-score of 0.537. Hence, to make an experimental comparison with the other related works, we define threshold value for RBP to evaluate video images as a robbery detection problem. Under this assumption, the experimental results show that the proposed method performs significantly better in detecting the robbery as compared to the robbery detection methods by distinctly report with F1-score of 0.607. We strongly believe that the application of the proposed method could cause reduction of robbery detriment in a control center of surveillance cameras by predicting and preventing incident of robbery. On the other hand, situational awareness of human operator enhances and more cameras can be managed.

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FST-GNN: A Fuzzy-Based Spatial-Temporal Graph Neural Network for Traffic Flow Prediction During Emergent Epidemics

An,

Zhang,

Fang

et al. 2024

Lecture Notes in Networks and Systems

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A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction

Cited by 19 publications

References 50 publications

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera

FST-GNN: A Fuzzy-Based Spatial-Temporal Graph Neural Network for Traffic Flow Prediction During Emergent Epidemics

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