A Hybrid Data-Driven Framework for Spatiotemporal Traffic Flow Data Imputation

Wang, Peixiao; Hu, Tao; Gao, Fei; Wu, Ranchao; Guo, Wei; Zhu, Xinyan

doi:10.1109/jiot.2022.3151238

Cited by 27 publications

(18 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1 3 2) * (2 5) is expressed as follows. Now we look at Eq.31: Study using Gang neurons [46]:"A Hybrid Data-Driven Framework for Spatiotemporal Traffic Flow Data Imputation"(see Fig. 29)…”

Section: A Polynomial Convolution and Dendrite In This Papermentioning

confidence: 99%

It may be time to improve the neuron of artificial neural network

Liu¹

2023

Preprint

View full text Add to dashboard Cite

Artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence in recent years. The neuron of ANNs was designed by the stereotypical knowledge of biological neurons 70 years ago. Artificial Neuron is expressed as f(wx+b) or f(WX). This design does not consider dendrites’ information processing capacity. However, some recent studies show that biological dendrites participate in the pre-calculation of input data. Concretely, biological dendrites play a role in extracting the interaction information among inputs (features). Therefore, it may be time to improve the neuron of ANNs. In this study, some dendritic modules with excellent properties are proposed and added to artificial neurons to form new neurons named Gang neurons. E.g., The dendrite function can be expressed as Wi,i-1Ai-1 ○ A0|1|2|...|i-1 . The generalized new neuron can be expressed as f(W(Wi,i-1Ai-1 ○ A0|1|2|...|i-1)).The simplified new neuron be expressed as f(∑(WA ○ X)). After improving the neurons, many networks can be tried. This paper shows some basic architecture for reference in the future. Up to now, others and the author have applied Gang neurons to various fields, and Gang neurons show excellent performance in the corresponding fields. Interesting things: (1) The computational complexity of dendrite modules (Wi,i-1Ai-1 ○ Ai-1) connected in series is far lower than Horner's method. Will this speed up the calculation of basic functions in computers? (2) The range of sight of animals has a gradient, but the convolution layer does not have this characteristic. This paper proposes receptive fields with a gradient. (3) The networks using Gang neurons can delete Fully-connected Layer. In other words, the parameters in Fully-connected Layers are assigned to a single neuron, which reduces the parameters of a network for the same mapping capacity. (4) ResDD(ResDD modules+One Linear module) can replace the current ANNs’ Neurons. ResDD has controllable precision for better generalization capability. Gang neuron code is available at https://github.com/liugang1234567/Gang-neuron.

show abstract

“…(1 3 2) * (2 5) is expressed as follows. Now we look at Eq.31: Study using Gang neurons [46]:"A Hybrid Data-Driven Framework for Spatiotemporal Traffic Flow Data Imputation"(see Fig. 29)…”

Section: A Polynomial Convolution and Dendrite In This Papermentioning

confidence: 99%

It may be time to improve the neuron of artificial neural network

Liu¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides that, studies such as [7], [18], [19], and [20] make use of street cameras and vehicle identification software in order to capture traffic data. Using cameras has the advantage of being able to analyze certain traffic parameters more accurately, such as the traffic flow, average gaps between vehicles during different traffic hours, as well as traffic accidents and other such events.…”

Section: ) Sensors and Camerasmentioning

confidence: 99%

“…Meanwhile, [20] designs a framework combining matrix modeling and factorization and conducting matrix decomposition before using a dendrite neural network to fuse the information to obtain the final imputed data. Besides being another ensemble model, the proposed neural network model was recently proposed by [96], of which the code is provided in their paper.…”

Section: Ensemble Modelmentioning

confidence: 99%

“…However, collecting a significant number of traffic data requires a significant amount of time, and as such, researchers might not have a lot of data points when compared to the available public datasets. This can be seen from some of the reviewed papers, such as [69], [19], [7], [91], [65], [66], and [20], whereby their datasets range from 14 days to 2 months. The quantity of data collected within this short period would prove difficult for models which rely on many data to be properly trained.…”

Section: A Limitation Of Datamentioning

confidence: 99%

See 1 more Smart Citation

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

2023

View full text Add to dashboard Cite

Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization or a specific neural network model. While there are review papers covering singular topics regarding missing data, there are none in the field of traffic, to the best of our knowledge, that introduces the process of missing data collection and the viability of the traffic data collected while also broadly covering the popularly used models of recent years. This has led to non-uniformity of the terms used in missing data imputation, limited research in areas where datasets are not available, and a narrowed view of the methods used for data imputation. Hence, this paper aims to standardize the terms used in missing data classifications, look into the limitations of using available public or private datasets for urban traffic research, and discuss popular statistical and data-driven methods used by recent AI and ITS papers. It was found that tensor decomposition-based methods are the most popular for missing data imputation, followed by Generative Adversarial Networks and Graph Neural Networks, all of which rely on a large training dataset. Meanwhile, Probability Principle Component Analysis (PPCA) methods provide valuable insights via traffic analysis and are used for real-time traffic imputation. This paper also highlights the need for more efficient and reliable methods for traffic data collection, such as online APIs.

show abstract

“…With the rapid rise of deep learning, relevant studies that applied graph-based networks and dendritic neural networks to reconstruct missing data achieved good results [17][18][19][20]. Wang et al [21] proposed a hybrid data-driven framework to impute the missing information in traffic flow. Yang et al [22] developed an online framework for more accurate online prediction and imputation performance, utilizing the deep learning model and graph Laplacian to capture the temporal dependency in time series data and spatial correlations among network sensors, respectively.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

Ding

Zheng

2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation performance. Fortunately, it is found that traffic data admits the nonlocal self‐similarity (NSS) prior. This paper incorporates the global and nonlocal low‐rank priors of traffic data and proposes a tensor completion model for spatiotemporal traffic data imputation. To be specific, the proposed method uses tensor ring (TR) decomposition with an enhanced representation capability to characterize the global low‐TR‐rank prior of traffic data, e.g. the correlation of sensor and time modes of the tensor (i.e. traffic data). An implicit plug‐and‐play (PnP)‐based regularization is further utilized to exploit the NSS prior, which depicts the nonlocal similar traffic data patterns. Furthermore, the proximal alternating minimization algorithm under the PnP framework is derived to solve this model. The experiment results on various datasets and missing scenarios show the superiority of the proposed model.

show abstract

A Hybrid Data-Driven Framework for Spatiotemporal Traffic Flow Data Imputation

Cited by 27 publications

References 37 publications

It may be time to improve the neuron of artificial neural network

It may be time to improve the neuron of artificial neural network

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

Contact Info

Product

Resources

About