Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model

Li, Fulin; Du, Bowen; Wang, Yonggang; Qin, Lingqiao; Tan, Huachun

doi:10.1016/j.knosys.2020.105592

Cited by 71 publications

(43 citation statements)

References 34 publications

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“…The RMSE values for three conditions are 0.0481, 0.0356, and 0.0954. Li et al [128] propose a multimodal DL model incorporating feature fusion to perform missing value estimation from heterogeneous traffic data. An experiment is conducted, and the correlation between missing data and accuracy is measured.…”

Section: ) Hybrid Algorithmmentioning

confidence: 99%

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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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Section: ) Hybrid Algorithmmentioning

confidence: 99%

“…This evaluation technique is scale-independent [140] and extremely useful for model evaluations and gives the same weight to the error [136]. The formula to calculate MAE as below [128]:…”

Section: ) Mean Absolute Error (Mae)mentioning

confidence: 99%

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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“…The interpolation methods regress the model of traffic state relationships by analyzing the spatial-temporal distribution of the traffic. There are several common interpolation methods for estimating traffic state such as Probabilistic principal component analysis (PPCA) [26], [27], tensor decomposition [28]- [31], Convolutional neural network (CNN) [32], [33], auto-encoders [34]- [36], Fuzzy neural network [37], Random forest [38]. L. Qu et al [26] employed the PPCA to estimate the traffic state by extracting the periodic spatial-temporal dependencies in traffic flow.…”

Section: B Missing Data Imputationmentioning

confidence: 99%

“…X. Chen et al [5] used the Bayesian probabilistic matrix factorization to derive missing data based on the similarity of spatial-temporal traffic states. L. Li et al [36] used two parallel auto-encoders to capture the spatial-temporal dependencies of the traffic state. These methods can deal with the problem of lost data with a low missing rate.…”

Section: B Missing Data Imputationmentioning

confidence: 99%

Traffic State Estimation of Bus Line With Sparse Sampled Data

et al. 2020

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The traffic state of the bus line is the information basis for the bus company to make bus dispatch and travel time prediction. However, the bus GPS data is severely sparse in time and space coverage of traffic state, due to the long data sampling time interval and low bus departure frequency. Because of ignoring the severe sparseness of the bus data, the existing traffic state methods cannot reconstruct the traffic state accurately. To deal with this problem, a new traffic state estimation method for the bus line, named GAN_BS, is proposed. First, an improved generative adversarial network (GAN-I) is used to generate reasonable bus data. GAN-I aims to find the probability space of the data distribution under sparse sampling. And to reduce the size of the latent space of data, the traffic knowledge is introduced as prior information layers. Then, a traffic adaptive bilateral smoothing method (BS) is used to map discrete bus data into the continuous traffic state. The BS convolves data with a bilateral kernel, which multiplies the local action kernel with a mask of traffic state similarity. Therefore, the BS can maintain transitions between different traffic patterns while separating noise from traffic state. Finally, a set of numerical experiments are performed on the real bus data set in Changchun. The results show that the GAN-I can accurately reproduce the traffic state when the missing rate of data exceeds 50%.

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“…Collected through various paths depending on the device or observation target, health data has different occurrence cycles, units, and shapes, so it is difficult to integrate it into one data set [25]. In addition, data is distributed across many institutions and personal devices.…”

Section: Hybrid Multi-modal Deep Learning Using Collaborative Comentioning

confidence: 99%

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

Kim

Chung

2020

IEEE Access

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A health model based on data has various missing values depending on the user situation, and the accuracy of a health model requiring variables that the user cannot collect appears low. A deep learning health model is fitted by learning weights to increase accuracy. This implies that additional learning is required when the environment changes, such as when new variables are added or deleted depending on the user situation. In the process of applying a deep-learning-based health model to the user situation, accuracy may be degraded if learning is omitted. In this paper, we propose hybrid multimodal deep learning using a collaborative concat layer in health big data. The proposed method uses a machine learning technique to alleviate the issue caused by the change in the data observation range according to a change in the user situation, and occurring in multimodal health deep learning. It is a layer composed of the connection, input, and output of the model of the collaborative node (CN). A CN is a node that predicts absent variables through filtering using the similarity of input values. With CN, a collaborative concat layer (CCL) that handles missing values from the input of the health model can be configured, and the issue related to missing values occurring in the health model can be resolved. With the proposed CCL, it is possible to reuse existing models or construct new models through the concatenation of several single-modal deep learning models. By evaluating the effect on the input and output of the model according to the structural position of the CCL, various networks can be configured, and the performance of the single-modal model can be maintained. In particular, the accuracy of a deep learning model is more stable when the CCL is used, suggesting the experiment progress based on the assumption that a specific variable is absent depending on the user situation.

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Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model

Cited by 71 publications

References 34 publications

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

Traffic State Estimation of Bus Line With Sparse Sampled Data

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

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