An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data

Huang, Junsheng; Mao, Baohua; Bai, Yun; Zhang, Tong; Miao, Changjun

doi:10.3390/s20071992

Cited by 22 publications

(11 citation statements)

References 46 publications

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“…A variety of methods have been proposed in literature for GPS data imputation. [19] presented a Gaussian process based approach, [20] presented a time-series approach named missForest, while [21] put forth a fuzzy c-means method that is tested with GPS data from taxis. When limited data in terms of density and granularity is available, simpler methods can be considered such as linear interpolation where two consecutive points are joint by a straight line, or splines which are piecewise functions of polynomials allowing to connect two consecutive points by a smooth curve [22].…”

Section: A Gps Signal Losses and Solutionsmentioning

confidence: 99%

Hybrid On/Off Blockchain Approach for Vehicle Data Management, Processing and Visualization Exemplified by the ADAPT Platform

Validi¹,

Kashansky²,

Khiari³

et al. 2022

Preprint

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Hybrid on/off-blockchain vehicle data management approaches have received a lot of attention in recent years. However, there are various technical challenges remained to deal with. In this paper we relied on real-world data from Austria to investigate the effects of connectivity on the transport of personal protective equipment. We proposed a three-step mechanism to process, simulate, and store/visualize aggregated vehicle datasets together with a formal pipeline process workflow model. To this end, we implemented a hybrid blockchain platform based on the hyperledger fabric and gluster file systems. The obtained results demonstrated efficiency and stability for both hyperledger fabric and gluster file systems and ability of the both on/off-blockchain mechanisms to meet the platform quality of service requirements.

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Section: A Gps Signal Losses and Solutionsmentioning

confidence: 99%

Hybrid On/Off Blockchain Approach for Vehicle Data Management, Processing and Visualization Exemplified by the ADAPT Platform

Validi¹,

Kashansky²,

Khiari³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, researchers have tried to solve this problem using machine learning methods. [17] have used population genetic algorithms for the shortest path calculation to implement a taxi dispatching model and also to recommend the best area for taxi drivers to carry passengers. [18] develop a taxi path optimization model and solve the taxi path optimization model by using an improved genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In spatial graph, we define the regional grids as an undirected graph G s = ðV s , E s , A s Þ, where V s is the centre point of each grid region, E is the distance between the centre points of each grid region, and the centre point of each grid is regarded as the geographic location centre of the grid. [14] Time series methods Taxi-RS [15] ARIMA [16] Machine learning methods Genetic algorithm PGA [17] HGA [18] LSTM ConvLSTM [19] LSTM and GRU [20] Deep learning methods GCN GCN [21,22] ST-ED-RMGC [23] Attention mechanism MRA [24] 4…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

Multigraph Aggregation Spatiotemporal Graph Convolution Network for Ride-Hailing Pick-Up Region Prediction

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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The prediction of pick-up regions for online ride-hailing can reduce the number of vacant vehicles on the streets, which will optimize the transportation efficiency of cities, reduce energy consumption and carbon emissions, and increase the income of online ride-hailing drivers. However, traditional studies have ignored the temporal and spatial dependencies among pick-up regions and the effects of similarity of POI attributes in different regions in modelling, making the features of the model incomplete. To address the above problems, we propose a new multigraph aggregation spatiotemporal graph convolutional network (MAST-GCN) model to predict pick-up regions for online ride-hailing. In this paper, we propose a graph aggregation method to extract the spatiotemporal aspects and preference features of spatial graphs, order graphs, and POI graphs. GCN is used on the aggregated graphs to extract spatial dimensional features from graph-structured data. The historical data are sequentially divided into temporal granularity according to the period, and convolution operations are performed on the time axis to obtain the features in the temporal dimension. The attention mechanism is used to assign different weights to features with strong periodicity and strong correlation, which effectively solves the pick-up region prediction problem. We implemented the MAST-GCN model based on the PyTorch framework, stacked with a two-layer spatiotemporal graph convolution module, where the dimension of the graph convolution is 64. We evaluate the proposed model on two real-world large scale ride-hailing datasets. The results show that our method provides significant improvements over state-of-the-art baselines.

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“…In addition to the abovementioned imputation method based on the KNN technology, the mean imputation (MI) and fuzzy c-means imputation (FCMI) methods have obtained signifcant research progress [31][32][33]. In MI [34], the missing data are estimated by the mean value or mode of the corresponding attribute, and it is used for the data sets with a similar attribute distribution in each category.…”

Section: Related Workmentioning

confidence: 99%

“…However, the estimations of the same attribute in different incomplete patterns are equal. In FCMI [ 33 ], the estimations are calculated by the clustering centers and the distance between the centers and the patterns. However, the performance of this imputation strategy depends on initial conditions.…”

Section: Related Workmentioning

confidence: 99%

A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Incomplete pattern clustering is a challenging task because the unknown attributes of the missing data introduce uncertain information that affects the accuracy of the results. In addition, the clustering method based on the single view ignores the complementary information from multiple views. Therefore, a new belief two-level weighted clustering method based on multiview fusion (BTC-MV) is proposed to deal with incomplete patterns. Initially, the BTC-MV method estimates the missing data by an attribute-level weighted imputation method with k-nearest neighbor (KNN) strategy based on multiple views. The unknown attributes are replaced by the average of the KNN. Then, the clustering method based on multiple views is proposed for a complete data set with estimations; the view weights represent the reliability of the evidence from different source spaces. The membership values from multiple views, which indicate the probability of the pattern belonging to different categories, reduce the risk of misclustering. Finally, a view-level weighted fusion strategy based on the belief function theory is proposed to integrate the membership values from different source spaces, which improves the accuracy of the clustering task. To validate the performance of the BTC-MV method, extensive experiments are conducted to compare with classical methods, such as MI-KM, MI-KMVC, KNNI-FCM, and KNNI-MFCM. Results on six UCI data sets show that the error rate of the BTC-MV method is lower than that of the other methods. Therefore, it can be concluded that the BTC-MV method has superior performance in dealing with incomplete patterns.

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An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data

Cited by 22 publications

References 46 publications

Hybrid On/Off Blockchain Approach for Vehicle Data Management, Processing and Visualization Exemplified by the ADAPT Platform

Hybrid On/Off Blockchain Approach for Vehicle Data Management, Processing and Visualization Exemplified by the ADAPT Platform

Multigraph Aggregation Spatiotemporal Graph Convolution Network for Ride-Hailing Pick-Up Region Prediction

A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion

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