RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern

Kong, Xiangjie; Qiao, Chunming; Hou, Mengshu; Rahim, Azizur; Ma, Kai; Xia, Feng

doi:10.1109/tvt.2022.3176243

Cited by 31 publications

(15 citation statements)

References 38 publications

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“…The evaluation metrics of the experiment were evaluated using the Symmetric Mean Absolute Percentage Error, Normalized Root Mean Square Error and R-squared ( Estrada et al, 2020 ; Kong et al., 2022a ; Kong et al., 2022b ; Memic et al, 2021 ). The abbreviations were SMAPE, NRMSE and R2 ( Chicco, Warrens & Jurman, 2021 ; Zhu et al, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

Supply forecasting and profiling of urban supermarket chains based on tensor quantization exponential regression for social governance

Zhou

Lin

et al. 2022

PeerJ Computer Science

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Background During the COVID-19 pandemic, the accurate forecasting and profiling of the supply of fresh commodities in urban supermarket chains may help the city government make better economic decisions, support activities of daily living, and optimize transportation to support social governance. In urban supermarket chains, the large variety of fresh commodities and the short shelf life of fresh commodities lead to the poor performance of the traditional fresh commodity supply forecasting algorithm. Methods Unlike the classic method of forecasting a single type of fresh commodity, we proposed a third-order exponential regression algorithm incorporating the block Hankle tensor. First, a multi-way delay embedding transform was used to fuse multiple fresh commodities sales to a Hankle tensor, for aggregating the correlation and mutual information of the whole category of fresh commodities. Second, high-order orthogonal iterations were performed for tensor decomposition, which effectively extracted the high-dimensional features of multiple related fresh commodities sales time series. Finally, a tensor quantization third-order exponential regression algorithm was employed to simultaneously predict the sales of multiple correlated fresh produce items. Results The experiment result showed that the provided tensor quantization exponential regression method reduced the normalized root mean square error by 24% and the symmetric mean absolute percentage error by 22%, compared with the state-of-the-art approaches.

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Section: Resultsmentioning

confidence: 99%

Supply forecasting and profiling of urban supermarket chains based on tensor quantization exponential regression for social governance

Zhou

Lin

et al. 2022

PeerJ Computer Science

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“…Inspired by the widely used data structure of graphs, the spatio-temporal graph data structure proposed in this paper can more accurately represent the spatio-temporal properties of cities [29]. As long as the data can be structured using graphs, spatiotemporal graphs can generally be adapted [30]. We formulate the spatio-temporal graph as G seq = {G 0 , G 1 , G 2 , .…”

Section: Spatio-temporal Graphmentioning

confidence: 99%

Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation

Song,

Yi,

Gao

et al. 2023

Applied Sciences

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Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity.

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“…2. Edge computing can provide rich real-time training data and diverse operating environments for AI models [73]- [75]. Meanwhile, AI can provide edge computing with powerful learning capabilities.…”

Section: Deployment Issuesmentioning

confidence: 99%