An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization

Guo, Zhengang; Zhang, Yingfeng; Lv, Jingxiang; Liu, Yang; Liu, Ying

doi:10.1109/tits.2020.2986158

Cited by 16 publications

(5 citation statements)

References 61 publications

(85 reference statements)

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“…However, if the update time of the model becomes infinitely long, it cannot meet the requirements of real-time processing of data streams. Based on the principle of real-time data-stream processing, the prediction accuracy should be relatively high when the model's update time is relatively short [33,34]. Therefore, the above results need to be further discussed.…”

Section: Discussionmentioning

confidence: 99%

A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

et al. 2022

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In the traditional coal-mine gas-concentration prediction process, problems such as low timeliness of data and low efficiency of the prediction model in learning data features result in low accuracy of the final prediction. To solve these problems, a gas-concentration prediction method driven by the Spark Streaming framework is proposed. In this research study, the Spark Streaming framework, autoregressive integrated moving average (ARIMA) model and support vector machine (SVM) model are used to construct a new prediction model called the SPARS model. The Spark Streaming framework is used to process large batches of real-time streaming data in a short period of time, and the model can be used to intermittently update and optimize the prediction model so that the model can fully learn the characteristics of the data. At the same time, the advantages of the ARIMA model and SVM model for processing linear data and nonlinear data are combined to improve the model’s prediction efficiency and fully reflect the timeliness of gas prediction. Finally, the proposed prediction model is verified using gas data collected on site. The optimal learning time for the SPARS model in predicting this set of data is determined, and a comparative analysis of the prediction results obtained from the ARIMA, SVM and other models fully confirms that high-precision prediction results can be obtained using the SPARS model. The proposed model can be used to realize scientific and accurate real-time prediction and analyses of coal-mine gas concentrations and provides a new idea for realizing real-time and accurate gas prediction in coal mines.

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Section: Discussionmentioning

confidence: 99%

A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

et al. 2022

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“…Traffic flow prediction is one of the main research contents of ITS. Traditional methods rely on a knowledge-driven approach, involving an analysis of the physical characteristics of transportation systems and the construction of models through traffic simulation and prior knowledge [2]. Representative approaches include queuing theory models, cellular transport models and microscopic fundamental graph models.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic flow prediction based on temporal multigraph convolutional neural network

Guan,

ZHENG,

WANG

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Traffic flow prediction plays an important role in intelligent transportation systems(ITS), but is challenged by the spatiotemporal complexity of traffic flow connections. In order to integrate the traffic temporal correlation, spatial correlation and semantic correlation in road network models, we propose a deep learning framework, Temporal Multi-Graph Convolutional Neural Network(T-MGCN) for traffic flow prediction. Firstly, we identify several semantic correlations and encode the non-Euclidean spatial correlations and heterogeneous semantic correlations between roads into multiple graphs. These correlations were modeled by a multi-graph convolutional neural network. Next, a recurrent neural network model is used to learn the dynamic characteristics of the traffic flow to obtain temporal correlations; Finally, a fully connected neural network is used to fuse spatio-temporal correlations and semantic correlations.

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“…Cloud computing enables ubiquitous network access to storage and computing resources, supporting information technology services and applications in an on-demand manner (Saikrishna et al, 2017). Especially in the case of online services and applications, cloud computing can provide fast, cost-effective resource allocation solutions (Díaz et al, 2017;Guo, et al, 2021a). As the recent exponential growth of IoT services becomes a burden to the traditional cloud platform, edge computing is an emerging solution moving computing resources close to data sources to satisfy real-time demands (Pham et al, 2022).…”

Section: Related Work and Motivationmentioning

confidence: 99%

Exploring self-organization and self-adaption for smart manufacturing complex networks

et al. 2022

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Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

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An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization

Cited by 16 publications

References 61 publications

A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

Traffic flow prediction based on temporal multigraph convolutional neural network

Exploring self-organization and self-adaption for smart manufacturing complex networks

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