A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports

Maritime Economics & Logistics, Год журнала: 2024, Номер 26(1), С. 44 - 73

Опубликована: Фев. 16, 2024

Язык: Английский

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Variational autoencoder based on knowledge sharing and correlation weighting for process-quality concurrent fault detection

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108051 - 108051

Опубликована: Фев. 13, 2024

Язык: Английский

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TCLN: A Transformer-based Conv-LSTM network for multivariate time series forecasting

Applied Intelligence, Год журнала: 2023, Номер 53(23), С. 28401 - 28417

Опубликована: Окт. 2, 2023

Язык: Английский

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Vacant Parking Space Prediction Based on Auto-Correlation Mechanism

Опубликована: Янв. 1, 2025

Язык: Английский

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A review of research on urban parking prediction

Journal of Traffic and Transportation Engineering (English Edition), Год журнала: 2024, Номер 11(4), С. 700 - 720

Опубликована: Июль 30, 2024

The rapid growth of urban traffic has intensified daily congestion, affecting both flow and parking. Accurate parking prediction plays a vital role in effectively managing limited resources is essential for the successful implementation advanced intelligent systems. In an effort to comprehensively assess latest developments prediction, we curated dataset 639 articles spanning from 2010 present, using Scopus database. Initially, performed bibliometric analysis utilizing VOSviewer software. These findings not only illuminate emerging trends within field but also provide strategic guidance its progression. Subsequently, categorized advancements three focal areas: behavior demand space prediction. A comprehensive overview present research status future directions was then provided. underscore substantial progress achieved current models, through diverse avenues like multi-source data integration, multi-variable feature extraction, nonlinear relationship modeling, deep learning techniques application, ensemble model utilization. innovative endeavors have pushed theoretical boundaries significantly heightened precision applicability predictive models practical scenarios. Prospective should explore such as processing unstructured datasets, developing small-scale data, mitigating noise interference harnessing potent platform fusion techniques. This study's significance transcends guiding catalyzing advancement academic domains; it holds paramount relevance across research, technological innovation, decision-making support, business applications, policy formulation.

Язык: Английский

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Traffic Flow Prediction Framework That Can Appropriately Process the Noise, Volatility, and Nonlinearity in Traffic Flow Data

International Journal of Intelligent Systems, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of prediction, we developed a framework—namely, multicomponent network—that appropriately processes noise, volatility, and nonlinearity in data. This framework comprises three components: factor selection component, decomposition component. The component considers dynamic effects weather‐related, environmental, spatiotemporal factors on flow; it then extracts analyzes exhibiting strong correlations with flow. optimizes parameters variational mode basis envelope entropy by using sparrow search algorithm; transforms into multiple intrinsic functions to enable accurate prediction. Finally, constructs feature matrices bidirectional gated recurrent unit model identify relationships within Moreover, uses an attention mechanism assign different weights features importance these thereby enabling efficient processing large volume performance proposed was examined experiments conducted volumes data time granularities. results indicated that achieved high stability various granularities, samples, dataset sizes, noise conditions. generally outperformed existing models under all experimental

Язык: Английский

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Exploring Urban Parking Solutions: A Literature Review of Predictive Occupancy Models

International Journal of Intelligent Transportation Systems Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 28, 2025

Язык: Английский

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Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 129, С. 107670 - 107670

Опубликована: Дек. 12, 2023

Язык: Английский

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Spatial and temporal attention-based and residual-driven long short-term memory networks with implicit features

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 134, С. 108549 - 108549

Опубликована: Май 29, 2024

Язык: Английский

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Enhancing Urban Parking Efficiency Through Machine Learning Model Integration

IEEE Access, Год журнала: 2024, Номер 12, С. 81338 - 81347

Опубликована: Янв. 1, 2024

Язык: Английский

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