LSTM training set analysis and clustering model development for short-term traffic flow prediction

Doğan, Erdem

doi:10.1007/s00521-020-05564-5

Cited by 18 publications

(7 citation statements)

References 33 publications

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“…-Deep learning Algorithm: The Long Short-Term Memory (LSTM) network consists of memory blocks, each containing a cell state and three gates [18][19] including the input gate (controls how the input can change the cell state), the output port (sets which part of the cell state to output), and the forged gate (decides how much memory to keep). Remark 1: Maritime data is collected from many diff erent sources and does not have integrated links.…”

Section: Overview Of Maritime Data Mining Connected To Classifi Catio...mentioning

confidence: 99%

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Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Pham,

Dang,

Koboević

et al. 2024

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High-speed passenger vessels, integrated river and sea vessels, container vessels, oil tankers, and other underwater vehicles operating in maritime traffic are among the types of vessels that must be equipped with AIS and VHF. The safety of navigation is one of the major problems in the maritime sector, particularly in Vietnam. Furthermore, marine traffic in the seaport zone is a common and difficult issue to manage in areas with a high volume of vessel traffic, mostly in places where the infrastructure supporting navigation is inadequately developed to meet the rapidly growing demands of the contemporary world. Therefore, it is necessary to create an integrated maritime management system to improve the efficiency of data exploitation and support maritime safety. To address this challenge, this study suggests a Maritime Traffic State Prediction (MTSP) model to predict traffic conditions in the channels where real-time data collection is insufficient in some specific locations. We recommend a deep learning method using Long Short-Term Memory (LSTM) networks to predict the safe path of the vessel in case of missing data segments. The findings have shown that the proposed approach encourages the mining of historical vessel data for maritime traffic, is ready to be applied, and can easily be implemented in a computer program or a web-based app.

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Section: Overview Of Maritime Data Mining Connected To Classifi Catio...mentioning

confidence: 99%

“…The goal of the classifi cation challenge is to identify a variety model that allows the determination of the class to which the latest information belongs. In this section, we examine some modern analysis and application methods for processing maritime big data [6][7][8][9][10][11][12][13][14][15][16][17][18][19] as follows:…”

Section: Introduction / Uvodmentioning

confidence: 99%

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Pham,

Dang,

Koboević

et al. 2024

Naše more

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“…• Deep Neural Network (DNN) [46]: A Deep neural network realizes complex prediction and calculation through connections and nesting among neurons. • Long short-term memory (LSTM) [47]: LSTM consists of a forgetting mechanism, an input mechanism, and an output mechanism and is mainly used to solve the problems of memory retention and gradient disappearance in long-term sequence training. Through practical parameter design, traffic flow can be effectively predicted.…”

Section: Baseline Algorithmsmentioning

confidence: 99%

GCN-GAN: Integrating Graph Convolutional Network and Generative Adversarial Network for Traffic Flow Prediction

Zheng

et al. 2022

IEEE Access

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As a necessary component in intelligent transportation systems (ITS), traffic flow-based prediction can accurately estimate the traffic flow in a certain period and area in the future. However, despite the success of traditional research and current machine learning methods, traffic flow prediction models have limitations in terms of prediction accuracy and efficiency. In this work, we propose a novel traffic flow prediction model named Graph Convolution and Generative Adversative Neural Network (GCN-GAN), which leverages Graph Convolution Neural Network (GCN) module and Generative Adversative Neural Network (GAN) module to predict urban traffic flow. Firstly, the GCN module extracts historical traffic flow information in the graph structure. Secondly, the GAN module generates reliable traffic flow prediction results by adversative training. Additionally, GCN-GAN can parallelly generate prediction results rather than traditional one by one. Through experiments on the traffic flow dataset at multiple intersections, our GCN-GAN model outperforms the baseline methods by over 30.54% and has apparent advantages in multistep prediction.

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“…LSTM's inherent ability to capture long-range dependencies and temporal patterns makes it particularly well-suited for modeling and forecasting complex traffic dynamics with high accuracy and reliability. By leveraging LSTM's capacity for sequential data processing, researchers can extract valuable insights from vast streams of traffic data, enabling more informed decision-making and resource allocation in urban planning, transportation management, and infrastructure development [10]- [15]. This research employs LSTM technology in the analysis of elevator traffic dynamics.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Shi,

Sun,

et al. 2024

ICA

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Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

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LSTM training set analysis and clustering model development for short-term traffic flow prediction

Cited by 18 publications

References 33 publications

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

GCN-GAN: Integrating Graph Convolutional Network and Generative Adversarial Network for Traffic Flow Prediction

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

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