Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

Drosouli, Ifigenia; Voulodimos, Athanasios; Miaoulis, Georgios; Mastorocostas, Paris A.; Ghazanfarpour, Djamchid

doi:10.3390/e23111457

Cited by 9 publications

(1 citation statement)

References 26 publications

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“…The utilization of these sensors resulted in the successful achievement of a 96.9% recognition rate for traffic modes. Drosouli et al [22] successfully elevated the accuracy of traffic mode detection by applying an optimized LSTM model to multimodal sensor data from smartphones. Wang et al [20] further leveraged data from smartphone sensors to construct a neural network model, significantly improving the accuracy of identifying different traffic states.…”

Section: Introductionmentioning

confidence: 99%

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

Cao,

Wen,

Huang

et al. 2024

Mathematics

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Navigation aids play a crucial role in guiding ship navigation and marking safe water areas. Therefore, ensuring the accurate and efficient recognition of a navigation aid’s state is critical for maritime safety. To address the issue of sparse features in navigation aid data, this paper proposes an approach that involves three distinct processes: the extension of rank entropy space, the fusion of multi-domain features, and the extraction of hidden features (EFE). Based on these processes, this paper introduces a new LSTM model termed EFE-LSTM. Specifically, in the feature extension module, we introduce a rank entropy operator for space extension. This method effectively captures uncertainty in data distribution and the interrelationships among features. The feature fusion module introduces new features in the time domain, frequency domain, and time–frequency domain, capturing the dynamic features of signals across multiple dimensions. Finally, in the feature extraction module, we employ the BiLSTM model to capture the hidden abstract features of navigational signals, enabling the model to more effectively differentiate between various navigation aids states. Extensive experimental results on four real-world navigation aid datasets indicate that the proposed model outperforms other benchmark algorithms, achieving the highest accuracy among all state recognition models at 92.32%.

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Section: Introductionmentioning

confidence: 99%

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

Cao,

Wen,

Huang

et al. 2024

Mathematics

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Is the Current Deep Learning Paradigm Really the Future?

Zhang

Qiu

2022

Lecture Notes in Electrical Engineering

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Origin–destination matrices from smartphone apps for bus networks

Barabino,

Coni,

Di Francesco

et al. 2024

Public Transp

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The knowledge of passenger flows between each origin–destination (OD) pair is a main requirement in public transport for service planning, design, operation, and monitoring, and is represented by OD matrices. Although they can be determined by traditional approaches (e.g., surveys, ride-check counts, and/or smartcard-based methods), the availability of new technologies and the proliferation of portable devices triggers an emerging interest in building OD matrices from the apps of bus operators. This research proposes the first framework for the estimation of OD matrices on transit networks by processing smartphone app call detail records (SACDRs). The framework is experimentally tested on a sample of 30 workdays of an Italian bus operator. The results are represented by easy-to-read control dashboards based on maps, which help quantify and visualise the OD matrices in the metropolitan area of Cagliari (Italy). The experimentation shows that the framework can properly estimate the number of trips for both origin and destination w.r.t. OD matrices built from household surveys: the mean absolute error is on average lower than five movements for 90% of the origins and 85% of the destinations.

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Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

Cited by 9 publications

References 26 publications

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

Is the Current Deep Learning Paradigm Really the Future?

Origin–destination matrices from smartphone apps for bus networks

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