Sequence to sequence analysis with long short term memory for tourist arrivals prediction

Rizal, Ahmad Ashril; Soraya, Siti; Tajuddin, Muhammad

doi:10.1088/1742-6596/1211/1/012024

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…In another study, the authors used several RNN-LSTM architectures to forecast the taxi traffic caused by the number of tourists visiting Beijing Capital International Airport [43]. The findings of the study used three models to build an LSTM-RNN prediction model for tourist visits.…”

Section: Related Work On Traffic Flow Predictionsmentioning

confidence: 99%

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

Tsalikidis,

Mystakidis,

Koukaras

et al. 2024

Smart Cities

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The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.

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Section: Related Work On Traffic Flow Predictionsmentioning

confidence: 99%

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

Tsalikidis,

Mystakidis,

Koukaras

et al. 2024

Smart Cities

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show abstract

“…Finally, the updated hidden state is derived as the Hadamard product between the output vector and the tanh activation of the current cell state, as in Equation ( 14). The input data are processed by the LSTM cell through timestep to timestep, which finally returns the whole sequence output data [41]. During the learning and validating operations, the weights and bias values are updated to minimize the loss-objective function across training data.…”

Section: = + ( )mentioning

confidence: 99%

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Nguyen-Da

Peng

et al. 2023

Sustainability

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The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is novel in this research. The Pearson matrix, which evaluates the correlation between selected features and target variables, is computed to select the most appropriate input parameters. The architecture of the hybrid CNN–LSTM is optimized by utilizing hyperparameter fine-tuning, which improves the prediction accuracy and efficiency of the proposed algorithm. Moreover, the proposed CNN–LSTM outperformed other traditional approaches, including the backpropagation neural network (BPNN), CNN, recurrent neural network (RNN), gated recurrent unit (GRU), and LSTM algorithms, by deploying the K-fold cross-validation methodology. The developed algorithm could be utilized as the baseline strategy for resource planning, which could efficiently maximize and deeply utilize the available resource in Vietnam.

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“…The DL model used in this study is the LSTM which is a development of the Recurrent Neural Network (RNN). to reduce errors during training using backpropagation [31] . LSTM can overcome long-term dependence on its inputs.…”

Section: Lstm Architecture Modelingmentioning

confidence: 99%

Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

Mukhtar,

Remli,

Wan Salihin Wong

et al. 2023

TEM Journal

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The DL (Deep Learning) method is the standard for forecasting tourist arrivals. This method provides very good forecasting results but needs improvement if the data is small. Statistical data from the BPS (Central Bureau of Statistics) needs to be corrected, resulting in forecasts that tend to be invalid. This study uses statistical data and GT (Google Trends) as a solution so that the data is sufficient. GT data has a lot of noise because there is a shift between web searches and departures. This difference will produce noise that needs to be cleaned. We use monthly data from January 2008 to December 2021 from BPS sources combined with GT. Hilbert-Huang Transform (HHT) is proposed to clean data from various disturbances. The DL used in this study is long short-time memory (LSTM) and was evaluated using the root mean squared error RMSE and mean absolute percentage error (MAPE). The evaluation results show that the HHT-LSTM results are better than without data cleaning.

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Sequence to sequence analysis with long short term memory for tourist arrivals prediction

Cited by 10 publications

References 3 publications

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

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