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A platform for research: civil engineering, architecture and urbanism
Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach
The long-term prediction of highway traffic parameters is frequently undermined by cumulative errors from various influencing factors and unforeseen events, resulting in diminished predictive accuracy and applicability. In the pursuit of sustainable highway development and eco-friendly transportation strategies, forecasting these traffic flow parameters has emerged as an urgent concern. To mitigate issues associated with cumulative error and unexpected events in long-term forecasts, this study leverages the empirical mode decomposition (EMD) method to deconstruct time series data. This aims to minimize disturbances from data fluctuations, thereby enhancing data quality. We also incorporate the BiLSTM model, ensuring bidirectional learning from extended time series data for a thorough extraction of relevant insights. In a pioneering effort, this research integrates the attention mechanism with the EMD–BiLSTM model. This synergy deeply excavates the spatiotemporal characteristics of traffic volume data, allocating appropriate weights to significant information, which markedly boosts predictive precision and speed. Through comparisons with ARIMA, LSTM, and BiLSTM models, we demonstrate the distinct advantage of our approach in predicting traffic volume and speed. In summary, our study introduces a groundbreaking technique for the meticulous forecasting of highway traffic volume. This serves as a robust decision-making instrument for both sustainable highway development and transportation management, paving the way for more sustainable, efficient, and environmentally conscious highway transit.
Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach
The long-term prediction of highway traffic parameters is frequently undermined by cumulative errors from various influencing factors and unforeseen events, resulting in diminished predictive accuracy and applicability. In the pursuit of sustainable highway development and eco-friendly transportation strategies, forecasting these traffic flow parameters has emerged as an urgent concern. To mitigate issues associated with cumulative error and unexpected events in long-term forecasts, this study leverages the empirical mode decomposition (EMD) method to deconstruct time series data. This aims to minimize disturbances from data fluctuations, thereby enhancing data quality. We also incorporate the BiLSTM model, ensuring bidirectional learning from extended time series data for a thorough extraction of relevant insights. In a pioneering effort, this research integrates the attention mechanism with the EMD–BiLSTM model. This synergy deeply excavates the spatiotemporal characteristics of traffic volume data, allocating appropriate weights to significant information, which markedly boosts predictive precision and speed. Through comparisons with ARIMA, LSTM, and BiLSTM models, we demonstrate the distinct advantage of our approach in predicting traffic volume and speed. In summary, our study introduces a groundbreaking technique for the meticulous forecasting of highway traffic volume. This serves as a robust decision-making instrument for both sustainable highway development and transportation management, paving the way for more sustainable, efficient, and environmentally conscious highway transit.
Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach
Yikang Rui (author) / Yannan Gong (author) / Yan Zhao (author) / Kaijie Luo (author) / Wenqi Lu (author)
2023
Article (Journal)
Electronic Resource
Unknown
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