Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach

Yikang Rui, Yannan Gong, Yan Zhao (), Kaijie Luo and Wenqi Lu
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Yikang Rui: School of Transportation, Southeast University, Nanjing 211189, China
Yannan Gong: Highway Development Center of Jiangsu Provincial Department of Transportation, Nanjing 210001, China
Yan Zhao: School of Transportation, Southeast University, Nanjing 211189, China
Kaijie Luo: School of Transportation, Southeast University, Nanjing 211189, China
Wenqi Lu: School of Transportation, Southeast University, Nanjing 211189, China

Sustainability, 2023, vol. 16, issue 1, 1-17

Abstract: 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.

Keywords: sustainable highway; empirical mode decomposition; bidirectional long short-term memory network; attention mechanism; traffic flow prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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