Year: 2025
Type: article
Abstract: The rapid growth in the number of motor vehicles has exacerbated traffic congestion. The occurrence of congestion not only poses significant challenges for traffic management authorities but also severely impacts residents’ travel and daily routines. Against this backdrop, predicting traffic flow can provide crucial insights for anticipating changing traffic patterns. Therefore, this paper proposes a novel hybrid deep learning architecture (CNN-LSTM-GRU) for highway traffic flow prediction that integrates spatiotemporal and meteorological dimensions. Our approach constructs a multidimensional feature matrix encompassing temporal sequences, spatial correlations, and weather conditions. Convolutional Neural Networks (CNN) are employed to capture spatial patterns, while Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks jointly model temporal dependencies. Through systematic hyperparameter tuning and step-length optimization, we validate the model using real-world traffic data from a provincial highway network. The experimental evaluation analyzes the following two critical dimensions: (1) holiday vs. non-holiday traffic patterns, and (2) the impact of weather data integration. Comparative analysis reveals that our hybrid model demonstrates superior prediction accuracy over standalone LSTM, GRU, and their CNN-based counterparts (CNN-LSTM, CNN-GRU).
Source: Systems
Authors: Jinsong Zhang, Junyi Sha, Chunyu Zhang, Yijin Zhang
Institutions: Dalian Maritime University, Dalian Minzu University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G5047202736
OpenAlex ID: W4413927555