ADVANCEMENTS IN LSTM-BASED TRAFFIC OPTIMIZATION: A REVIEW OF METHODOLOGIES, APPLICATIONS, AND FUTURE DIRECTIONS - Наукові конференції
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ADVANCEMENTS IN LSTM-BASED TRAFFIC OPTIMIZATION: A REVIEW OF METHODOLOGIES, APPLICATIONS, AND FUTURE DIRECTIONS

14.03.2025 13:33

[1. Інформаційні системи і технології]

Автор: Andrii Smetiukh, postgraduate, Lviv Polytechnic National University, AI Department, Ukraine; Orest Bilas, candidate of technical sciences, Lviv Polytechnic National University, AI Department, Ukraine


ORCID: 0000-0002-2606-928X Orest Bilas

Urban traffic congestion, a critical challenge exacerbated by rapid urbanization, demands innovative solutions for real-time prediction and adaptive management. Long Short-Term Memory (LSTM) networks have revolutionized traffic optimization by outperforming traditional statistical models like ARIMA and SVM, achieving over 95% accuracy in spatiotemporal forecasting. This review highlights cutting-edge methodologies, including hybrid architectures (e.g., GCN-LSTM) and attention mechanisms (e.g., NTAM-LSTM), which reduce prediction errors by 30% and MAE to 0.401. Applications span dynamic route planning, air traffic conflict prediction (<3% error), and adaptive signal control systems that reduce peak-hour delays by 25%. Despite their success, challenges such as scalability, data heterogeneity, and real-time processing persist. Emerging solutions like edge computing, multi-modal data fusion, and reinforcement learning integration are poised to address these limitations. By synthesizing advancements and future directions, this work underscores LSTM’s transformative role in building efficient, sustainable transportation networks.

1. Introduction

Urbanization and population growth have intensified traffic congestion, costing economies billions annually in lost productivity and fuel waste. Traditional traffic prediction models, such as ARIMA and SVM, struggle to capture the non-linear, dynamic nature of traffic patterns due to their inability to model long-term temporal dependencies and spatial heterogeneity. For instance, ARIMA’s linear assumptions fail to account for sudden disruptions like accidents, while SVM’s reliance on kernel functions limits scalability in large networks.

2. Methodologies and Advancements

2.1 Core LSTM Networks: LSTM networks excel in traffic prediction by resolving the vanishing gradient problem through input, output, and forget gates [1], [2]. These gates regulate information flow, enabling the model to retain critical historical data—such as rush-hour patterns—over extended sequences. Recent innovations focus on hybrid architectures:

GCN-LSTM: Combines graph convolutional networks (GCNs) with LSTMs to model road networks as graphs, where nodes represent intersections and edges denote road segments. GCNs extract spatial dependencies (e.g., congestion spillover effects), while LSTMs capture temporal trends. Article [3] demonstrated a 95.2% R² score and 30% error reduction in urban traffic datasets.

CNN-LSTM: Integrates CNNs for grid-based spatial feature extraction (e.g., traffic heatmaps) with LSTMs for time-series analysis, achieving 93% accuracy in highway traffic forecasting.

2.2 Attention Mechanisms and Bidirectional LSTMs: Attention mechanisms dynamically prioritize influential features, such as flow volume or peak-hour anomalies. The NTAM-LSTM model uses a neural temporal attention module to assign adaptive weights to traffic variables, reducing MAE to 0.401 in network traffic prediction [4]. Bidirectional LSTMs (BiLSTMs) further enhance robustness by processing data in forward and reverse sequences, capturing dependencies from past and future contexts [5].

2.3 Transformer-LSTM Fusion: Emerging frameworks integrate transformers—known for their self-attention capabilities—with LSTMs to handle multi-scale temporal patterns. For example, transformer-LSTM models improve prediction horizons for weekly traffic trends while maintaining hourly accuracy.

3. Applications in Traffic Optimization

3.1 Dynamic Route Planning: LSTM-based systems integrated with routing algorithms (e.g., A*) provide real-time congestion updates, enabling drivers to bypass bottlenecks. In Singapore’s Intelligent Transport System, such models reduced average commute times by 18% during peak hours by analyzing historical GPS data and real-time sensor inputs.

3.2 Air Traffic Management: LSTM models predict flight conflicts by analyzing airspace density, velocity, and weather data. The LSTM framework described in  [6] was developed for en-route airspace management, achieving a 2.8% error rate in conflict prediction. This application is critical for high-traffic corridors, such as European airspace, where delays cost €1.4 billion annually.

3.3 Adaptive Traffic Signal Control: LSTM-driven signal systems adjust green-light durations based on predicted traffic volumes. In a pilot study in Los Angeles, adaptive signals reduced intersection delays by 25% and idling emissions by 15%, showcasing environmental benefits.

4. Challenges and Mitigation Strategies

4.1 Scalability and Real-Time Processing: Processing terabytes of data from IoT sensors and cameras in real-time remains a bottleneck. Edge computing decentralizes computation, enabling local data processing at traffic nodes. For example, NVIDIA’s Metropolis platform uses edge-based LSTMs to optimize traffic flow in smart cities.

4.2 Data Quality and Heterogeneity: Noisy or incomplete data from disparate sources (e.g., GPS, loop detectors) hinder model generalization. Techniques like generative adversarial networks (GANs) synthesize realistic traffic data, while federated learning preserves privacy by training models on decentralized datasets.

4.3 External Factor Integration: Unpredictable events (e.g., accidents, protests) require probabilistic modeling. Ensemble methods combining LSTMs with Bayesian networks improve robustness to outliers, as demonstrated in New York City’s traffic incident management system.

5. Future Directions

5.1 Hybrid Models with Reinforcement Learning (RL): RL-LSTM frameworks can optimize traffic signals dynamically. For instance, DeepMind’s collaboration with Istanbul reduced bus delays by 40% using RL-driven LSTM models.

5.2 Multi-Modal Data Fusion: Integrating V2X communication, drone surveillance, and social media feeds enriches contextual awareness. In Barcelona, X (formerly Twitter )data on public events improved congestion prediction accuracy by 12%.

5.3 Ethical and Sustainable AI: Addressing data privacy concerns and energy consumption is critical. Quantum computing and spiking neural networks offer pathways for energy-efficient LSTM training.

6. Conclusion

LSTM networks have redefined traffic optimization through unparalleled spatiotemporal modeling. Hybrid architectures, attention mechanisms, and edge computing address current limitations, while RL and multi-modal integration promise scalable, adaptive solutions. Collaboration between policymakers, engineers, and AI researchers is essential to translate these innovations into real-world impact. As urbanization accelerates, LSTM-based systems will remain pivotal in creating resilient, sustainable transportation ecosystems.

References:

1. Льовкін, В. (2024). Особливості програмування алгоритмів і структур даних при прогнозуванні автомобільного трафіку. Herald of Khmelnytskyi National University. Technical Sciences, 331(1), 252-258. https://doi.org/10.31891/2307-5732-2024-331-38

2. Льовкін, В. М. (2023). Моделі прогнозування автомобільного трафіку на основі LSTM при розробці прикладних програм. Технічна інженерія, (2(92), 152–157. https://doi.org/10.26642/ten-2023-2(92)-152-157

3. Wu, Zhizhu & Huang, Mingxia & Zhao, Aiping & lan, Zhixun. (2021). Traffic prediction based on GCN-LSTM model. Journal of Physics: Conference Series. 1972. 012107. 10.1088/1742-6596/1972/1/012107

4. Zhao Z., Chen W., Wu X., Chen P. C. Y., and Liu J., LSTM network: a deep learning approach for short-term traffic forecast, IET Intelligent Transport Systems. (2017) 11, no. 2, 68–75, https://doi.org/10.1049/iet-its.2016.0208, 2-s2.0-85015163282.

5. Abduljabbar, R.L., Dia, H. & Tsai, PW. Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data. Sci Rep 11, 23899 (2021). https://doi.org/10.1038/s41598-021-03282-z

6. Lyu, W., Zhang, H., Wan, J., & Yang, L. (2021). Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model. Applied Sciences, 11(11), 5141. https://doi.org/10.3390/app11115141



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