Abstract
Whether efficient road traffic control needs accurate modelling is still an open question. Additionally, whether complex models can dynamically adapt to traffic uncertainty is still a design challenge when optimizing traffic plans. What is certain is that the highly nonlinear and unpredictable real-world road traffic situations need timely actions. This study introduces TRAMESINO (TRAffic Memory System INtelligent Optimization). This novel approach to traffic control models only relevant causal action-consequence pairs within traffic data (e.g. green time - car count) in order to store traffic patterns and retrieve plausible decisions. Multiple such patterns are then combined to fully describe the traffic context over a road network and recalled whenever a new, but similar, traffic context is encountered. The system acts as a memory, encoding and manipulating traffic data using high-dimensional vectors using a spiking neural network learning substrate. This allows the system to learn temporal regularities in traffic data and adapt to abrupt changes, while keeping computation efficient and fast. We evaluated the performance of TRAMESINO on real-world data against relevant state-of-the-art approaches in terms of traffic metrics, robustness, and run-time. Our results emphasize TRAMESINO’s advantages in modelling traffic, adapting to disruptions, and timely optimizing traffic plans.
C. Axenie, R. Shi, D. Foroni, A. Wieder, M. A. H. Hassan, P. Sottovia, M. Grossi and S. Bortoli—Authors contributed equally to this research.
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Notes
- 1.
Codebase at: https://github.com/omlstreaming/aaltd2021.
- 2.
The SPRING-MUSTARD real-world dataset used in our experiments is available at: http://doi.org/10.5281/zenodo.5025264.
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Axenie, C. et al. (2021). TRAMESINO: Traffic Memory System for Intelligent Optimization of Road Traffic Control. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2021. Lecture Notes in Computer Science(), vol 13114. Springer, Cham. https://doi.org/10.1007/978-3-030-91445-5_6
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