Abstract
Reliable bike-sharing systems can lead to numerous environmental, economic and social benefits and therefore play a central role in the effective development of smart cities. Bike-sharing models deal with spatially distributed stations and interact with an unpredictable environment, the users. Monitoring the trustworthiness of such a collective system is of paramount importance to ensure a good quality of the delivered service, but this task can become computationally demanding due to the complexity of the model under study. Neural Predictive Monitoring (NPM) [5], a neural-network learning-based approach to predictive monitoring (PM) with statistical guarantees, can be employed to preemptively detect violations of a specific requirement – e.g. a station has no more bikes available or a station is full. The computational efficiency of NPM makes PM applicable at runtime even on embedded devices with limited computational power. The goal of this paper is to demonstrate the applicability of NPM on collective adaptive systems such as bike-sharing systems. In particular, we first analyze the performance of NPM over a collective system evolving deterministically. Then, following [7], we tackle a more realistic scenario, where sensors allow only for partial observability and where the system evolves in a stochastic fashion. We evaluate the approach on multiple bike sharing network topologies, obtaining highly accurate predictions and effective error detection rules.
This work has been partially supported by the PRIN project “SEDUCE” n. 2017TWRCNB.
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Notes
- 1.
The experiments were performed on a computer with a CPU Intel x86, 24 cores and a 128 GB RAM and 15 GB of GPU Tesla V100.
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Cairoli, F., Paoletti, N., Bortolussi, L. (2022). Neural Predictive Monitoring for Collective Adaptive Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_3
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