[2603.27119] Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction

Computer Science > Machine Learning arXiv:2603.27119 (cs) [Submitted on 28 Mar 2026] Title:Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction Authors:Alireza Nezhadettehad, Arkady Zaslavsky, Abdur Rakib, Seng W. Loke View a PDF of the paper titled Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction, by Alireza Nezhadettehad and 3 other authors View PDF Abstract:Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not only accurate but also uncertainty-aware. In this work, we propose a loosely coupled neuro-symbolic framework that integrates Bayesian Neural Networks (BNNs) with symbolic reasoning to enhance robustness in uncertain environments. BNNs quantify predictive uncertainty, while symbolic knowledge extracted via decision trees and encoded using probabilistic logic programming is leveraged in two hybrid strategies: (1) using symbolic reasoning as a fallback when BNN confidence is low, and (2) refining output classes based on symbolic constraints before reapplying the BNN. We evaluate both strategies on real-world parking data under full, sparse, and noisy conditions. Results demonstrate that both hybrid methods outperform symbolic reasoning alone, and the context-refinement strategy consistently exceeds the performance of Long Short-Term Memory (LSTM) ...