Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction

TL;DR

This paper introduces a neuro-symbolic framework combining Bayesian Neural Networks with symbolic reasoning to improve parking prediction accuracy and robustness under uncertain, noisy, and sparse data conditions.

Contribution

It presents a novel hybrid approach that leverages BNNs and symbolic knowledge, outperforming traditional models in uncertain environments.

Findings

Hybrid methods outperform symbolic reasoning alone.

Context-refinement strategy exceeds LSTM and BNN baselines.

Framework demonstrates robustness in real-world parking data.

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) networks and BNN baselines across all prediction windows. Our findings highlight the potential of modular neuro-symbolic integration in real-world, uncertainty-prone prediction tasks.