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.
Abstract (translated)
精准的停车位可用性预测对智能交通系统至关重要，但实际部署常面临数据稀疏、噪声及不可预测变化等挑战。应对这些挑战需要模型不仅具备高精度，还需具备不确定性感知能力。本研究提出一种松耦合神经符号框架，通过整合贝叶斯神经网络（BNNs）与符号推理，增强不确定环境下的鲁棒性。BNNs量化预测不确定性，而通过决策树提取并利用概率逻辑编程编码的符号知识，在两种混合策略中得到应用：（1）当BNN置信度较低时，采用符号推理作为回退机制；（2）在重新应用BNN前，基于符号约束对输出类别进行细化修正。我们在完整、稀疏及含噪的真实停车数据上评估了两种策略。结果表明，两种混合方法均优于单独使用符号推理，且上下文细化策略在所有预测时间窗口内持续超越长短期记忆网络（LSTM）与BNN基线模型。我们的发现凸显了模块化神经符号集成在现实世界易受不确定性影响的预测任务中的潜力。
URL
https://arxiv.org/abs/2603.27119
