Event-Driven Spiking Neural Networks for Private Vehicle Parking Prediction

TL;DR

This paper introduces Spark, an event-driven spiking neural network for predicting private vehicle parking locations and durations efficiently on edge devices.

Contribution

The paper introduces Spark, a novel spiking neural network addressing event interval variability, quantization errors, and context-based information flow for parking prediction.

Findings

Spark achieves high prediction accuracy while maintaining computational efficiency on real-world datasets.

The proposed Time-Adaptive Leaky Integrate-and-Fire neuron effectively models variable inter-event intervals.

The accumulate-based readout strategy reduces quantization errors in regression tasks.

Abstract

Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Advanced deep neural networks like Transformers demonstrate exceptional performance in mobility prediction; however, their heavy reliance on dense matrix multiplication makes them unsuitable for real-time applications on vehicular edge devices. Spiking neural networks offer a potential solution due to their asynchronous event-driven characteristics and low power consumption. However, existing spiking neural networks face three fundamental challenges: (1) handling heterogeneous inter-event intervals; (2) mitigating quantization errors in regression tasks under limited simulation steps; and (3) efficiently regulating information flow based on external contexts. To address these challenges, we propose an event-driven spiking neural network for private vehicle parking prediction called Spark. First, we design a Time-Adaptive Leaky Integrate-and-Fire neuron with a lookup table-based decay mechanism to efficiently model variable inter-event intervals. Second, an accumulate-based readout strategy is introduced to mitigate quantization errors by integrating discrete spike trains into continuous output values for high-precision regression. Third, a Spiking Contextual Gating module is proposed to selectively regulate spiking information flow across channels based on environmental context. These components are integrated into a unified architecture that maintains high prediction accuracy while remaining computationally efficient. Extensive experiments on real-world datasets demonstrate that Spark achieves an effective balance between prediction accuracy and computational efficiency compared to baselines.

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