停车的一种新方法：建模、验证和预测
A Queuing Approach to Parking: Modeling, Verification, and Prediction
摘要：
我们提出了一个排队模型的停车动态和基于模型的预测方法，提供实时概率预测未来的停车场占用率。该排队模型具有非齐次到达率和时变服务时间分布。该模型的所有统计假设都使用来自29个卡车停车场的数据进行了验证，每个停车场有55到299个停车位。对于每个位置和每个地点，数据指定了卡车在16个月的运营中的到达和离开时间。本文提出的建模框架为许多理论研究和政策设计中采用的排队模型提供了实证支持。我们将讨论如何使用我们的框架来研究不同环境中的停车问题。基于排队模型，我们提出了两种预测方法，微观的方法和宏观的方法，提供了一个实时的概率预测的停车场占用任意的预测水平。这些基于模型的方法将概率预测问题转换为参数估计问题，该参数估计问题可以使用诸如回归或纯机器学习算法的经典估计方法来解决。我们刻画了一个任意的实时预测算法的下界。我们评估这些方法的性能，使用卡车数据比较其实施的结果与其他基于模型和无模型的方法在文献中提出的。
英文摘要：
We present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data from 29 truck parking locations, each with between 55 and 299 parking spots. For each location and each spot the data specifies the arrival and departure times of a truck, for 16 months of operation. The modeling framework presented in this paper provides empirical support for queuing models adopted in many theoretical studies and policy designs. We discuss how our framework can be used to study parking problems in different environments. Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon. These model-based methods convert a probabilistic forecast problem into a parameter estimation problem that can be tackled using classical estimation methods such as regressions or pure machine learning algorithms. We characterize a lower bound for an arbitrary real-time prediction algorithm. We evaluate the performance of these methods using the truck data comparing the outcomes of their implementations with other model-based and model-free methods proposed in the literature.
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