Abstract
Accurate spatio-temporal information about the current situation is crucial for smart city applications such as modern routing algorithms. Often, this information describes the state of stationary resources, e.g. the availability of parking bays, charging stations or the amount of people waiting for a vehicle to pick them up near a given location. To exploit this kind of information, predicting future states of the monitored resources is often mandatory because a resource might change its state within the time until it is needed. To train an accurate predictive model, it is often not possible to obtain a continuous time series on the state of the resource. For example, the information might be collected from traveling agents visiting the resource with an irregular frequency. Thus, it is necessary to develop methods which work on sparse observations for training and prediction. In this paper, we propose time-inhomogeneous discrete Markov models to allow accurate prediction even when the frequency of observation is very rare. Our new model is able to blend recent observations with historic data and also provide useful probabilistic estimates for future states. Since resources availability in a city is typically time-dependent, our Markov model is time-inhomogeneous and cyclic within a predefined time interval. To train our model, we propose a modified Baum-Welch algorithm. Evaluations on real-world datasets of parking bay availability show that our new method indeed yields good results compared to methods being trained on complete data and non-cyclic variants.
Abstract (translated)
准确的空间和时间信息对于诸如现代路线算法等智能城市应用至关重要。通常，这些信息描述了静止资源的状况，例如停车位、充电站的可用性或等待车辆接他们的位置上的人数。为了利用这类信息，预测监测资源未来的状态通常是必要的，因为资源可能会在需要它们之前改变其状态。要训练一个准确的预测模型，通常无法在资源状态上获得连续的时间序列。例如，信息可能来自访问资源的不规则频率的旅行代理。因此，需要开发用于训练和预测的稀疏观察方法。在本文中，我们提出了一种名为时间异构离散马尔可夫模型的方法，即使在观察频率非常低的情况下，仍能实现准确预测。我们的新模型能够将最近观察到的信息和历史数据相结合，并为未来状态提供有用的概率估计。由于城市的资源可用性通常与时间相关，我们马尔可夫模型在指定的时间间隔内是时间异构的。为了训练我们的模型，我们提出了一个修改的Baum-Welch算法。对于停车位可用性的真实世界数据集的评估表明，与完全数据和非循环变体的训练方法相比，我们新方法确实产生了很好的效果。
URL
https://arxiv.org/abs/2404.12240
