智慧城市道路停车场占用率的统计建模
Statistical modeling of on-street parking lot occupancy in smart cities
摘要：
许多研究表明，寻找停车位与显着的直接和间接成本有关。因此，减少汽车驾驶员花费在寻找可用停车场上的时间是有吸引力的，特别是在所有道路使用者的空间有限的城市地区。道路停车场占用率的预测可以为驾驶员提供一个明确的停车场可能被发现的指导，这一领域的研究在过去的十年中越来越受到关注，随着越来越多的实时停车场占用率数据的可用性。在本文中，我们追求的停车场占用率的预测，在那里我们利用时间到事件模型和半马尔可夫过程理论的统计方法。后者涉及就业的拉普拉斯变换以及反演这是一个雄心勃勃的数值任务。我们将我们的方法应用于澳大利亚墨尔本市的数据。我们的主要结果是，半马尔可夫模型优于马尔可夫模型的真负率和真正率，而这基本上是通过尊重当前的持续时间，停车场已经逗留在其初始状态。
英文摘要：
Many studies suggest that searching for parking is associated with significant direct and indirect costs. Therefore, it is appealing to reduce the time which car drivers spend on finding an available parking lot, especially in urban areas where the space for all road users is limited. The prediction of on-street parking lot occupancy can provide drivers a guidance where clear parking lots are likely to be found. This field of research has gained more and more attention in the last decade through the increasing availability of real-time parking lot occupancy data. In this paper, we pursue a statistical approach for the prediction of parking lot occupancy, where we make use of time to event models and semi-Markov process theory. The latter involves the employment of Laplace transformations as well as their inversion which is an ambitious numerical task. We apply our methodology to data from the City of Melbourne in Australia. Our main result is that the semi-Markov model outperforms a Markov model in terms of both true negative rate and true positive rate while this is essentially achieved by respecting the current duration which a parking lot already sojourns in its initial state.
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