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.
翻译:许多研究表明,寻找停车位与大量的直接和间接成本有关。因此,它呼吁减少汽车驾驶员在寻找一个可用的停车场上花费的时间,特别是在所有道路使用者空间有限的城市地区。预测街上停车场占用情况可以为司机提供指导,因为有可能找到明确的停车场。通过增加实时停车场占用率数据,这一研究领域在过去十年中越来越受到关注。在本文中,我们采用统计方法预测停车场占用率,我们利用时间进行活动模型和半马尔科夫过程理论。后者涉及使用拉皮特的变换及其转换,这是一个雄心勃勃的数字任务。我们对澳大利亚墨尔本市的数据采用了我们的方法。我们的主要结果是,半马尔科夫模式在真实的负率和真实的积极率两方面都超过了马尔科夫模式。而实现这一结果主要是通过尊重停车场最初状态中已经居住区的当前期限。