The excessive search for parking, known as cruising, generates pollution and congestion. Cities are looking for approaches that will reduce the negative impact associated with searching for parking. However, adequately measuring the number of vehicles in search of parking is difficult and requires sensing technologies. In this paper, we develop an approach that eliminates the need for sensing technology by using parking meter payment transactions to estimate parking occupancy and the number of cars searching for parking. The estimation scheme is based on Particle Markov Chain Monte Carlo. We validate the performance of the Particle Markov Chain Monte Carlo approach using data simulated from a GI/GI/s queue. We show that the approach generates asymptotically unbiased Bayesian estimates of the parking occupancy and underlying model parameters such as arrival rates, average parking time, and the payment compliance rate. Finally, we estimate parking occupancy and cruising using parking meter data from SFpark, a large scale parking experiment and subsequently, compare the Particle Markov Chain Monte Carlo parking occupancy estimates against the ground truth data from the parking sensors. Our approach is easily replicated and scalable given that it only requires using data that cities already possess, namely historical parking payment transactions.

翻译：过度寻找停车位（即巡航）会产生污染和交通拥堵。城市正在寻求减少与寻找停车位相关的负面影响的方法。然而，准确测量正在寻找停车位的车辆数量是困难的，并且需要传感技术。在本文中，我们开发了一种方法，通过使用停车计时器支付交易来估计停车占用率和寻找停车位的汽车数量，从而消除了对传感技术的需求。该估计方案基于粒子马尔可夫链蒙特卡罗方法。我们使用从GI/GI/s队列模拟的数据验证了粒子马尔可夫链蒙特卡罗方法的性能。我们表明，该方法能生成停车占用率以及到达率、平均停车时间和支付合规率等底层模型参数的渐近无偏贝叶斯估计。最后，我们使用来自SFpark（一个大规模停车实验）的停车计时器数据估计了停车占用率和巡航情况，随后将粒子马尔可夫链蒙特卡罗的停车占用率估计值与来自停车传感器的地面真实数据进行了比较。我们的方法易于复制且可扩展，因为它仅需要使用城市已经拥有的数据，即历史停车支付交易。