Where is My Parking Spot?
Where is My Parking Spot?
复制标题
我的停车位在哪里？
DOI：
10.3141/2489-09
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发表时间：
2015
影响因子：
1.7
通讯作者：
R. Rajagopal
中科院分区：
文献类型：
--
作者：
A. Tamrazian;Z. Qian;R. Rajagopal
停车位占用信息对于停车和交通需求管理至关重要。这项研究提出了有效的无监督学习算法来预测停车位占用率。研究了两种类型的预测：（a）离线预测，其中通过使用历史数据以及各种特征（星期几、天气、季节性）来预测第二天的占用率；以及（b）在线预测，其中利用历史数据和实时数据来预测当天未来几个小时的占用率。两种模式均可适用于路外停车和路内停车。使用了两个数据源：游客停车场的停车付费亭和通勤车库新部署的实时逐点停车传感器。研究发现，通过一组适当的特征，离线方法可以成功区分不同的流动模式，拥挤或未充分利用，以及密集或温和的到达和离开率。离线程序的表现明显优于历史平均水平和前一天的平均水平。在线方法通常比离线方法提供更准确的预测，因为它是从实时占用数据中学习的。随着时间的推移，在线预测的平均错误率和最大错误率下降到远低于历史平均错误率和离线预测错误的水平。当收集足够的实时占用数据并识别流型类型时（案例研究中为上午 9:00 左右），预测误差可以急剧下降。
Parking occupancy information is central to the management of parking and traffic demand. This study proposed efficient unsupervised learning algorithms to predict parking occupancy rates. Two types of predictions were studied: (a) an offline prediction, in which next-day occupancy was predicted by using historical data along with various features (day of week, weather, seasonality), and (b) an online prediction, in which occupancy of future hours of the current day was predicted with both historical and real-time data. The two models can be applied to both off-street and on-street parking. Two data sources were used: parking payment kiosks for a visitors' parking garage and newly deployed real-time spot-by-spot parking sensors for a commuter garage. It was found that, with a proper set of features, the offline method could successfully distinguish different flow patterns, congested or underused, with intensive or mild arrival and departure rates. The offline procedure significantly outperformed both the historical and the previous day's average. The online method provided generally more accurate predictions than the offline method because it learned from the real-time occupancy data. As time progressed, the mean and maximum error rates of the online prediction decreased to a level well below both the historical average and the offline prediction error. A sharp decline of the prediction error could be obtained when sufficient real-time occupancy data were collected and the type of flow pattern was identified (around 9:00 a.m. in a case study).