On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models
On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models
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DOI：
10.1109/tits.2015.2428705
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发表时间：
2015-10-01
影响因子：
8.5
通讯作者：
Ioannou, Petros A.
中科院分区：
文献类型：
--
作者：
Rajabioun, Tooraj;Ioannou, Petros A.
由于汽车与基础设施的联系越来越紧密，停车引导和信息系统（PGI）正成为智能交通系统的重要组成部分。开发高效PGI系统的一个主要挑战是停车设施（街道上和街道外）停车可用性的不确定性。一个可靠的PGI系统应该能够以可靠的精度预测到达时间的停车可用性。本文研究了某大城市停车可用性数据的性质，提出了一个考虑停车可用性时空相关性的多元自回归模型。该模型用于车位可用性预测，具有较高的准确性。预测误差用于推荐在估计到达时间至少有一个停车位的最高概率的停车位置。研究结果通过旧金山和洛杉矶地区的实时停车数据进行了验证。
Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.