中文摘要：

预报可得到的停车的空间(APS ) 的技术是为停指导系统(PGS ) 的不可缺少的部件。根据在 Tyne 之上在纽卡斯尔收集的数据，英格兰， APS 的变化特征被学习。此后，试图建立预报比一个常规一步舞模型，提供更富有的信息的模型的多步 APS 最大的 Lyapunov 代表(最大的 LE ) 方法被介绍进 PGS。由用一样的数据集进行的试验性的测试，它的预言性能与在一步舞和多步的神经网络(WNN ) 方法处理的传统的小浪相比。基于结果，，预报打电话给 WNN-LE 方法的模型的新多步被建议 WNN ，在短期的预报与一个更好学习的能力一起享受更精确的表演，当在后者的 Lyapunov 代表预言方法走时，在早预报步被使用精确在后者预报时期反映混乱特征。为一个小时时间时期预报的 APS 的 MSE 能从 83.1 ～ 27.1 被减少(在有 492 个铺位的一座停车的大楼里) 由使用最大的 LE 方法而不是 WNN 并且进一步归结为 19.0 由进行了新方法。

英文摘要：

The techniques to forecast available parking space（APS） are indispensable components for parking guidance systems（PGS）. According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents（largest LEs） method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network（WNN） method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1（in a parking building with 492 berths） by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.