中文摘要：

在某停车场泊位变化实测数据分析的基础上，建立了一个停车场有效泊位占有率短时预测模型．南京某停车场实测泊位数据分析表明，在不同的观测尺度上，停车场有效泊位占有率具有很强的周期性和相似性，但观测尺度越小，随机性越强．基于有效泊位占有率的这种短时变化特性，提出采用小波分析和加权马尔可夫组合模型对有效泊位占有率进行短时预测．首先，通过选择合适的小波函数对有效泊位占有率时间序列进行多分辨率的小波分解，并对低频信号与高频干扰信号分别进行重构，然后对重构后的基本信号和不同分辨率的干扰信号分别建立加权马尔可夫预测模型，最后对各自外推的预测结果进行合成，得到最终预测结果．实例分析表明，所提出的预测模型对有效泊位占有率的短时预测结果是有效的，但模型的预测精度依赖于有效泊位占有率数据库的实时更新．

英文摘要：

Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.