基于随机非平稳和长短时记忆网络的泊位混合预测

Hybrid Prediction Model for Parking Occupancy Based on Non-Stationary Stochastic Process and Long Short-Term Memory Network

为了缓解大城市中日益突出的停车困难,现如今中国各大城市级停车诱导系统的研究开发势在必行.在停车诱导系统中,作为帮助用户找到最合适的停车场的重要因素,对未来停车位的预测是一个非常重要的智能技术手段.目前主流预测方法如果没有了实时数据,大部分会出现误差累积现象,从而影响预测准确性.然而,在停车诱导系统平台的建设早期,我们很难做到将城市所有停车场实时的数据流搜集起来.因此,文中以具有周期特性的非平稳停车位历史数据为研究对象,首先根据中心极限定理和大数定理对停车位进行统计分析,然后结合LSTM (Long Short-Term Memory),提出混合预测模型SAL (non-stationary Stochastic And Long short-term memory)来对未来某个时间段的停车位作有效预测.实验数据证明,相比于单独使用LSTM和Lyapunov指数法作长期预测,SAL的计算复杂度更低,预测效果相对更加精确,并且有效解决了在失去实时数据支撑情况下多步长期预测导致的误差累积问题.

It is popular to develop the city-wide Parking Guidance System (PGS) in China nowadays, in order to alleviate the parking difficulties arising in large cities. Prediction on parking occupancy is the essential intelligent technology to help vehicles find the proper parking lot efficiently in PGS. And the known prediction methods have to be powered by real-time data, without which would cause error accumulation and significant inaccuracies. In the early stage of PGS deployment, however, it is very hard to collect the real-time data from the parking lots all over the city. Therefore, this study takes the historical data of non-stationary parking spaces with periodic characteristics as the research object. Firstly, statistical analysis of parking spaces is carried out according to the central limit theorem and Law of Large Numbers. Then, we propose a method named SAL (non-stationary Stochastic And Long short-term memory) combined with LSTM (Long Short-Term Memory), to predict the parking occupancy at the given time, based on digging the history data. Experimental data prove that compared with using LSTM and Lyapunov exponent method, SAL has lower computational complexity, more accurate prediction, and effectively solves the problem of error accumulation caused by multi-step long-term prediction without real-time data.