【摘要】 将医院停车管理由被动应对停车需求改为主动提前预测车流量高峰，借助全国推行的门诊就医全预约制，由智慧停车管理系统获取医院预约就诊量信息、一周门急诊量(分析增减趋势),结合历史停车量及变化趋势，预测当日停车趋势，分析历史门急诊量与停车量的数据拟合变化规律，建立预测数据模型，用不同周期、月份的数据进行动态参数修正与验证，找到停车与就诊服务内在耦合规律。在预约门诊页面提示停车高峰时段，推荐周边停车场信息，达到减少停车排队等待时间、提高停车场周转率、提升病患就诊体验的目的。通过预测模型参数的调整可复制推广到各大医院，具有现实意义。更多还原

【Abstract】 The study changes the hospital parking management from passively responding to parking demands to proactively predicting peak traffic volumes in advance, acquires information on the number of hospital appointments and the weekly outpatient and emergency visits(analyzing the trend of increase and decrease) from the intelligent parking management system by leveraging the nationwide implementation of the full appointment system for outpatient medical treatment, predicts the parking trend of the day through combining the historical parking volume and its changing trend, analyzes the data fitting change law of the historical outpatient and emergency visit volume and parking volume, and establishes prediction data model, conducts dynamic parameter correction and verification through the data from different periods and months, and finds the inherent coupling law of parking and medical treatment services. On the outpatient appointment page, peak parking hours are indicated, and information about nearby parking lots is recommended to reduce the waiting time for parking queues, increase the turnover rate of parking lots, and enhance the patient’s medical experience. The adjustment of the parameters of the prediction model can be replicated and extended to major hospitals. It is of practical significance.更多还原