Information and communication technologies have opened the way to guide recent developments in the field of parking. In this paper these technologies are applied to model a decision support system that gives insight into 6-months ahead parking occupancy forecasts for 57 off-street parking locations in Amsterdam. An effect analysis was conducted into the influence of weather-, event-, parking tariff-, and public transport attributes on parking occupancy. The most influential factors on the parking occupancy were the scheduling of artistic and sports events, the addition of a public transport line, and the weather variables thunderstorm, average wind speed, temperature, precipitation, and sunshine duration. Parking tariffs did not significantly contribute to model performance, which could have been because of the lack of data and time variability in the parking tariffs of the examined parking locations. The forecasting algorithms compared were the seasonal naive model as a benchmark approach, the Box–Jenkins seasonal autoregressive integrated moving average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and the long short-term memory neural network. The SARIMAX model outperformed the other algorithms for the 6-months ahead forecasts according to the lowest root mean square error (RMSE). By including the event factor, the model improved by 24% based on the RMSE. Weather variables improved the predictive performance by 8%. Future studies could focus on the addition of more event variables, extension into an online model, and the impact of spatial–temporal features on parking occupancy.

信息和通信技术为引导停车领域的最新发展开辟了道路。在本文中，这些技术被应用于对决策支持系统进行建模，该系统可深入了解阿姆斯特丹 57 个街边停车位的未来 6 个月停车占用率预测。对天气、事件、停车费和公共交通属性对停车占用率的影响进行了效果分析。对停车位占用影响最大的因素是艺术和体育赛事的安排、公共交通线路的增加以及天气变量雷暴、平均风速、温度、降水和日照时数。停车费对模型性能没有显着影响，这可能是因为所检查的停车地点的停车费缺乏数据和时间可变性。比较的预测算法是作为基准方法的季节性朴素模型、带和不带外生回归量的 Box-Jenkins 季节性自回归综合移动平均线（分别为 SARIMAX 和 SARIMA）、指数平滑模型和长短期记忆神经网络。根据最低均方根误差 (RMSE)，SARIMAX 模型在未来 6 个月的预测中优于其他算法。通过包含事件因素，该模型在 RMSE 的基础上提高了 24%。天气变量将预测性能提高了 8%。未来的研究可以集中在添加更多事件变量、扩展到在线模型、