论文标题
通过事件数据预测蒙特利尔地铁智能卡条目日志
Forecasting of the Montreal Subway Smart Card Entry Logs with Event Data
论文作者
论文摘要
运输运营商的主要目标之一是将运输供应计划适应每个特定时期内对现有运输网络的需求。操作员提到的另一个问题是准确估算一次性票的需求或通过以适应乘客需求的票证。在这种情况下，我们提出了通用数据构建，允许使用众所周知的回归模型（基本，统计和机器学习模型），以通过精细的时间分辨率对乘客需求进行长期预测。具体而言，本文调查了预测，直到一年的乘客人数提前一年，通过考虑计划的活动（例如，音乐会，演出等）进入运输网络的每个站点，并进行了四分之一小时的聚合。为了比较预测的模型和质量，我们使用了来自加拿大蒙特利尔市的真正智能卡和事件数据集，涵盖了三年的时间，并进行了两年的培训和一年的测试。
One of the major goals of transport operators is to adapt the transport supply scheduling to the passenger demand for existing transport networks during each specific period. Another problem mentioned by operators is accurately estimating the demand for disposable ticket or pass to adapt ticket availability to passenger demand. In this context, we propose generic data shaping, allowing the use of well-known regression models (basic, statistical and machine learning models) for the long-term forecasting of passenger demand with fine-grained temporal resolution. Specifically, this paper investigates the forecasting until one year ahead of the number of passengers entering each station of a transport network with a quarter-hour aggregation by taking planned events into account (e.g., concerts, shows, and so forth). To compare the models and the quality of the prediction, we use a real smart card and event data set from the city of Montréal, Canada, that span a three-year period with two years for training and one year for testing.
