This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

本文旨在估算由于气象事件，重大活动和地铁服务中断等事件而引起的短期运输需求波动。对四种不同的模式进行了分析和比较，分别是自行车共享，出租车，地铁和公共汽车。案例研究包括3年在加拿大蒙特利尔收集的有关工作日的交易数据。针对每种模式都开发了通用加性模型（GAM）。因变量是一个地铁站附近每小时出行的次数。自变量是来自各种事件的数据。为每个地铁站附近地区校准了不同的模型，以更好地了解空间差异。同样，将GAM的性能和自回归综合移动平均模型进行比较，以在不同的水平进行预测。结果表明，降雨的存在减少了共享自行车，地铁和公交车的需求，同时增加了出租车的需求。实际上，在连续四个小时的降雨之后，共享单车的需求下降了28.0％，地铁和公交车的需求下降了4.6％，出租车的需求增长了13.9％。风只被发现对共享自行车很重要。发现温度对所有四种模式均显着，但对共享自行车和出租车的影响更大。此外，在地铁服务中断期间，对于所研究的三种替代模式（尤其是出租车）的需求显着增加，这表明在中断1小时后需求增加了182％。此外，活动会影响所有四种模式的需求，但地铁似乎是受影响最大的一种。这种方法可以更好地理解出行行为，并可以考虑根据各种事件对运输服务供给进行更动态的调整以匹配出行需求。例如，通过临时增加地铁频率或更改某些自行车共享站点的位置，可以更好地共同策划活动和运输服务。