In recent years, there has been a tremendous change in the landscape of mobility offerings with the introduction with the introduction of Uber and Lyft, companies collectively known as Transportation Network Companies (TNCs), otherwise known as ridesourcing companies. Due to the nascency of these companies, there is a small but rapidly growing body of literature on the impacts these TNCs are having on traditional forms of shared ride modes, however, most of the emerging literature centers on the relationships between TNC and only one other shared mode of transportation (e.g. TNCs and Public Transit). This research attempts to contribute to the literature by examining the relationship between TNCs and multiple shared ride modes simultaneously. To this end, a joint modeling framework was used to study New York City ridership data for TNCs, taxi services, bikesharing, and the subway from January 2015 to June 2017. The goal of the research presented is two-fold: explore the dynamic relationships between TNCs and other modes of shared ridership, as well as to build a predictive model for the daily ridership usage for each modal offering and total daily ridership usage. To accomplish these tasks, we first used a compositional time series approach in which the four series are modeled as proportions of total daily demand and then, after a suitable transformation, jointly modeled them via a vector autoregression with exogenous predictors (VARX) to account for trend, a weekly seasonal structure, and exogenous predictors. The second part of our analysis involved modeling the daily total usage of the four modes using a dynamic linear model (DLM) and then using that model to draw inferences about the total ridership in NYC. Results of the models were then combined to produce medium-term forecasts for each modal ridership. Our findings corroborate those of others in investigating correlations over time between usage of TNCs and taxis in servicing consumers. In keeping with our second goal, this analysis demonstrates that our modeling framework may be useful for forecasting several competing types of shared ride modes in New York City.

近年来，随着Uber和Lyft的引入（统称为运输网络公司（TNCs），又称为骑行外包公司），出行产品领域发生了巨大变化。由于这些公司的天真，有关这些跨国公司对传统形式的共享乘车模式产生的影响的文献很少，但发展迅速，但是，大多数新兴文献都集中在跨国公司与彼此之间的关系上。共享运输方式（例如跨国公司和公共交通）。这项研究试图通过同时检查跨国公司与多种共享出行方式之间的关系来为文献做出贡献。为此，我们使用了一个联合建模框架来研究跨国公司在纽约市的出行数据，从2015年1月至2017年6月为出租车服务，共享自行车和地铁服务。研究的目的有两个：探索跨国公司与其他共享乘车方式之间的动态关系，以及建立每日的预测模型每个模式产品的乘客量使用量和每日总乘客量使用量。为了完成这些任务，我们首先使用了组成时间序列该方法将四个系列建模为每日总需求的比例，然后在进行适当的转换后，通过向量自回归与外生预测变量（VARX）共同对它们进行建模，以说明趋势，每周季节结构和外生预测变量。我们的分析的第二部分涉及使用动态线性模型（DLM）对四种模式的每日总使用量进行建模，然后使用该模型得出有关纽约市总乘客量的推论。然后将模型的结果合并，以生成每个模态乘员的中期预测。我们的研究结果证实了其他人在调查跨国公司和出租车为消费者提供服务之间的相关性时所得出的结论。为了实现我们的第二个目标，