Shared mobility services, such as on-demand ride-hailing, car sharing, and bike sharing, have significantly expanded the mobility tools available to urban residents. Understanding how these technologies are adopted over time is critical towards developing appropriate transportation policy measures. Most fundamentally: is adoption over time driven by demographic or geographic diffusion or a combination of the two? This study uses 2016 and 2018 survey data to explore the demographic and spatial predictors of changes in the adoption of on-demand ride-hailing, car sharing, and bike sharing in the Greater Toronto and Hamilton Area (GTHA) in Ontario, Canada.

This study uses both descriptive statistics and inferential models to identify changes in the adoption process between 2016 and 2018. Trivariate ordered models estimated using diagonally weighted least squares (DWLS) suggest that spatial diffusion plays a role (accounting for 9–44% of changes in adoption between 2016 and 2018), that joint demographic-spatial patterns explain some variation in adoption over time (4–11%), but that demographic controls explain most changes in adoption over time (approximately 39–49%). Notably, adoption is concentrated (albeit somewhat less so over time) among younger cohorts, and it increases among women, among larger households, and among households without children. It is unclear how these changes in adoption will be impacted by the Covid-19 pandemic and future studies should explore the non-linear and complex nature of technology adoption, including the relative roles of service supply and demand changes.

按需乘车、汽车共享和共享单车等共享出行服务极大地扩展了城市居民可用的出行工具。了解随着时间的推移如何采用这些技术对于制定适当的交通政策措施至关重要。最根本的是：随着时间的推移，采用率是由人口或地理扩散或两者的组合驱动的？本研究使用 2016 年和 2018 年的调查数据来探索加拿大安大略省大多伦多和汉密尔顿地区 (GTHA) 按需叫车、汽车共享和自行车共享采用变化的人口统计和空间预测因素。

本研究同时使用描述性统计和推理模型来确定 2016 年至 2018 年采用过程中的变化。使用对角加权最小二乘法 (DWLS) 估计的三变量有序模型表明空间扩散发挥了作用（占2016 年至 2018 年之间的采用率），联合人口空间模式解释了采用率随时间的一些变化（4-11%），但人口控制解释了采用率随时间的大部分变化（约 39-49%）。值得注意的是，收养集中（尽管随着时间的推移有所减少）在较年轻的群体中，并且在女性、较大的家庭和没有孩子的家庭中有所增加。