This paper investigates the effects of the built environment and weather on the demands for transportation network companies (TNC) in Toronto. The research is based on a historical dataset of Uber trips from September 2016 to September 2018 in Toronto. A wide range of built environments, sociodemographic, and weather data are generated at the dissemination area-level and fused with the monthly aggregated Uber dataset. To provide insight into the underlying factors that affect TNC demand, a series of aggregate demand models are estimated using log-transformed constant elasticity demand functions, with consideration of the seasonal lag effect. To capture the weather effect, an autoregressive moving average model is estimated for the downtown core of Toronto. The model results show that the influence of lagged ridership and seasonal lag effect have a positive correlation with TNC demand. The trip generation and attraction models reveal that TNC trips increase where when the commuting trip duration is longer than 60 min. It is found that the number of apartments in a dissemination area is positively correlated with TNC trip generation, while the number of single-detached houses has a negative correlation. The time-series model indicates that temperature and total daily precipitations are positively correlated with TNC demand. Due to the lack of comprehensive data sources on the Uber and Lyft ridership, the policymakers often struggle to make evidence-based policy recommendations to regulate such disruptive technologies. The series of models presented in this study will help us better understand the potential users of transportation network companies (TNC) and the effects of land use, built environment and weather on transportation network company trips.

本文研究了建筑环境和天气对多伦多交通网络公司 (TNC) 需求的影响。该研究基于 2016 年 9 月至 2018 年 9 月在多伦多的优步出行历史数据集。广泛的建筑环境、社会人口统计和天气数据在传播区域级别生成，并与每月汇总的 Uber 数据集融合。为了深入了解影响 TNC 需求的潜在因素，使用对数转换的恒定弹性需求函数估计了一系列总需求模型，并考虑了季节性滞后效应。为了捕捉天气效应，我们估计了多伦多市中心的自回归移动平均模型。模型结果表明，滞后乘客量和季节性滞后效应的影响与跨国公司的需求呈正相关。旅行生成和吸引力模型显示，当通勤旅行持续时间超过 60 分钟时，TNC 旅行会增加。研究发现，传播区域内的公寓数量与TNC出行产生量呈正相关，而独栋房屋数量呈负相关。时间序列模型表明温度和日总降水量与 TNC 需求呈正相关。由于缺乏有关 Uber 和 Lyft 乘客量的全面数据来源，政策制定者往往难以提出基于证据的政策建议来规范此类颠覆性技术。