Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scarcely known how prices evolve at any given time. This research analyzes ride-hailing fares before and during the COVID-19 pandemic, focusing on applications of time series forecasting and machine learning models that may be useful for transport policy purposes. The Lyft Application Programming Interface was used to collect data on Lyft ride supply in Atlanta and Boston over 2 years (2019 and 2020). The Facebook Prophet model was used for long-term prediction to analyze the trends and global evolution of Lyft fares, while the Random Forest model was used for short-term prediction of ride-hailing fares. The results indicate that ride-hailing fares are affected during the COVID-19 pandemic, with values in the year 2020 being lower than those predicted by the models. The effects of fare peaks, uncontrollable events, and the impact of COVID-19 cases are also investigated. This study comes up with crucial policy recommendations for the ride-hailing market to better understand, regulate and integrate these services.

Lyft、Uber 和 Cabify 等叫车服务通过智能手机应用程序运行，在世界各地的城市中是一种流行且不断增长的出行选择。这些公司可以使用动态算法实时调整票价，以平衡司机和乘客的需求，但价格在任何给定时间的变化情况仍然鲜为人知。这项研究分析了 COVID-19 大流行之前和期间的网约车票价，重点关注可能对交通政策有用的时间序列预测和机器学习模型的应用。Lyft 应用程序编程接口用于收集 2 年（2019 年和 2020 年）在亚特兰大和波士顿的 Lyft 乘车供应数据。Facebook Prophet 模型用于长期预测，以分析 Lyft 票价的趋势和全球演变，而随机森林模型用于网约车票价的短期预测。结果表明，在 COVID-19 大流行期间，网约车票价受到影响，2020 年的数值低于模型预测的数值。还调查了票价高峰、不可控事件的影响以及 COVID-19 病例的影响。本研究为网约车市场提出了重要的政策建议，以更好地理解、监管和整合这些服务。