## 2 Background and Literature Review  
Uber, Lyft, Cabify, and Didi are examples of ride-hailing services that use information and communication technologies (ICT). Ride-hailing companies typically use smartphone apps to provide their services, allowing the users to request a ride and receive information about the pick-up time, vehicle location, and the fare they will pay in advance. The pricing strategy of ride-hailing companies is needed for their long-term success [7]. Dynamic pricing is a strategy by which products or services prices are adjusted in response to real-time supply-demand imbalances using a dynamic algorithm [8]. Up to about ten years ago, dynamic pricing was primarily limited to a few industries, such as airlines and hotels. Now dynamic pricing is used by companies in many other sectors, such as ride-hailing companies. The regular fare for a ride might, for example, increase during rainy conditions, trip delays (congestion), morning and evening peak hours, and leisure days [5], which influence supply or demand. The flexibility of dynamic pricing should increase the global welfare for society if the industry market is perfect, and thus externalities are internalized. However, the significance and distributions of welfare gains are unclear. Many critics suggest that dynamic pricing can decrease welfare gains for riders [9] or drivers [10]. The advent of ride-hailing services has significantly impacted the taxi market. Its real impact is difficult to measure due to the limited data. According to [PERSON] (2017) [11], Uber reduced regular taxi drivers' income by 12% in Taiwan, and up to 18% after three years. [PERSON] and [PERSON] (2021) [12] conclude that traditional taxi trips in New York have decreased after the entry of Uber. [PERSON] et al. (2020) [13] showed that ride-hailing services have had a significant negative impact on the profitability of taxi companies in Madrid and Barcelona. Besides, the taxi supply is limited by the municipalities in many countries such as Spain. Ride-hailing and taxi services operate under different legal and regulatory frameworks in many countries [5; 7]. The ride-hailing platforms retain a percentage of the total fare as commissions once the ride is completed and paid, and the rest is transferred to the driver. The Chinese Department of Transportation started to regulate in August 2021 the commissions charged by the ride-hailing platforms to standardize the business of those companies and reduce their excessive commissions. In addition, some cities in China, such as Beijing, Shanghai, and Hangzhou, have adopted regulatory measures to restrict the number of drivers registered in Didi [5; 7]. In recent years, the competition between Uber, Cabify, and taxis has caused strong opposition from taxi drivers, who have organized protests and strikes in countries such as Spain [5] and Chile [7]. Currently, the Chilean government is discussing the definition of a basic framework to regulate the system in Congress [7]. In New York, the government has taken regulatory measures to limit the number of Uber drivers in 2019 [7]. In Spain, Denmark, Italy, and Sweden, Uber services have already been declared illegal at some point [14]. Despite the increasing interest in ride-hailing topics, there are still some gaps in the literature that have motivated this research. Statistical models are the most common methodological tools used in ride-hailing studies. From the demand perspective, [PERSON] et al. (2019) [15] used time series to predict ride-hailing demand. From the supply perspective, [PERSON] et al. (2021) [5] used an econometric model to explore Uber fares in Madrid, based only on time-varying fare data. In recent years, there has been growing interest in applying machine learning methods in ride-hailing studies. For instance, [PERSON] and [PERSON] (2019) [16] proposed a general real-time framework for predicting surge multipliers. Their approach was based on a log-linear model, and their model was able to predict Uber surge multipliers in Pittsburgh up to two hours in advance.