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.

The increasing popularity of moped scooter-sharing as a direct and eco-friendly transportation option highlights the need to understand travel demand for effective urban planning and transportation management. This study explores the use of machine learning techniques to forecast travel demand for moped scooter-sharing services in Madrid, Spain, based on origin–destination trip data. A comprehensive dataset was utilized, encompassing sociodemographic characteristics, travel attraction centers, transportation network attributes, policy-related variables, and distance impedance. Two supervised machine learning models, linear regression and random forest, were employed to predict travel demand patterns. The results revealed the effectiveness of ensemble learning methods, particularly the random forest model, in accurately predicting travel demand and capturing complex feature relationships. The feature scores emphasize the importance of neighborhood characteristics such as tourist accommodations, public administration centers, regulated parking, and commercial centers, along with the critical role of trip distance. Users’ preference for short-distance trips within the city highlights the appeal of these services for urban mobility. The findings have implications for urban planning and transportation decision-making to better accommodate travel patterns, improve the overall transportation system, and inform policy recommendations to enhance intermodal connectivity and sustainable urban mobility.

App-based ride-hailing mobility services are becoming increasingly popular in cities worldwide. However, key drivers explaining the balance between supply and demand to set final prices remain to a considerable extent unknown. This research intends to understand and predict the behavior of ride-hailing fares by employing statistical and supervised machine learning approaches (such as Linear Regression, Decision Tree, and Random Forest). The data used for model calibration correspond to a ten-month period and were downloaded from the Uber Application Programming Interface for the city of Madrid. The findings reveal that the Random Forest model is the most appropriate for this type of prediction, having the best performance metrics. To further understand the patterns of the prediction errors, the unsupervised technique of cluster analysis (using the k-means clustering method) was applied to explore the variation of the discrepancy between Uber fares predictions and observed values. The analysis identified a small share of observations with high prediction errors (only 1.96%), which are caused by unexpected surges due to imbalances between supply and demand (usually occurring at major events, peak times, weekends, holidays, or when there is a taxi strike). This study helps policymakers understand pricing, demand for services, and pricing schemes in the ride-hailing market.

Toll plazas are commonly described as a potential bottleneck due to the collection process at specific points on the highway. In a highway concession model, tolls are used to cover the majority of infrastructure costs. However, not all existing road concessions have toll plaza–related key performance indicators (KPIs), resulting in less efficient operations for transport managers and road users. To address this issue, this paper presents a discrete-event simulation (DES) model for policy analysis and the model for the planning, management, and evaluation of toll plazas using an extensible simulation package with animation capabilities, built by selecting modules that contain process features. The DES describes situations in which entities (vehicles) arrive, wait in the queue (if necessary), and then are attended to before leaving the system, either directly or indirectly. As a result, the simulation is based on discrete events (arrivals and departures) and statistical process data. This paper examines data from Brazil’s first public–private partnership (PPP) project in the area of road infrastructure and analyzes the toll plaza in Itaúna/MG using a case study methodology. The DES model was used in this case study, and the results show that the toll plaza was in good operating condition during the peak hour analyzed because the vehicular flow was lower than the system capacity. The willingness to pay the toll rate through the concept of open road tolling or a free-flow system, on the other hand, would provide significant benefits to road users because it would significantly reduce the time it takes to cross the toll plaza. The developed DES model assists toll plaza managers in making decisions while keeping in mind the need to comply with the operational efficiency measures agreed upon in the concession contracts.

Ride-hailing apps are getting increasingly common in cities all around the world. However, the major factors that determine how supply and demand interact to determine the ultimate prices are still mostly understood. By using statistical and supervised machine learning techniques (Linear Regression, Decision Tree, and Random Forest), this study aims to comprehend and forecast the behavior of ride-hailing fares. Ten months’ worth of data were taken from the Uber Application Programming Interface for the city of Madrid and used to calibrate the model. The results show that the Random Forest model is the most suitable for this kind of prediction due to its superior performance metrics. The unsupervised methodology of cluster analysis (using the k-means clustering method) was also used to examine the variation of the difference between Uber fare forecasts and observed values to better understand prediction error patterns. The investigation found that a tiny percentage of observations (approximately 1.96%) had substantial prediction errors due to unexpected surges caused by supply and demand imbalances, which typically happen during major events, peak hours, weekends, holidays, or when there is a taxi strike. This study assists in the understanding of pricing, service demand, and ride-hailing market pricing structures by policymakers.