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.

Travel time and activity time have been treated as separate notions, albeit with some assumed interdependencies. But, previous studies show that people accept noticeable higher travel time ratios when travelling by public transport compared with travelling by car. The cost of travel time is reduced as travel time is converted as activity time (towards the productive use). Furthermore, the impact of a high quality journey is an important issue in lifestyle and distribution of activities. The efficiency, safety and productivity of the journey can be expected to impact the value of time. And, the proportion of travel time saved, which can be used for other activities might have positive effects on the quality of life. In recent years some significant ICT-related innovations have been introduced to large shares of society, but research on the current relation between characteristics of ICT-use and its impact on mobility is limited. Development of smart cities and urban living labs is strongly affected by the ICT use and mobility patterns, as urban living labs co-create new products from collaboration between public, private and civic partnerships. At the same time, in certain self-constrained environments, activity duration has many effects in travel patterns for out-ofhome activities. Use of big data to model travel behaviour might bring important implications for individual mobility. Similarly, traffic safety and management of transport infrastructure are a main concern for government decisions in developing countries. This paper contains a summary of five keynote sessions of the Symposium Smart Cities and Value of Time, that took place in Santo Domingo, Dominican Republic in January 18 and 19 2018. This symposium aimed to contribute to the debate on planning transport system and urban development according to the needs and activities undertaken by citizens of the modern ‘on move’ society. This summary presents five key points of transport research oriented to smart cities and value of time, which can be mentioned as: user’s perspective (equity), data collection and infrastructure (traffic safety and network design). These topics are substantially important to estimate accurate travel demand models, and therefore design efficient transport measures.