Juan José Vinagre Díaz

In the last years we have seen several studies showing the potential of mobile network data to reconstruct activity and mobility patterns of the population. These data sources allow continuous monitoring of the population with a higher degree of spatial and temporal resolution and at a lower cost compared with traditional methods. However, for certain applications, the spatial resolution of these data sources is still not enough since it typically provides a spatial resolution of hundreds of meters in urban areas and of few kilometers in rural areas. In this work, we fill this gap by proposing a methodology that utilises GPS data from the usage of different applications in mobile devices. This approach improves the spatial precision in the location of activities, previously identified with the mobile network data.

E-scooter services have been deployed in cities worldwide, as a clean, flexible, and enjoyable mobility mode that contribute to reaching sustainable objectives. Given its recent advent, we still need to understand its actual operation and efficiency, and the corresponding result from users. This work contributes to this aim, providing a novel metric, the time-based utilization rate of the fleet, to measure the performance of e-scooter services. Its time-based nature implies a novel approach in the field, separating from traditional approaches that use the number of trips as the basic variable. We will mathematically demonstrate that these metrics are approximations of the exact value we provide as they intrinsically depend on the time interval they select for the calculation. In order to feed our method, we have retrieved e-scooter data from an operator (Helbiz) in Atlanta (USA) and Rome (Italy), corresponding to February 2021. These data include the state of each e-scooter in time, which allows us to precisely know whether they were used, out of service, or parked. Results show utilization rates of the fleet of $0.2021\,\%$ and $0.3310\,\%$ , respectively and high percentages of out of service vehicles that exceed $25\,\%$ . However, this inefficiency was not reflected in the profitability of the business. The economic analysis we conducted shows revenues close to $1$ million euros in Rome, which may detach profitability from efficiency.

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.

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.

Car-sharing systems have irrupted in our cities following the shared mobility paradigm. They have evolved the personal mobility market from product-based into service-oriented, which ultimately provides a positive impact on the city’s sustainability. Car sharing systems are a complex interactive service, whose dynamics can dramatically affect its operational viability. In order to better asses this viability, we must rely on data to produce novel metrics that characterize both the user behavior and the service performance. Up to date, research has focused on modeling the demand on the basis of the number of rentals that start within a specific time slot. However, this approach seems unable to provide a representative metric of the performance of a car-sharing system. In this paper, we propose a novel metric, the utilization rate of the fleet, which considers the precise number of vehicles within a fleet that are in service every minute of the day. From this basic metric, we derive a key performance indicator (KPI) to reflect the viability of any car-sharing system in economic and sustainability terms. We have applied this new metric and KPI to a dataset with 449 days of car2go data, collected in 10 European cities.

In recent years, moped-style scooter sharing is gaining increasing attention in many urban areas worldwide. Nevertheless, research contributions are still limited, unlike other shared mobility systems. This paper is aimed at providing a first insight on moped sharing demand by exploring the usage and opinions towards this new mobility alternative. To that end, the research exploits the data from a web-based survey conducted in Spain, one of the countries with the largest implementation around the world in terms of the shared e-mopeds fleet. Kruskal–Wallis tests were conducted to identify the segment of the urban population that is more likely adopted moped sharing, and additional statistical mean differences in specific variables concerning moped sharing were carried out. The paper also provides a better understanding of the shared mopeds market and some implications for urban mobility, such as the potential role of shared mopeds in reducing vehicle ownership and its effect on urban modal shift. Furthermore, two discrete choice models were developed to (i) analyze the key drivers determining the willingness to use moped sharing, and (ii) explore individuals’ opinions on whether owning a private vehicle will not be a need in the future. The results indicate that age, occupation, income, and environmental awareness seem to be among the main reasons behind the potential use of these services in the future. The results may be useful for both operators and transport planners when designing actions and policy efforts addressing this mobility option and urban mobility in general.