RESUMEN
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.
RESUMEN
Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city's mobility.
RESUMEN
The explosion of the Internet of Things has dramatically increased the data load on networks that cannot indefinitely increment their capacity to support these new services. Edge computing is a viable approach to fuse and process data on sensor platforms so that information can be created locally. However, the integration of complex heterogeneous sensors producing a great amount of diverse data opens new challenges to be faced. Rather than generating usable data straight away, complex sensors demand prior calculations to supply meaningful information. In addition, the integration of complex sensors in real applications requires a coordinated development from hardware and software teams that need a common framework to reduce development times. In this work, we present an edge and fog computing platform capable of providing seamless integration of complex sensors, with the implementation of an efficient data fusion strategy. It uses a symbiotic hardware/software design approach based on a novel messaging system running on a modular hardware platform. We have applied this platform to integrate Bluetooth vehicle identifiers and radar counters in a specific mobility use case, which exhibits an effective end-to-end integration using the proposed solution.
