As transit ridership continues to fall in many cities across the globe, key policy debates continue around whether Uber and other ride-hailing services contribute to this trend. This research explores the effects of ride-hailing to Colombian cities on public transportation ridership using Uber's...

The literature on ride-hailing has experienced rapid growth in recent years, with an accent on industrialized cities, mainly in the United States and Europe. Previous research has identified the characteristics and preferences of ride-hailing adopters in a handful of cities. However, given their...

Ride-hailing services from companies such as Uber, DiDi and 99 have significantly changed travel behavior in cities across the globe. Despite the common presence of these services in Brazilian cities, there is still little information about who are the users of these services in the country,...

Ride-hailing services have gained popularity in recent years due to attributes such as reduced travel costs, traffic congestion, and emissions. However, with the impact of COVID-19, the ride-hailing market is estimated to lose its fair share of an uprising as a transportation mode. During normal...

Ride-hailing applications create new challenges for governments providing transit services, but also create new opportunities to raise tax revenue. To shed light on the effect of taxing or subsidizing ride-hailing applications, we extend a pseudo-monocentric city model to include multiple...

Ride-hailing services have the potential to expand access to opportunities, but out-of-pocket costs may limit the benefits of ride-hail for low-income individuals. This paper examines how ride-hailing services can shape spatial and socioeconomic differences in access to opportunities while...

Changes in transport costs can affect mobility in ways that differ across the population, affecting the impacts of transport policies. We randomly assign large price reductions on Uber in Egypt over a 3-month period and collect comprehensive data on participant mobility using Google Timeline. A...

Die Qualitätssicherung bei der Produktion von Solarzellen ist ein entscheidender Faktor, um langfristige Leistungsgarantien auf Solarpanels gewähren zu können. Die vorliegende Arbeit leistet hierzu einen Beitrag zur automatisierten Fehlererkennung auf Wafern, indem Elektrolumineszenz-Bilder...

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...