Up-to-date information on urban air pollution is of great importance for health protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements. We collected the measurements throughout more than a year using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with subweekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.

Up-to-date information on urban air pollution, such as reliable pollution maps, is of great importance for health protection agencies to timely assess the air quality situation and provide advice to the general public. Ultrafine particles (UFPs) are widely spread in urban environments and believed to have severe impact on the human health. However, the lack of spatially resolved data hampers profound evaluation of these effects. In this work, we introduce one of the largest spatially resolved UFP data set available today, with over 25 million measurements to build high-resolution pollution maps for an urban area of 100 km2. The data is collected throughout more than one year using mobile sensor nodes, which are installed on top of public transport vehicles in the city of Zurich, Switzerland. We develop land-use regression models to create pollution maps with a high spatial resolution and study their temporal resolution limit.

Predictive models developed by applying Data Mining techniques are used to improve forecasting accuracy in the airline business. In order to maximize the revenue on a flight, the number of seats available for sale is typically higher than the physical seat capacity (overbooking). To optimize the overbooking rate, an accurate estimation of the number of no-show passengers (passengers who hold a valid booking but do not appear at the gate to board for the flight) is essential. Currently, no-shows on future flights are estimated from the number of no-shows on historical flights averaged on booking class level. In this work, classification trees and logistic regression models are applied to estimate the probability that an individual passenger turns out to be a no-show. Passenger information stored in the reservation system of the airline is either directly used as explanatory variable or used to create attributes that have an impact on the probability of a passenger to be a no-show. The total number of no-shows in each booking class or on the total flight is then obtained by accumulating the individual no-show probabilities over the entity of interest. We show that this forecasting approach is more accurate than the currently used method. In addition, the selected models lead to a deepened insight into passenger behavior.