Abstract
Bike usage in Smart Cities is paramount for sustainable urban development: cycling promotes healthier lifestyles, lowers energy consumption, lowers carbon emissions, and reduces urban traffic. However, the expansion and increased use of bike infrastructure has been accompanied by a glut of bike accidents, a trend jeopardizing the urban bike movement. This paper leverages data from a diverse spectrum of sources to characterise geolocated bike accident severity and, ultimately, study cycling risk and discomfort. Kernel density estimation generates a continuous, empirical, spatial risk estimate which is mapped in a case study of Zürich city. The roles of weather, time, accident type, and severity are illustrated. A predominance of self-caused accidents motivates an open-source software artifact for personalized route recommendations. This software is used to collect open baseline route data that are compared with alternative routes minimizing risk and discomfort. These contributions have the potential to provide invaluable infrastructure improvement insights to urban planners, and may also improve the awareness of risk in the urban environment among experienced and novice cyclists alike.
Highlights
Bikes are transforming urban environments in Smart Cities [2,12] and advancing challenging sustainable development goals: cycling supports healthier lifestyles, decreases energy consumption and carbon emissions in urban centers, and reduces traffic jams
A data-driven approach for the estimation and mapping of cycling risk in complex evolving urban environments can provide invaluable empirical insights about safety. This is shown for the city center of Zürich, in which continuous risk contours are calculated based on historical geolocated accident data and information about their severity, linked to health insurance compensation policies
Accidents increase at a higher rate than bike use, while weather, seasonality, day of the week, and time play a role on the likelihood of an accident and its severity
Summary
Bikes are transforming urban environments in Smart Cities [2,12] and advancing challenging sustainable development goals: cycling supports healthier lifestyles, decreases energy consumption and carbon emissions in urban centers, and reduces traffic jams. This paper introduces a data-driven method to estimate cyclist risk and discomfort and applies it to the city of Zürich. Between risk and exposure as well as other methodologies to model and measure cycling safety are reviewed extensively in a recent report by the U.S Department of Transportation [17], though this report does not provide any quantitative data analysis as performed in this paper. Spatiotemporal analysis frameworks of bike risk have been introduced using network-based kernel density estimation [3,9] Applying this method in the city center of Vienna, Austria, reveals that bike accident hot spots vary in space according to season, light, and precipitation conditions. The contributions of this paper are as follows: (i) A continuous spatial risk and route discomfort estimation model for mapping cyclists’ urban safety.
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