Probabilistic Modelling of Station Locations in Bicycle-Sharing Systems

Abstract

Abstract. We present a simulation methodology for generating the locations of stations in Bicycle-Sharing Systems. We present several methods that are inspired by the literature on spatial point processes. We evaluate how the artificially generated systems compare to existing systems through a case study involving 11 cities worldwide. The method that is found to perform best is a data-driven approach in which we use a dataset of places of interest in the city to 'rate' how attractive city areas are for station placement. The presented methods use only non-proprietary data readily available via the Internet.

Capturing Dynamicity and Uncertainty in Security and Trust via Situational Patterns

“…Systems of the Industry 4.0 domain can be seen as a special case of Smart Cyber-Physical Systems (sCPS) [11], which also exhibit a high level of uncertainty. This has been already partially studied, e.g., in the scopes of the ASCENS and Quanticol projects [28,35]. Also, we partially addressed statistical modeling of human behavior in sCPS [10] and adaptation via meta-adaptation strategies [21] however a complete approach for sCPS is yet missing.…”