With the various smart city initiatives being developed around the globe, a common challenge is to enable strategic decisions about planning and managing the flow of traffic in highly populated, often traffic-laden mobility networks involving roads, cycle paths, and public transportation on rails for example. The increased ability to track traffic opens new opportunities to plan for better efficiency of the mobility networks but also to react better in the case of an unanticipated accident. Analytical methods are needed that allow finding mobility bottlenecks and traffic hotspots.
In this use case we exploit a dataset comprising the journeys of almost two hundred million taxi journeys, which happened in New York City over the course of several years. We examine how Transcendental Information Cascades can be used to expose the flow of traffic within an urban environment. We will focus on the analysis of historic data but we will also feed in synthetic data to demonstrate how our method can be used to simulate problematic situations in order to increase resilience.