FASTER: Fusion AnalyticS for public Transport Event Response
This addresses operational challenges in urban public transport systems for commuters and operators, but it appears incremental as it integrates existing analytical methods.
The authors tackled the problem of improving public transport commuter experience by developing the FASTER platform, which provides fine-grained situational awareness and real-time decision support, deployed at a national scale handling 1.5 billion trips annually.
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal responses. The FASTER platform that we describe in this work, deployed at nation scale and handling 1.5 billion public transport trips a year, offers such a full stack of techniques for this large-scale, real-time problem. FASTER provides fine-grained situational awareness and real-time decision support with the objective of improving the public transport commuter experience. The methods employed range from statistical machine learning to agent-based simulation and mixed-integer optimization. In this work we present an overview of the challenges and methods involved, with details of the commuter movement prediction module, as well as a discussion of open problems.