FASTER: Fusion AnalyticS for public Transport Event Response

TL;DR

This work presents an overview of the challenges and methods involved in the FASTER platform, with details of the commuter movement prediction module, as well as a discussion of open problems.

Abstract

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.

Figures (11)

• Figure 1: FASTER integrated real-time monitoring interface.
• Figure 2: Lambda architecture.
• Figure 3: Response lines; emergency train lines may "loop" around the incident or go through available tracks. Emergency bus lines are focused on re-connecting the network at a regional or connection to connection scale.
• Figure 4: Continuous HMM.
• Figure 5: Adjacency matrix for euclidean distance in spatio-temporal histogram-based feature space.