FASTER: Fusion AnalyticS for public Transport Event Response
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| Soggetti: | |
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| _version_ | 1866929508500635648 |
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| author | Blandin, Sebastien Wynter, Laura Poonawala, Hasan Laguna, Sean Dura, Basile |
| author_facet | Blandin, Sebastien Wynter, Laura Poonawala, Hasan Laguna, Sean Dura, Basile |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1906_03040 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | FASTER: Fusion AnalyticS for public Transport Event Response Blandin, Sebastien Wynter, Laura Poonawala, Hasan Laguna, Sean Dura, Basile Computers and Society Machine Learning Systems and Control Signal Processing 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. |
| title | FASTER: Fusion AnalyticS for public Transport Event Response |
| topic | Computers and Society Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/1906.03040 |