Unified​ ​Transport​ ​Engine: Learning​ ​to​ ​Predict​ ​Traffic​ ​Congestions

Ramesh​ ​Sarukkai, ​ ​Shaohui​ ​Sun

Oct 31, 2017 (modified: Oct 31, 2017) NIPS 2017 Workshop MLITS Submission readers: everyone
  • Abstract: We present the framework for an unified transport engine that allows for streamlining wide variety of data sources and designed to support different elements required for seamless transport ranging from speed profiles, congestion, estimated time, features required for routing, and so on. In this paper, we specifically dig into the problem of real time traffic congestion estimation using imagery collected from standard mobile phones. We discuss how we can quickly build highly accurate detectors using transfer learning. Examples and edge cases that worked well as well are challenges for our system are presented.
  • Keywords: transport, congestion, CNN

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