Data-Driven Robust Taxi Dispatch Approaches
Fei Miao, Shuo Han, Shan Lin, George Pappas

Citation
Fei Miao, Shuo Han, Shan Lin, George Pappas. "Data-Driven Robust Taxi Dispatch Approaches". Talk or presentation, October, 2015; Poster presented at the 2015 TerraSwarm Annual Meeting.

Abstract
Traditional transportation systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and traffic demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding traffic demand and system status can be collected in real-time. This information provides opportunities to perform various types of control and coordination for large scale intelligent transportation systems. In this work, we first present a receding horizon control (RHC) framework to dispatch taxis, which combines highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include reducing taxi idle driving distance and matching spatiotemporal ratio between demand and supply for service quality. Such efficient dispatch control and coordinating strategies face a new challenge: how to deal with future customer demand uncertainties while fulfilling system's performance requirements. To address this problem, we then present a novel robust optimization method for taxis dispatch problems to consider closed convex form of spatiotemporally correlated demand model uncertainties. The robust optimization problem is proved equivalent to a convex optimization form, given uncertain demand sets built according to a taxi operational records dataset, and computational tractability is guaranteed. Extensive trace driven analysis with real taxi operational record data sets show that the RHC framework reduce the average total idle distance and reduce the total supply demand ratio error across the city. Moreover, the robust taxi dispatch solutions are less probable to get large costs compared with non-robust results.

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Citation formats

• HTML

  Fei Miao, Shuo Han, Shan Lin, George Pappas. <a
  href="http://www.terraswarm.org/pubs/660.html"><i>Data-Driven
  Robust Taxi Dispatch Approaches</i></a>, Talk or
  presentation,  October, 2015; Poster presented at the <a
  href="http://terraswarm.org/conferences/15/annual"
  >2015 TerraSwarm Annual Meeting</a>.

• Plain text

  Fei Miao, Shuo Han, Shan Lin, George Pappas.
  "Data-Driven Robust Taxi Dispatch Approaches".
  Talk or presentation,  October, 2015; Poster presented at
  the <a
  href="http://terraswarm.org/conferences/15/annual"
  >2015 TerraSwarm Annual Meeting</a>.

• BibTeX

  @presentation{MiaoHanLinPappas15_DataDrivenRobustTaxiDispatchApproaches,
      author = {Fei Miao and Shuo Han and Shan Lin and George
                Pappas},
      title = {Data-Driven Robust Taxi Dispatch Approaches},
      month = {October},
      year = {2015},
      note = {Poster presented at the <a
                href="http://terraswarm.org/conferences/15/annual"
                >2015 TerraSwarm Annual Meeting</a>.},
      abstract = {Traditional transportation systems in metropolitan
                areas often suffer from inefficiencies due to
                uncoordinated actions as system capacity and
                traffic demand change. With the pervasive
                deployment of networked sensors in modern
                vehicles, large amounts of information regarding
                traffic demand and system status can be collected
                in real-time. This information provides
                opportunities to perform various types of control
                and coordination for large scale intelligent
                transportation systems. In this work, we first
                present a receding horizon control (RHC) framework
                to dispatch taxis, which combines highly
                spatiotemporally correlated demand/supply models
                and real-time GPS location and occupancy
                information. The objectives include reducing taxi
                idle driving distance and matching spatiotemporal
                ratio between demand and supply for service
                quality. Such efficient dispatch control and
                coordinating strategies face a new challenge: how
                to deal with future customer demand uncertainties
                while fulfilling system's performance
                requirements. To address this problem, we then
                present a novel robust optimization method for
                taxis dispatch problems to consider closed convex
                form of spatiotemporally correlated demand model
                uncertainties. The robust optimization problem is
                proved equivalent to a convex optimization form,
                given uncertain demand sets built according to a
                taxi operational records dataset, and
                computational tractability is guaranteed.
                Extensive trace driven analysis with real taxi
                operational record data sets show that the RHC
                framework reduce the average total idle distance
                and reduce the total supply demand ratio error
                across the city. Moreover, the robust taxi
                dispatch solutions are less probable to get large
                costs compared with non-robust results.},
      URL = {http://terraswarm.org/pubs/660.html}
  }
  

Posted by Fei Miao on 11 Oct 2015.