Online Framework for Demand-Responsive Stochastic Route Optimization

Inon Peled*, Kelvin Lee, Yu Jiang, Justin Dauwels, Francisco Camara Pereira

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This study develops an online predictive optimization framework for operatinga fleet of autonomous vehicles to enhance mobility in an area, where thereexists a latent spatio-temporal distribution of demand for commuting betweenlocations. The proposed framework integrates demand prediction and supplyoptimization in the network design problem. For demand prediction, ourframework estimates a marginal demand distribution for each Origin-Destinationpair of locations through Quantile Regression, using counts of crowd movementsas a proxy for demand. The framework then combines these marginals into a jointdemand distribution by constructing a Gaussian copula, which captures thestructure of correlation between different Origin-Destination pairs. For supplyoptimization, we devise a demand-responsive service, based on linearprogramming, in which route structure and frequency vary according to thepredicted demand. We evaluate our framework using a dataset of movement counts,aggregated from WiFi records of a university campus in Denmark, and the resultsshow that our framework outperforms conventional methods for routeoptimization, which do not utilize the full predictive distribution.
Original languageUndefined/Unknown
JournalArXiv
Number of pages32
Publication statusAccepted/In press - 2019

Keywords

  • stat.ML
  • cs.LG
  • math.OC
  • stat.AP

Cite this

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title = "Online Framework for Demand-Responsive Stochastic Route Optimization",
abstract = "This study develops an online predictive optimization framework for operatinga fleet of autonomous vehicles to enhance mobility in an area, where thereexists a latent spatio-temporal distribution of demand for commuting betweenlocations. The proposed framework integrates demand prediction and supplyoptimization in the network design problem. For demand prediction, ourframework estimates a marginal demand distribution for each Origin-Destinationpair of locations through Quantile Regression, using counts of crowd movementsas a proxy for demand. The framework then combines these marginals into a jointdemand distribution by constructing a Gaussian copula, which captures thestructure of correlation between different Origin-Destination pairs. For supplyoptimization, we devise a demand-responsive service, based on linearprogramming, in which route structure and frequency vary according to thepredicted demand. We evaluate our framework using a dataset of movement counts,aggregated from WiFi records of a university campus in Denmark, and the resultsshow that our framework outperforms conventional methods for routeoptimization, which do not utilize the full predictive distribution.",
keywords = "stat.ML, cs.LG, math.OC, stat.AP",
author = "Inon Peled and Kelvin Lee and Yu Jiang and Justin Dauwels and Pereira, {Francisco Camara}",
year = "2019",
language = "Udefineret/Ukendt",
journal = "arXiv",
publisher = "Cornell University",

}

Online Framework for Demand-Responsive Stochastic Route Optimization. / Peled, Inon; Lee, Kelvin; Jiang, Yu; Dauwels, Justin; Pereira, Francisco Camara.

In: ArXiv, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Online Framework for Demand-Responsive Stochastic Route Optimization

AU - Peled, Inon

AU - Lee, Kelvin

AU - Jiang, Yu

AU - Dauwels, Justin

AU - Pereira, Francisco Camara

PY - 2019

Y1 - 2019

N2 - This study develops an online predictive optimization framework for operatinga fleet of autonomous vehicles to enhance mobility in an area, where thereexists a latent spatio-temporal distribution of demand for commuting betweenlocations. The proposed framework integrates demand prediction and supplyoptimization in the network design problem. For demand prediction, ourframework estimates a marginal demand distribution for each Origin-Destinationpair of locations through Quantile Regression, using counts of crowd movementsas a proxy for demand. The framework then combines these marginals into a jointdemand distribution by constructing a Gaussian copula, which captures thestructure of correlation between different Origin-Destination pairs. For supplyoptimization, we devise a demand-responsive service, based on linearprogramming, in which route structure and frequency vary according to thepredicted demand. We evaluate our framework using a dataset of movement counts,aggregated from WiFi records of a university campus in Denmark, and the resultsshow that our framework outperforms conventional methods for routeoptimization, which do not utilize the full predictive distribution.

AB - This study develops an online predictive optimization framework for operatinga fleet of autonomous vehicles to enhance mobility in an area, where thereexists a latent spatio-temporal distribution of demand for commuting betweenlocations. The proposed framework integrates demand prediction and supplyoptimization in the network design problem. For demand prediction, ourframework estimates a marginal demand distribution for each Origin-Destinationpair of locations through Quantile Regression, using counts of crowd movementsas a proxy for demand. The framework then combines these marginals into a jointdemand distribution by constructing a Gaussian copula, which captures thestructure of correlation between different Origin-Destination pairs. For supplyoptimization, we devise a demand-responsive service, based on linearprogramming, in which route structure and frequency vary according to thepredicted demand. We evaluate our framework using a dataset of movement counts,aggregated from WiFi records of a university campus in Denmark, and the resultsshow that our framework outperforms conventional methods for routeoptimization, which do not utilize the full predictive distribution.

KW - stat.ML

KW - cs.LG

KW - math.OC

KW - stat.AP

M3 - Tidsskriftartikel

JO - arXiv

JF - arXiv

ER -