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

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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 languageEnglish
JournalArXiv
Number of pages32
Publication statusPublished - 2020

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