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


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
Number of pages32
Publication statusAccepted/In press - 2020

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