Control and estimation of multi-commodity network flow under aggregation

TL;DR

This paper extends Schr"odinger bridge methodology to multi-commodity network flow estimation and control, enabling minimal perturbation from prior distributions based on scarce aggregate data, with applications to traffic flow management.

Contribution

It introduces a novel multi-commodity Schr"odinger bridge framework that accounts for entry and exit of commodities, enhancing traffic flow estimation from limited observations.

Findings

Developed a method to estimate most likely flow rates from aggregate data.

Extended Schr"odinger bridges to multi-commodity networks with entry/exit dynamics.

Validated the approach with a numerical experiment.

Abstract

A paradigm put forth by E. Schr\"odinger in 1931/32, known as Schr\"odinger bridges, represents a formalism to pose and solve control and estimation problems seeking a perturbation from an initial control schedule (in the case of control), or from a prior probability law (in the case of estimation), sufficient to reconcile data in the form of marginal distributions and minimal in the sense of relative entropy to the prior. In the same spirit, we consider traffic-flow and apply a Schr\"odinger-type dictum, to perturb minimally with respect to a suitable relative entropy functional a prior schedule/law so as to reconcile the traffic flow with scarce aggregate distributions on families of indistinguishable individuals. Specifically, we consider the problem to regulate/estimate multi-commodity network flow rates based only on empirical distributions of commodities being transported (e.g., types of vehicles through a network, in motion) at two given times. Thus, building on Schr\"odinger's large deviation rationale, we develop a method to identify {\em the most likely flow rates (traffic flow)}, given prior information and aggregate observations. Our method further extends the Schr\"odinger bridge formalism to the multi-commodity setting, allowing commodities to exit or enter the flow field as well (e.g., vehicles to enter and stop and park) at any time. The behavior of entering or exiting the flow field, by commodities or vehicles, is modeled by a Markov chains with killing and creation states. Our method is illustrated with a numerical experiment.