Data-driven estimation of origin-destination demand and user cost functions for the optimization of transportation networks
Files
Accepted manuscript
Date
2017-01-01
Authors
Zhang, Jing
Pourazarm, Sepideh
Cassandras, Christos G.
Paschalidis, Ioannis Ch.
Version
OA Version
Citation
Jing Zhang, Sepideh Pourazarm, Christos G Cassandras, Ioannis Ch Paschalidis. 2017. "Data-driven Estimation of Origin-Destination Demand and User Cost Functions for the Optimization of Transportation Networks." IFAC PAPERSONLINE, v. 50, issue 1, pp. 9680 - 9685 (6).
Abstract
Description
In earlier work (Zhang et al., 2016) we used actual traffic data from the Eastern Massachusetts transportation network in the form of spatial average speeds and road segment flow capacities in order to estimate Origin-Destination (OD) flow demand matrices for the network. Based on a Traffic Assignment Problem (TAP) formulation (termed “forward problem”), in this paper we use a scheme similar to our earlier work to estimate initial OD demand matrices and then propose a new inverse problem formulation in order to estimate user cost functions. This new formulation allows us to efficiently overcome numerical difficulties that limited our prior work to relatively small subnetworks and, assuming the cost functions are available, to adjust the values of the OD demands accordingly so that the flow observations are as close as possible to the solutions of the forward problem. Finally, using the same actual traffic data from the Eastern Massachusetts transportation network, we quantify the Price of Anarchy (PoA) for a much larger network than that in Zhang et al. (2016).