TR2019-144

Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data


    •  Wollenstein, S., Sun, C., Zhang, J., Paschalidis, I., "Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data", IEEE Conference on Decision and Control (CDC), December 2019.
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      • @inproceedings{Wollenstein2019dec,
      • author = {Wollenstein, SalomoĢn and Sun, Chuangchuang and Zhang, Jing and Paschalidis, Ioannis},
      • title = {Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-144}
      • }
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  • Research Areas:

    Machine Learning, Optimization


Existing work has tackled the problem of estimating Origin-Destination (OD) demands and recovering travel latency functions in transportation networks under the Wardropian assumption. The ultimate objective is to derive an accurate predictive model of the network to enable optimization and control. However, these two problems are typically treated separately and estimation is based on parametric models. In this paper, we propose a method to jointly recover nonparametric travel latency cost functions and estimate OD demands using traffic flow data. We formulate the problem as a bilevel optimization problem and develop an iterative first-order optimization algorithm to solve it. A numerical example using the Braess Network is presented to demonstrate the effectiveness of our method.