Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control. (October 2015)

• Record Type: Journal Article
• Authors: Liu, Hongcheng; Han, Ke; Gayah, Vikash V.; Friesz, Terry L.; Yao, Tao
• Is Part Of: Transportation research. Volume 59(2015)
• Page Start: 260
• Page End: 277
• Publication Date: 2015-10
• Subjects: On-line signal control -- LWR model -- Linear decision rule -- Distributionally robust optimization -- Data-driven modeling; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.05.016
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A two-stage on-line signal control strategy is proposed based on linear decision rules. The signal control method utilizes both historical and real-time traffic information. A distributionally robust optimization ensures the on-line performance of the controls. All computations are performed off line, making on-line operation efficient and reliable. A case study shows significant improvement of the on-line control over fixed signal plans. Abstract: We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic.