A data-driven bi-level program for knowledge-based signal control system under uncertainty

•A data-driven knowledge system (DDKS) for traffic signal control is presented.•A data-driven bi-level program (DDBP) is proposed.•A knowledge based two-stage approach is proposed to effectively solve DDBP.•Numerical computations using real-data city road network are made with recent state-of-the-ar...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 160; pp. 210 - 227
Main Author: Chiou, Suh-Wen
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 15.11.2018
Elsevier Science Ltd
Subjects:
Bi-level programming problem (BLPP)
Data
Data processing
Data-driven bi-level program (DDBP)
Data-driven knowledge based system (DDKS)
Expert systems
Information systems
Knowledge based systems
Mitigation
Risk
Signaling
Stochastic models
Traffic control
Transportation
Two-stage approach
Uncertainty
Urban traffic signal control
Data-driven bi-level program (DDBP)
Bi-level programming problem (BLPP)
Data-driven knowledge based system (DDKS)
Two-stage approach
Urban traffic signal control
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:•A data-driven knowledge system (DDKS) for traffic signal control is presented.•A data-driven bi-level program (DDBP) is proposed.•A knowledge based two-stage approach is proposed to effectively solve DDBP.•Numerical computations using real-data city road network are made with recent state-of-the-art robust signal controls. A data-driven knowledge based system (DDKS) is considered for urban signal control with hazardous material (hazmat) transportation. A data-driven bi-level program (DDBP) is presented to determine generalized travel cost for hazmat carriers and regular traffic flows. A risk-averse (RA) signal control is developed for DDKS with uncertain risk in the presence of hazmat transportation. Since DDBP is generally non-convex, a stochastic program using two-stage approach is proposed to find local optimal solutions. Numerical computations using a real-data city network are made and good results are obtained. As compared with conventional signal controls such as delay-minimizing (DM) and risk-neutral (RN) signal control, the proposed RA exhibits considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven alternatives in all cases.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.06.031