A data-driven bi-level program for knowledge-based signal control system under uncertainty - Chiou, Suh-Wen (Knowledge-based systems, 2018)
•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.