An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation
A data-driven multi-objective bi-level signal design for urban network with hazmat transportation is considered in this chapter. A bundle-like algorithm for a min-max model is presented to determine generalized travel cost for hazmat carriers under uncertain risk. A data-driven bi-level decision support system (DBSS) is developed for robust signal control under risk uncertainty. Since this problem is generally non-convex, a data-driven bounding strategy is developed to stabilize solutions and reduce relative gap between iterations. Numerical comparisons are made with other data-driven risk-averse models. The trade-offs between maximum risk exposure and travel costs are empirically investigated. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven risk models.
KeywordsData-driven bi-level decision support system Robust signal control Hazardous material shipments Risk model Equilibrium constraints
The author is grateful to editors for their kind comments in earlier version of this manuscript. The work reported has been supported by grant MOST 104-2221-E-259-029-MY3 from Taiwan National Science Council.
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