This study develops a risk/cost-based dangerous goods routing algorithm. The algorithm focuses on mitigating the risks associated with the transportation of dangerous goods (DG) via route selection. The algorithm was applied to a large-scale transportation network representing the Metro Vancouver area. The network is represented spatially in a GIS database along with a realtime dispersion plume simulating a specific chemical release under local weather conditions. GIS facilitates the comparison between the various criteria by overlaying transportation networks characteristics on other spatially referenced data, such as population demographics or meteorological data. The algorithm and general methodology is used for the routing of dangerous goods on-demand, serving individual shipments in a permitting environment. The uniqueness of the proposed approach is in the “normalization” of risks and operating costs such that a costbased DG routing optimization is achieved. Furthermore, the practicality of the algorithm is demonstrated by developing a computer application using Canadian and B.C. datasets.
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