摘要
Signal Detection Theory （SDT） offers an unparalleled deterministic set of decision variables necessary to formulate applied risks in transportation. SDT has distinct advantages over basic prediction models since the latter may not represent an entirely accurate analysis. Thresholds based on elements of stimulus （signal and noise） and response for： a Type I discrimination of response variable where decision outcomes and rates are computed for metacognition to discriminate a Type II of decision outcomes was set. We also adapted the classical Dijkstra＇s shortest path algorithm within a GIS environment using Avenue programming. Contours derived from LiDARwere used to set flood levels while satellite imagery corresponding to Red River of the North inundated （signal） areas were acquired amongst other spatial datasets. The signal information was further dichotomized using a binary yes-no model. Origin and destination points constrained within Fargo-Morehead were generated using a random point generator. From these points, trips were generated with some connected segments traversing through flooded areas. By analyzing False Alarm Rate （FAR） and Corrected Rejection （CRR） computation, we found out that, when Hit Rate （HR） and FAR are both low then there was an increased corresponding sensitivity. At 30-35 ft flood level, the values for FAR and HR was 0.97 and 0.91 respectively.When FAR〉HR, lower set flood levels offered numerous route choices. Corresponding routes with associated impedance can be classified for risk-averse drivers or risk-takers While the risk-averse avoid risky and unfavorable routes, the risk-taker optimizes at an adjustment factor of ω = 0.1 or ω = 0.2. An idealistic stage is achieved for a conservative, co, equal to 0.4 or 0.5, which indicates maximum achievement in terms of time gain and safety simultaneously. At ω = 0.0 the prevailing conditions can be considered unrealistic since they incorporate areas considered impassable with absolute resistance like segments with a ＂Road Closed＂ or ＂Detour＂ sign. The applicability of our approach can be used to design multi-level and multi-modal transportation systems involving risk.
