0.2 0.4 0.3 0.1
Traffic_intensity
0.8 0.2 0.97 0.03 0.95 0.05 0.92 0.08
Node3
0 0.03 0.06 0.1
N_of_accid Traffic_intensity
0.05 0.01 0.94 0.06 0.01 0.9299999999999999 0.1 0.03 0.87 0.15 0.05 0.7999999999999999 0.05 0.01 0.94 0.06 0.01 0.9300000000000001 0.1 0.03 0.87 0.15 0.05 0.8
HGV_DGV_Cars
0.6 0.4 0.5 0.5 0.2 0.8
HGV_DGV_Cars Fire
0 0 0 0 0 0
HGV_DGV_Cars Node3 Fire
1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0.2 0.8 0 1 0.6 0.4 0.4 0.6 0.8 0.2 0.6 0.4 0.9 0.1 0.7 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
Node2 Fire
0 0 5 10
Node4 Cost Node5
1 1 1
HGV_DGV_Cars
252 328 356 358
Fractions of HGV, DGV and Cars in the total traffic capacity.
N persons
263 386 346 416
Number od endangered persons for HGV, DGV and Cars depending on fire development.
Escape r.
133 389 201 415
Decision concerning ecsape routes.
Cost1
130 441 204 473
Additional cost due to escape routs.
Injury
263 442 344 474
Number of fatal injuries per year\n.
N_of_accid
123 330 212 356
Number of accidents per year for the trafic intensity in vhkm per year (10 to 40 vhkm/year)
Fire
403 327 483 360
Probability of fire development for HGV, DGV and Cars.\n
Cost2
407 441 482 473
Economic consequences due to fire per year\n.\n
Total economic and societal cost
140 496 477 528
Totql cost due to economic and societal consequences\n
Traffic intensity
214 274 394 300
Vehicles per day
Bay bridge
222 205 352 237
Influence diagram analysing risk of bay bridge in San Fransisco\nInput file for the software product GeNie.
71 139 597 185
Innovation Transfer in Risk Assessment and Management of Aging Infrastructures \nSoftware tools
20 63 637 105