| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Uncertainty in Hazardous Materials Transportation Changhyun Kwon Department of Industrial & Systems Engineering University at Buffalo, SUNY May 7, 2015 C Kwon 1/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Outline 1 Introduction 2 Risk Measures 3 Data Uncertainty 4 Behavioral Uncertainty C Kwon 2/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Outline 1 Introduction 2 Risk Measures 3 Data Uncertainty 4 Behavioral Uncertainty C Kwon 3/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Hazardous Materials Hazardous Materials (hazmat), Dangerous Goods Hazardous Materials Class 1: Explosives Class 2: Gases Class 3: Flammable Class 4: Flammable Class 5: Oxidizer and Divisions: 2.1, 2.2, 2.3 Liquid and Solid, Spontaneously Organic Peroxide Divisions: 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 Combustible Liquid Combustible, and Divisions 5.1, 5.2 Dangerous When Wet Divisions 4.1, 4.2, 4.3 8 Class 6: Poison (Toxic) and Class 7: Radioactive Class 8: Corrosive Class 9: Dangerous Poison Inhalation Hazard Miscellaneous C Kwon 4/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Introduction Hazmat transportation Number of accidents is small compared to the number of shipments Consequence is very severe in terms of fatalities, injuries, large-scale evacuation and environmental damage C Kwon 5/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Introduction Table: 2014 Hazmat Summary by Transportation Phase 1 Transportation Phase Incidents Hospitalized Non-Hospitalized Fatalities Damages In Transit 4,190 2 53 5 $63,686,925 In Transit Storage 614 1 1 0 $1,629,889 Loading 3,262 3 20 0 $1,021,289 Unloading 8,149 5 47 1 $3,848,737 Unreported 1 0 0 0 $0 1 Hazmat Intelligence Portal, US Department of Transportation. C Kwon 6/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Introduction Table: Hazmat Shipment Tonnage Shares by Mode in 2007 2 Mode of Transportation Percentage of Tons Truck 53.9% Pipeline 28.2% Water 6.7% Rail 5.8% Multiple modes 5.0% Other and unknown modes 0.4% 2 Research and Innovative Technology Administration and US Census Bureau, 2007 Commodity Flow Survey, Hazardous Materials C Kwon 7/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Three Types of Uncertainty 1 Where will be the accident location? Probabilistic nature of traffic accident 2 How large will be the accident consequence? Data uncertainty 3 How do hazmat carriers determine routes? Behavioral uncertainty (1), (2): Risk Measures (2), (3): Robustness C Kwon 8/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Three Types of Uncertainty 1 Where will be the accident location? Probabilistic nature of traffic accident 2 How large will be the accident consequence? Data uncertainty 3 How do hazmat carriers determine routes? Behavioral uncertainty (1), (2): Risk Measures (2), (3): Robustness C Kwon 8/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Three Types of Uncertainty 1 Where will be the accident location? Probabilistic nature of traffic accident 2 How large will be the accident consequence? Data uncertainty 3 How do hazmat carriers determine routes? Behavioral uncertainty (1), (2): Risk Measures (2), (3): Robustness C Kwon 8/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Outline 1 Introduction 2 Risk Measures 3 Data Uncertainty 4 Behavioral Uncertainty C Kwon 9/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Hazmat Transportation Network G = ( N , A ) – a road network p ij N is the node set and A is the arc j O i D set. c ij p ij – accident probability on arc G = ( N , A ) ( i , j ) ∈ A . c ij – accident consequence of traveling on arc ( i , j ) ∈ A . C Kwon 10/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function � TR Expected Risk min p ij c ij l ∈P ( i , j ) ∈A l C Kwon 11/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function � TR Expected Risk min p ij c ij l ∈P ( i , j ) ∈A l � PE Population Exposure min c ij l ∈P ( i , j ) ∈A l C Kwon 11/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function � TR Expected Risk min p ij c ij l ∈P ( i , j ) ∈A l � PE Population Exposure min c ij l ∈P ( i , j ) ∈A l � IP Incident Probability min p ij l ∈P ( i , j ) ∈A l C Kwon 11/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function � TR Expected Risk min p ij c ij l ∈P ( i , j ) ∈A l � PE Population Exposure min c ij l ∈P ( i , j ) ∈A l � IP Incident Probability min p ij l ∈P ( i , j ) ∈A l � p ij ( c ij ) q PR Perceived Risk min l ∈P ( i , j ) ∈A l C Kwon 11/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function MM Maximum Risk min max ( i , j ) ∈A l c ij l ∈P C Kwon 12/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function MM Maximum Risk min max ( i , j ) ∈A l c ij l ∈P � ( p ij c ij + kp ij ( c ij ) 2 ) MV Mean-Variance min l ∈P ( i , j ) ∈A l C Kwon 12/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function MM Maximum Risk min max ( i , j ) ∈A l c ij l ∈P � ( p ij c ij + kp ij ( c ij ) 2 ) MV Mean-Variance min l ∈P ( i , j ) ∈A l � DU Disutility min p ij (exp( kc ij − 1)) l ∈P ( i , j ) ∈A l C Kwon 12/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Comparison of Risk Measures Model Risk Measure Function MM Maximum Risk min max ( i , j ) ∈A l c ij l ∈P � ( p ij c ij + kp ij ( c ij ) 2 ) MV Mean-Variance min l ∈P ( i , j ) ∈A l � DU Disutility min p ij (exp( kc ij − 1)) l ∈P ( i , j ) ∈A l � � � CR Conditional Probability min p ij c ij p ij l ∈P ( i , j ) ∈A l ( i , j ) ∈A l C Kwon 12/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Value-at-Risk (VaR) in Hazmat Problem 3 Cutoff Risk β l α for path l such that the probability of a shipment experiencing a greater risk than β l α is less than confidence level α α = min { β : Pr( R l > β ) ≤ 1 − α } VaR l VaR = 100 at α = 99%: With probability 99%, risk is less than 100. Risk of a path l : m l � 0 , w.p. 1 − p i i =1 R l = C 1 , w.p. p 1 . . . C m l , w.p. p m l C Kwon 3Kang, Y., R. Batta and C. Kwon (2014), “Value-at-Risk Model for Hazardous Material Transportation”, 13/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Value-at-Risk (VaR) in Hazmat Problem 3 Cutoff Risk β l α for path l such that the probability of a shipment experiencing a greater risk than β l α is less than confidence level α α = min { β : Pr( R l > β ) ≤ 1 − α } VaR l VaR = 100 at α = 99%: With probability 99%, risk is less than 100. Risk of a path l : m l � 0 , w.p. 1 − p i i =1 R l = C 1 , w.p. p 1 . . . C m l , w.p. p m l C Kwon 3Kang, Y., R. Batta and C. Kwon (2014), “Value-at-Risk Model for Hazardous Material Transportation”, 13/54
| Industrial and Systems Engineering Intro Risk Measures Data Uncertainty Behavioral Uncertainty Value-at-Risk (VaR) in Hazmat Problem 3 Cutoff Risk β l α for path l such that the probability of a shipment experiencing a greater risk than β l α is less than confidence level α α = min { β : Pr( R l > β ) ≤ 1 − α } VaR l VaR = 100 at α = 99%: With probability 99%, risk is less than 100. Risk of a path l : m l � 0 , w.p. 1 − p i i =1 R l = C 1 , w.p. p 1 . . . C m l , w.p. p m l C Kwon 3Kang, Y., R. Batta and C. Kwon (2014), “Value-at-Risk Model for Hazardous Material Transportation”, 13/54
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