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HMCRP Report 1: Hazardous Materials Transportation Incident Data for Root Cause Analysis (2009)
Hazardous Material Cooperative Research Program (HMCRP)

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Transportation Research Board. "4.1.12 Quality Control Process." HMCRP Report 1: Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press, 2009.

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Page
42
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Page
42
Front Matter (R1-R11)
Summary (1-8)
1.1 Project Purpose (9-9)
1.2.1 Literature Review (10-10)
1.2.3 Analysis of Databases (11-11)
1.3 Effective Methods to Ensure High-Quality Data (12-12)
1.4 Potential Measures to Enhance the Ability of Databases to Identify the Root Causes of Hazmat Crashes (13-13)
2.2.1 Rail Equipment - Train Accident Data (14-14)
2.2.2 Project 5 Overview - Developing Common Data on Accident Circumstances (15-15)
2.2.4 Transportation Research Circular 231: Truck Accident Data Systems: State-of-the-Art Report (16-16)
2.2.6 The Human Factors Analysis and Classification System - HFACS (17-17)
2.2.9 Highway Safety: Further Opportunities Exist to Improve Data on Crashes Involving Commercial Motor Vehicles (18-18)
2.2.11 Comprehensive Safety Analysis 2010: 2006 Listening Session (19-19)
2.2.16 Hazardous Materials Serious Crash Analysis: Phase 2 (20-20)
2.3 Summary of Findings and Implications (21-21)
2.3.2 Solutions Being Implemented or Under Consideration (22-22)
3.1 Introduction (23-23)
3.2 Summary of Responses from Carriers (24-24)
3.2.1 Carrier Satisfaction with HMIRS (25-25)
3.3.1 Shipper 1 (26-26)
3.3.2 Shipper 2 (27-27)
3.4.1 Interviews with Agencies Maintaining Databases (PHMSA) (28-28)
3.4.2 Interviews with Agencies Maintaining Databases (FMCSA) (29-29)
3.4.3 Interviews with Agencies Maintaining Databases (FRA) (30-30)
3.5 Summary of Findings from Interviews (31-31)
4.1.1 MCMIS Database Description (32-32)
4.1.3 Database Format (33-33)
4.1.6 Types of Fields Covered (34-34)
4.1.7 Database Purpose and Function (35-35)
4.1.10 Accuracy and Completeness of Data (36-36)
4.1.11 Identification of Hazmat Incidents in MCMIS (37-41)
4.1.12 Quality Control Process (42-42)
4.1.13 Interconnectivity with Other Databases (43-43)
4.1.14 Analyses Using Database (44-44)
4.1.15 Summary and Potential Measures for Improving Root Cause Analysis (45-45)
4.2 Hazardous Materials Incident Reporting System (HMIRS) (46-46)
4.2.1 Database Description (47-48)
4.2.3 Data Collection (49-49)
4.2.5 Accuracy and Completeness of Data (50-53)
4.2.8 Analyses Using Database (54-59)
4.2.9 Summary and Potential Measures for Improving Root Cause Analysis (60-60)
4.3 Fatality Analysis Reporting System (FARS) (61-61)
4.3.4 Types of Hazmat Data Included (62-62)
4.3.6 Data Quality (63-63)
4.3.7 Additional Fields (64-64)
4.3.9 Compatibility with Other Databases (65-65)
4.4.4 Types of Hazmat Data Included (66-66)
4.4.5 Usefulness of the Data for Determining Root Causes (67-70)
4.4.7 Additional Fields (71-71)
4.4.10 Data Uses (72-72)
4.5.1 Database Description (73-73)
4.5.3 Data Collection (74-74)
4.5.7 Interconnectivity with Other Databases (75-75)
4.5.8 Analyses Using Database (76-77)
4.5.9 Summary and Potential Measures to Improve Root Cause Analysis (78-78)
4.6 Railroad Accident/Incident Reporting System (RAIRS) (79-79)
4.6.1 Track, Roadbed, and Structures (80-80)
4.6.3 Mechanical and Electrical Failures (81-81)
4.6.5 Summary of Causes and Impact (82-83)
4.7.3 Data Collection (84-84)
4.7.5 Accuracy and Completeness (85-85)
4.8.1 Scope of Investigations (86-86)
4.8.2 Approach to Identifying Root Causes (87-87)
4.8.4 Data Quality (88-88)
4.8.5 Probable Cause Findings (89-89)
4.8.6 Summary (90-90)
4.9.1 Introduction (91-91)
4.9.4 Populating Records and Improving Data Quality (92-92)
4.9.6 Database Enhancements and Limitations (93-93)
4.9.7 Summary (94-94)
5.2 Information System Development (95-95)
5.2.1 Develop Framework for Identifying Contributing Causes and Root Causes of Hazardous Material Accidents (96-96)
5.2.3 Add or Modify Inventory Data in Databases (97-97)
5.2.5 Develop a System for Each Database That Will Target About 5% of Hazmat Crashes for More Detailed Investigation (98-98)
5.3.2 Complete Values for All Parameters (99-102)
5.4.1 Potential Measures for MCMIS (103-104)
5.4.2 Potential Measures for HMIRS (105-106)
5.4.3 Potential Measures for TIFA (107-107)
5.4.4 Potential Measures for RAIRS (108-108)
5.6 Follow-On Project (109-109)
References (110-111)
Appendices (112-112)
Abbreviations used without definitions in TRB publications (113-113)

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42 Hazardous Materials Transportation Incident Data for Root Cause Analysis Table 4-6. Proportion of cargo body type in MCMIS coded correctly, based on comparison with TIFA data. Cargo Body Type Frequency % Correct No cargo body (e.g., bobtail) 251 49.0 Van 5,432 71.9 Flatbed 1,713 70.1 Tank 1,011 77.7 Auto carrier 77 68.8 Dump 1,947 65.5 Refuse 288 64.9 cargo body types from the TIFA file, the number of such body types in TIFA, and the percentage identified correctly in MCMIS. Table 4-7 makes a similar comparison for truck configuration. The primary truck types, straight trucks with no trailer and tractor-semitrailers, are identified accurately 87.5% and 75.5% of the time, respectively. Less recognizable types like straight trucks pulling a trailer or bobtail tractors are less often accurately identified in MCMIS. Finally, Table 4-8 shows the percentage of selected variables that are coded the same in TIFA and the MCMIS Crash file. Variables shown in Table 4-8 are drawn from the FARS file and not from the TIFA interview. GVWR class in MCMIS aggregates the classes to 1 to 2, 3 to 6, and 7 to 8. The variable is left unknown in 62% of the cases, so the last row of the table shows the accuracy of the variable in MCMIS excluding unknowns. 4.1.12 Quality Control Process The MCMIS reporting methodology presents a difficult quality control process. First, there are a large number of jurisdictions filling out PARs that vary from state to state. Although many reporting agencies do not break down the reporting to the officer's level by providing a badge number, the 151,000 reports filed in 2005 were filled out by more than 61,000 agencies or indi- vidual officers. This means that, on average, a police officer from a specific agency might fill out less than three truck PARs in a given year. Assuming there are about 3,000 placarded shipments involved in crashes each year, the probability that a police officer will have to fill out a PAR for a placarded truck is on average, less than once every 20 years (60,000/3,000). This poses a signif- icant training problem if the officer will be filling out the hazmat supplement only a few times in his or her career. Requiring or sponsoring a formal training program in 50 states for an event that occurs a few times in an officer's career is probably not cost effective. Providing a guide to Table 4-7. Proportion of truck configuration in MCMIS coded correctly, based on comparison with TIFA data. Truck Configuration Frequency % Correct Straight truck 2,839 87.5 Straight truck plus trailer 373 42.9 Other straight truck 8 75.0 Bobtail tractor 175 61.7 Tractor-semitrailer 7,956 75.5 Tractor doubles 439 76.1