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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. "4.4.7 Additional Fields." HMCRP Report 1: Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press, 2009.

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Page
71
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Page
71
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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Database Analysis 71 Table 4-20. Completeness of contributing factors in TIFA/FARS. Vehicle Driver Packaging Infrastructure Situational Configuration Age Package Type Road Surface Pre-Crash Condition Cargo Body Experience Quantity Shipped Road Condition Event Type GVW Condition Quantity Lost Road Type Vehicle Speed Valid License Age (Cargo Tank) Traffic Way Impact Location Citation Issued Rollover Protection Access Control Primary Reason Inspection History Speed Limit Accident Type Design Specification No. of Lanes Weather Condition Light Condition Time of Day Key: > 95% 50% to 95% < 50% Not captured doned after 2005. The cargo weight variable was known precisely for about 84%, unknown for about 4%, and partially known (light load or full load) for the remaining 12% of the cases. Generally, the fields derived from the TIFA survey or extracted from FARS are populated fairly completely. Table 4-20 shows that missing data rates for the fields captured are quite uniformly low. If the field is present, for the most part, it is complete in more than 95% of the cases. GVW and quantity shipped are exceptions, because it can be difficult to determine precise values after the fact, but even for those variables there is some information for 80% to 90% of the cases. Miss- ing data rates are somewhat higher for vehicle speed prior to the crash, as that is even more diffi- cult to determine. That information is taken from PAR and is available for about 75% of the cases. Complete rates of missing data, averaged over five years, are provided for all TIFA variables in Appendix D (available on the TRB website at www.TRB.org by searching for HMCRP Report 1). 4.4.6 Data Quality The TIFA system includes multiple layers of quality control. The survey is administered by means of a telephone interview. Each case record is reviewed by an editor for accuracy, con- sistency, and completeness. The vehicle identification number of the power unit is decoded, and the vehicle description from the survey is compared with the original specifications. Infor- mation about cargo and operations are similarly compared with a library of information that has been accumulated from over 25 years during which the TIFA survey has been conducted. Survey information also is compared with the information on the original PAR. Any discrep- ancies are discussed with the interviewer, who may be required to make additional calls for information. Once discrepancies are resolved, the data are then entered with verification. At that point, there is a computerized check of each batch of keypunched cases for consistency and to identify any invalid codes or responses that are outside of the usual range. Unusual responses are reviewed by the data editors. Finally, when a data year is complete, there is a computerized check of all cases for invalid codes, inconsistent data, or unusual responses. 4.4.7 Additional Fields Although the TIFA survey adds valuable detail to the FARS data, additional data fields could add important detail about hazmat packaging and also enhance the information available on