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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. "4.3.9 Compatibility with Other Databases." HMCRP Report 1: Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press, 2009.

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Page
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Page
65
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 65 additional resources, but would fit well within NHTSA's crash data program. Adding the follow- ing data fields is suggested: · Right of way. This data element would identify which vehicle, if any, within a crash had the right of way prior to the collision. This could be readily coded from the PAR in most cases. Some state crash reports include right of way on the report. Right of way would be very use- ful in most crashes in identifying the vehicle that primarily contributed to the crash. · Accident type. The General Estimates System (GES) file and Crashworthiness Data System (CDS) file both include an accident type variable coded at the vehicle level that captures the relative position and movement of the vehicle prior to its first harmful event. The TIFA data adds this to trucks in fatal crashes, but capturing this within the FARS system would be a valu- able addition. An accident type field can identify key relationships that describe how the crash occurred and suggest contribution (for example, by identifying the vehicle that crossed over the center line in a head-on collision). The following two fields would be useful although this would take additional resources and possibly require some change in the management of the FARS file: · Critical event is a field that would identify and describe the event that precipitated the vehicle crash. This field is included in both the GES and CDS files, so the agency is very familiar with (and, indeed, invented) its use. · Critical reason captures the "reason" for the critical event, classified broadly as driver, vehicle, or environment, with detailed levels under each. The variable is useful for identifying the immediate failure that led to the crash and would shed considerable light on crash causation. The field was used in the LTCCS, conducted jointly by the FMCSA and NHTSA, and in the National Motor Vehicle Crash Causation Survey (NMVCCS), conducted by NHTSA. Thus, the agency already has developed coding procedures for both variables. However, adding these fields might require some changes to the FARS protocol. Both are dif- ficult to code consistently and require a high level of focus and analysis. Currently, virtually all FARS fields are coded by analysts located off-site, that is in the 50 states and District of Colum- bia. But the coding of both GES and CDS is more centralized. In the LTCCS, both critical event and critical reason were coded by a small number of analysts in two locations. The National Cen- ter for Statistics and Analysis (NCSA) could adopt a similar method for the FARS file, if these data elements were added. 4.3.8 Potential Measures to Improve Data Quality The FARS quality control system is complete and mature. It is subject to annual review and adjustment, including continuous training of the coders. FARS might be improved if the system could be adapted to take advantage of the additional information provided through the TIFA system. FARS has not engaged TIFA in this regard, although one problem has been that information from TIFA has not been available in a timely fashion. However, greater cooperation between the systems would be valuable for both. 4.3.9 Compatibility with Other Databases The FARS file does not include case identifiers that can be used to uniquely link to other data systems, such as the PAR number. Including the PAR number would provide a hard link. (Note that the MCMIS Crash file report number field in the past was supposed to include the PAR number in one of the fields, and it is recommended that MCMIS require that again. Currently, many states use a random report number, rather than using the PAR number.)