appendix a: data collection, reduction, and analysis in...

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APPENDIX A:

DATA COLLECTION, REDUCTION, AND ANALYSIS IN CALIFORNIA, NORTH CAROLINA, OHIO, AND WASHINGTON

OVERVIEW This research project was undertaken in order to determine whether work performed at a particular location will produce lower total crash costs as a nighttime work zone or as a daytime work zone. Although desirable from the standpoint of statistical rigor, conducting direct comparisons of day and night work (the same type of work, in fact) at the same sites is obviously not possible. Therefore, it is necessary to utilize indirect methods of assessing the comparative risks of doing the same type of work on the same type of roadway between these two potential time periods of interest. Data collection for this part of the research project involved two distinct activities. One activity was the field data collection of daytime and nighttime work zone project information from each of the study states, whereas the second is the extraction of roadway and crash data from the Highway Safety Information System (HSIS) database that correspond to each project and comparison sites. Researchers used all these data to determine the additional crash costs generated by each work zone. This appendix documents the data collection and reduction procedures, as well as the safety performance functions (SPFs) used to conduct this analysis. WORK ZONE AND TRAFFIC VOLUME DATA COLLECTION AND REDUCTION Desired Work Zone Configurations The approach used by the research team was to target a finite number of specific work zone configurations, some of which were performed with daytime work activities and others which were performed at night. Based on discussions with highway agency personnel in several of the HSIS states and the research team’s recent experiences with night work projects in Texas, nationally in the past few years, the majority of night work projects have occurred on freeway facilities in urban areas. Such facilities were therefore the primary focus of the analysis. Researchers believe that targeting other roadway types such as two-lane highways or urban arterials with signalized intersections, would simply not yield sufficient night work sample sizes to allow meaningful analyses. With the investigation targeted towards freeway projects, three other main work zone configuration parameters were utilized in project site selection and data analysis: number of lanes normally present and number closed in the work zone during work activity, average daily traffic (ADT) (both overall and per open lane), and type of work being performed.

The relationship between the number of travel lanes normally present and the number closed impacts not only the traffic-carrying capacity through the work zone (and thus the potential for queuing conditions to develop), but also the number of lane-change maneuvers that will be

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required from approaching motorists. Researchers expected that lane-changing requirements may correlate with an increase in traffic conflicts, and ultimately traffic crashes. Researchers targeted freeway sections that normally had 2 to 4 lanes per direction, and work zones that had one or more lanes closed during times of work activity. Researchers believed that there would be very few night work zone projects that did not require at least one lane to be closed (the very reason that work is performed at night is because a lane must be closed and doing so during the day would create unacceptable traffic queues and delays). ADT was another key work zone parameter used to target projects to be included in the analysis. Researchers sampled from the projects made available in such a way that ensured that those selected encompassed as wide a range of overall ADTs and ADT/open lane values as possible. The third parameter used in defining desired project configurations for analysis was the type of work being performed. Researchers utilized two main categories based on recent findings from night work research in Texas. The first category was road work that involves only temporary traffic control devices to close a lane during a particular night or particular day work period, devices which are then completely removed at the end of each work shift. For these projects, roadway geometrics remain the same as they were before the work shift began. This category included typical pavement repair and rehabilitation projects such as hot-mix asphalt overlays, pavement grinding or milling procedures, pavement patching, etc. The next category was those projects that involve major freeway reconstruction and widening and/or bridge reconstruction/replacement. These types of projects involve both temporary changes in geometrics (lane shifts, reduced lane and shoulder widths, shoulder and acceleration/deceleration lane closures, sight distance restrictions, etc.) that exist throughout the duration of the project, and additional temporary lane closures during the day or at night as needed to perform certain activities in the travel lanes. Thus, data when the work zone is inactive represents the effects of the fixed work zone geometrics implemented during construction, while data when the work zone is active reflects a combined influence of the fixed geometrics and work activity. States Targeted Of the eight states participating in the HSIS program at the time of this project, researchers received positive responses from four regarding their willingness to assist in this research effort: California, North Carolina, Ohio, and Washington.

In addition, researchers initially planned to use projects in Texas; however, difficulties with the development of the Crash Records Information System (CRIS) ultimately required that Texas be dropped from the list of states with usable data.

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Work Zone Data Collection Procedures Researchers worked closely the key personnel in each of the four remaining states to identify suitable nighttime and daytime projects for inclusion in the work zone crash analysis dataset. This process typically involved the initial identification of all projects completed by a state department of transportation (DOT) on limited-access roadways for a certain time period (e.g., 2001-2005), followed by telephone and email contacts with the district and resident engineers overseeing those projects to determine whether they were conducted during the day or at night. Once a final list of projects of interest were identified from each state and the representative from that state concurred, two-person teams of researchers travelled to each DOT office, except Ohio, where the project records were kept. Researchers then reviewed the project diaries, traffic control plans, and other documentation as necessary to determine the following: mile point limits of the project; project start date and end dates; general type of work performed or improvement being made; dates of major phase changes or traffic control switches (for reconstruction/widening

projects); daily (or nightly) information regarding if, when, and where work actually occurred; number

and travel direction of lanes closed; length of lane closures; workers and equipment present; and any other notes that are unique to that particular night;

police presence; presence of any traveler information or other ITS components to influence driver behavior; presence and characteristics of the illumination present during night work activities; and availability of hourly traffic volumes before and during construction.

The two-person team spent two weeks at each site collecting these data. As indicated above, researchers did not have to travel to Ohio to collect these data. Ohio has developed a unique construction management system in which a summary of project diary information is typed into an electronic database for each project. Thus, researchers were able to obtain project diary data and traffic control plans electronically. Project Sample Size and Characteristics The original goal of the research team was to gather data from a total of 100 projects across five states (California, North Carolina, Ohio, Texas, and Washington). However as noted above, the research team could not utilize Texas data. Table A-1 contains a summary of the project sample size from each state throughout the identification, data collection, and data analysis stages. The number of projects ultimately obtained from each state was dependent upon how much data could be reasonably retrieved and accommodated by the state DOT and the travel budget of the research team. All total, work zone data were collected from 84 projects; however, the analysis only included data from 64 projects due the following reasons. Based on the HSIS roadway inventory, three of the projects (two in North Carolina and one

in Washington) contained sections of roadway that were not limited access facilities (i.e., freeway). This happens when the work activity occurs in the area where a roadway changes

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to or from a limited access facility. Researchers decided not to include these projects in the dataset since the accident trends on these roadways may differ from the rest of the dataset.

Ten of the Ohio projects could not be used since the electronic diary data did not include the exact work times (e.g., midnight to 5 am), information concerning lane closures, or both. Researchers contacted Ohio DOT personnel to obtain more detailed hard-copy diaries; however, researchers were told that the hard-copy diaries would not contain any additional information.

During the data collection and reduction stages researchers anticipated that the 2005 Ohio HSIS crash data would become available; however, this did not come to fruition. Thus, two Ohio projects conducted during this time period could not be used.

One of the Ohio projects could not be used since the mile points where the project occurred were missing from the HSIS database from calendar years 1997 to 2004.

WADOT did not provide crash data to the Federal Highway Administration (FHWA) for inclusion into the HSIS database from calendar years 1997 and 1998. Thus, four Washington projects conducted during this time period could not be used.

Table A-1. Project Sample Size.

Number of Projects State Initial List Work Zone Data Collected Used in AnalysisCalifornia 16 16 16 North Carolina 28 22 20 Ohio 23 23 10 Washington 25 23 18 Total 92 84 64

Unfortunately, these issues were not discovered until after researchers had collected diary and traffic control plan data. Even so, the final project dataset represents a range of work periods and times of lane closures (daytime only, nighttime only, and both daytime and nighttime periods are represented). The dataset also includes several work zone types (major reconstruction, pavement widening, bridge work, pavement repair or rehabilitation, and lighting or other traffic control device installation/upgrading). A summary of the projects by state are shown in Table A-2 through Table A-5. Additional details regarding each project are discussed later. Not all of the project mile point limits were noted in the Ohio traffic control plans obtained. Therefore, researchers worked with the HSIS database administrators to estimate actual mile point limits of those projects based on roadway landmarks and other features recorded in the HSIS roadway inventory file and the physical maps and other drawings in those plans.

Table A-2. Summary of California Projects

Project No. State County

Hwy. No. Type of Work

Beginning Date

End Date

Duration(days)

Starting Mile

Point a

Ending Mile

Point a

Section Length (miles)

Total Length (miles)

04-OC7014 CA Alameda I-880 Pavement

Repair/Rehab 3/15/2001 11/26/2002 622 2.50 15.29 12.79 12.79

04-044OU4 CA Contra Costa I-680 Bridge Work 4/6/2000 3/9/2002 703 24.10 25.46 1.36 1.36

04-042234 CA San Mateo Rte 92 Bridge Work 1/14/2003 8/14/2003 213 13.77 17.24 3.47 3.47

04-0TO504 CA Solano I-80 Pavement

Repair/Rehab 8/13/2001 12/22/2003 862 0.50 4.00 3.50 3.50

04-0C2704 CA Solano I-780

(Rte 74)Pavement

Repair/Rehab 4/23/2001 4/12/2002 355 0.68 7.44 6.76 6.76

04-045034 CA Alameda Rte 92

Pavement Repair/Rehab Bridge

Work Pavement Widening 5/5/2000 4/30/2001 361 2.30 6.40 4.10 4.10

04-0C2904 CA Marin Rte 101 Pavement

Repair/Rehab 10/4/2001 2/27/2002 147 18.60 23.40 4.80 4.80

04-1R6701 CA Santa Clara Rte 237 Pavement

Repair/Rehab 11/17/2000 9/28/2001 316 R3.80 7.30 3.50 3.50 34.40 R40.80 6.40

04-0C5004 CA Sonoma Rte 101 Pavement

Repair/Rehab 10/6/2000 8/31/2001 330 R46.00 R50.70 4.70 11.10

06-440104 CA Fresno Rte 99

Fence Removal & Installation

Barrier Rail Work Landscaping 7/31/2001 2/13/2003 563 14.50 31.50 17.00 17.00

06-426004 CA Fresno Rte 41 Pavement

Repair/Rehab 7/24/2000 10/5/2000 74 22.50 31.90 9.40 9.40

06-397004 CA Madera Rte 99 Pavement

Repair/Rehab 7/8/2002 8/14/2003 403 13.00 23.00 10.00 10.00 20.20 27.30 7.10

06-499804 CA Fresno Rte 99 Pavement

Repair/Rehab 8/6/2003 7/2/2004 332 28.10 31.60 3.50 10.60

03-367004 CA Butte Rte 70 Pavement

Repair/Rehab 5/1/2002 1/7/2003 252 13.50 19.80 6.30 6.30

03-0A7814 CA Sacramento Rte 99 Pavement

Repair/Rehab 4/9/2001 12/11/2002 612 17.60 22.20 4.60 4.60

03-2A4604 CA Nevada I-80 Pavement Widening

Bridge Work 4/19/2002 11/13/2003 574 14.10 15.70 1.60 1.60 a “R” denotes revised mile point.

Table A-3. Summary of North Carolina Projects.



Beginning Date

End Date

Duration(days)

Starting Mile Point

Ending Mile Point


Total Length(miles)

I-4415 NC Johnston I-95 Pavement

Repair/Rehab 1/27/2003 11/24/2003 302 22.15 30.34 8.19 8.19 Guilford 27.50 29.57 2.07

I-4414 NC Alamance I-

85/40 Pavement

Repair/Rehab 6/3/2002 6/29/2003 392 0.00 1.00 1.00 3.07

I-3602 NC Iredell I-40 Pavement

Repair/Rehab 2/19/2001 5/31/2002 467 4.50 11.62 7.12 7.12

I-3308A NC Iredell I-77 Pavement

Repair/Rehab 5/23/2001 5/20/2004 1094 14.72 23.37 8.65 8.65 Nash 18.35 26.27 7.92

I-3102A NC Halifax I-95 Pavement

Repair/Rehab 8/6/2002 12/1/2003 483 0.00 2.35 2.35 10.27 I-2201F NC Guilford I-40 Pavement Widening 9/15/1998 10/1/2003 1843 5.00 12.19 7.19 7.19

Durham 8.60 12.78 4.18 I-2204BA NC Wake I-40 Pavement Widening 8/13/2001 11/26/2003 836 0.00 1.20 1.20 5.38

I-4412 NC Mecklenburg I-

85/40 Pavement

Repair/Rehab 5/22/2002 6/28/2003 403 8.10 11.40 3.30 3.30 Davie 17.70 19.30 1.60

I-4741 NC Forsyth I-40 Pavement

Repair/Rehab 3/15/2004 7/30/2004 138 0.00 0.88 0.88 2.48

I-4030 NC Cleveland I-85 Pavement

Repair/Rehab 11/2/2000 7/30/2001 271 0.00 7.60 7.60 7.60

I-3606 NC Wilson I-95 Pavement

Repair/Rehab 6/4/2001 3/28/2003 663 6.00 15.00 9.00 9.00 Rowan 18.06 19.44 1.38

I-4036 NC Davidson I-85 Pavement

Repair/Rehab 4/10/2001 7/20/2001 102 0.00 5.18 5.18 6.56

I-3309A NC Iredell I-77

Pavement Repair/Rehab Bridge

Work 7/31/2000 12/1/2001 489 23.37 27.57 4.20 4.20 Yadkin 12.10 13.73 1.63

I-4025 NC Surry I-77 Bridge Work 12/6/2001 5/13/2003 524 0.00 0.54 0.54 2.17

I-2807A NC Surry I-77 Pavement

Repair/Rehab 4/13/2000 11/19/2001 586 0.10 4.55 4.45 4.45 I-2511BB NC Rowan I-85 Pavement Widening 8/5/1998 5/18/2004 2114 6.60 12.80 6.20 6.20

Orange/ Durham I-85

6.50 (Orange)

2.39 (Durham) 11.89 I-4017 NC

Orange I-40 Guardrail Installation 10/2/2000 6/29/2001 271

7.45 13.44 5.99 24.20

Orange/ Durham I-40

17.9 (Orange)

1.51 (Durham) 2.83

Durham I-40 5.87 9.36 3.49 Pender 1.83 25.71 23.88

I-4408 NC Hanover I-40 Pavement

Repair/Rehab 7/2/2001 2/22/2002 236 0.00 6.36 6.36 30.24

W-4439 NC Gaston I-85 Pavement

Repair/Rehab 5/16/2003 7/25/2003 71 8.90 12.48 3.58 3.58

I-4403 NC Robeson I-95 Pavement

Repair/Rehab 9/9/2001 10/22/2001 44 26.00 28.00 2.00 2.00

Table A-4. Summary of Ohio Projects.



BeginningDate

End Date

Duration(days)

Starting Mile Point

Ending Mile Point


Total Length(miles)

487-03 OH Allen I-75 Pavement Repair/Rehab 5/5/2004 7/24/2004 81 0.21 9.60 9.39 9.39 Stark 17.61 18.54 0.93

454-01 OH Summit I-77 Pavement Widening 2/15/2002 6/24/2004 861 0.00 1.59 1.59 2.52 119-01 OH Summit I-77 Bridge Work 4/3/2001 6/10/2003 799 24.19 28.37 4.18 4.18 5013-02 OH Summit I-77 Pavement Repair/Rehab 10/2/2002 4/21/2003 202 19.51 24.19 4.68 4.68 430-03 OH Stark US 30 Pavement Repair/Rehab 9/17/2003 7/13/2004 301 13.10 16.82 3.72 3.72

Hamilton 16.77 17.47 0.70 420-02 OH Butler I-75 Pavement Widening 12/2/2002 12/31/2004 a 761 0.00 5.35 5.35 6.05 172-02 OH Clermont I-275 Pavement Widening 6/17/2002 12/31/2004 a 929 9.79 13.79 4.00 4.00 32-02 OH Hamilton I-275 Pavement Widening 3/5/2002 12/31/2004 a 1033 29.79 30.12 0.33 0.33

271-04 OH Lake I-90 Pavement Repair/Rehab 7/19/2004 10/27/2004 101 21.50 29.21 7.71 7.71 Cuyahoga 29.56 30.20 0.64

534-02 OH Lake I-90 Pavement Widening 3/31/2003 12/31/2004 a 642 0.00 0.80 0.80 1.44 a The actual project end date occurred in 2005; however, 2005 Ohio crash data were not available.

Table A-5. Summary of Washington Projects.



Beginning Date

End Date

Duration(days)

Starting Mile Point

Ending Mile Point


Total Length (miles)

5620 WA King SR 520 Major Reconstruction 6/25/1999 11/29/2000 524 9.00 11.30 2.30 2.30 6621 WA King I-5 Pavement Repair/Rehab 8/4/2003 1/16/2004 166 171.57 176.60 5.03 5.03 5694 WA Snohomish SR 405 Pavement Widening 8/24/1999 1/10/2003 1236 26.00 30.34 4.34 4.34 6125 WA Pierce SR 16 Pavement Repair/Rehab 8/13/2001 1/17/2002 158 9.39 15.48 6.09 6.09 6116 WA Thurston SR 5 Pavement Repair/Rehab 7/2/2001 10/16/2001 107 101.01 104.28 3.27 3.27 5712 WA Pierce I-5 Pavement Repair/Rehab 8/26/1999 11/21/2000 454 124.21 135.32 11.11 11.11 6084 WA King I-5 Lighting Installation 10/15/2001 8/9/2002 299 166.20 167.77 1.57 1.57 6317 WA King I-405 Bridge Work 5/28/2002 9/9/2004 836 12.82 14.82 2.00 2.00 5970 WA Pierce SR 16 Pavement Repair/Rehab 2/19/2001 1/29/2003 710 0.00 2.10 2.10 2.10 6719 WA Benton I-82 Pavement Repair/Rehab 6/1/2004 7/10/2004 40 122.17 123.27 1.10 1.10 6718 WA Benton I-82 Pavement Repair/Rehab 6/2/2004 9/10/2004 101 82.14 84.35 2.21 2.21

6061 WA Kittitas I-90 Pavement Repair/Rehab

Bridge Work 5/23/2001 11/15/2001 177 106.30 110.11 3.81 3.81 6637 WA Kittitas I-90 Pavement Widening 9/16/2003 8/17/2004 337 90.57 92.76 2.19 2.19 6630 WA Kittitas I-90 Pavement Widening 9/3/2003 11/17/2004 442 125.51 135.72 10.21 10.21

35.05 40.15 5.10 42.75 43.25 0.50

6790 WA King I-90 Bridge Work 7/19/2004 8/31/2004 44 45.25 45.85 0.60 6.20 5822 WA Yakima I-82 Pavement Repair/Rehab 6/9/2000 8/29/2000 82 58.27 58.96 0.69 0.69

Yakima/ Benton

38.75 (Yakima)

82.15 (Benton) 43.40

Benton 90.00 95.55 5.55 5951 WA Benton I-82 Pavement Repair/Rehab 3/12/2001 7/3/2001 114 100.80 132.36 31.56 80.51

55.27 58.91 3.64 5906 WA Kittitas I-90 Pavement Repair/Rehab 9/18/2000 4/26/2001 221 61.89 67.56 5.67 9.31

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Researchers also worked with the HSIS database administrators to determine the correct mile points to designate for the California project limits. The roadway inventory file from California includes a large number of revised mile points on various roadway segments due to significant alignment changes and other factors. For each project, it was necessary to determine the correct mile points for the project limits and comparison segments (discussed later) on a year-by-year basis, in the event that the mile points were modified during a particular year. Further refinements to the project limits occurred after researchers received and reviewed the HSIS crash data for each project. These changes are discussed in more detail later in this appendix. Traffic Volume Data Collection and Reduction For each state, researchers obtained historical traffic volume data for freeway facilities similar in nature to those included in the work zone project dataset (same region, number of lanes, etc.). From these data, researchers developed the hourly distribution percentages shown in Table A-6.

Table A-6. Hourly Traffic Volume Percentages.

State

Time California (n=300) a

North Carolina (n=20) b

Ohio (n=150) a

Washington (n=10) b

Mid-1A 1.23% 1.27% 1.20% 1.02% 1A-2A 0.87% 0.90% 0.86% 0.69% 2A-3A 0.74% 0.76% 0.77% 0.57% 3A-4A 0.75% 0.71% 0.80% 0.55% 4A-5A 1.17% 0.88% 1.10% 1.03% 5A-6A 2.38% 1.63% 2.32% 2.37% 6A-7A 4.14% 3.66% 4.81% 3.96% 7A-8A 5.45% 5.57% 6.71% 4.99% 8A-9A 5.27% 5.26% 5.98% 5.20% 9A-10A 5.20% 5.19% 5.03% 5.46% 10A-11A 5.43% 5.54% 4.88% 5.75% 11A-Noon 5.75% 5.86% 5.09% 6.09% Noon-1P 6.04% 6.07% 5.27% 6.31% 1P-2P 6.23% 6.27% 5.47% 6.49% 2P-3P 6.62% 6.65% 6.13% 6.75% 3P-4P 7.05% 7.16% 7.19% 7.02% 4P-5P 7.17% 7.40% 7.75% 7.16% 5P-6P 6.85% 7.43% 7.64% 6.90% 6P-7P 5.69% 5.98% 5.63% 5.93% 7P-8P 4.49% 4.59% 4.20% 4.67% 8P-9P 3.73% 3.70% 3.58% 3.79% 9P-10P 3.28% 3.14% 3.21% 3.25% 10P-11P 2.60% 2.50% 2.52% 2.45% 11P-Mid 1.88% 1.87% 1.86% 1.60% a N = the number of counts on which the percentages were based. b N = the number of count stations on which the percentages were based.

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For each project, researchers also obtained the average annual daily traffic (AADT) data from the HSIS roadway inventory files. However, researchers encountered the following difficulties and so had to develop some methods of circumventing these issues in order to obtain the desired data. The 2003 and 2004 California roadway inventory data were not available. So California

DOT personnel matched the 2003 and 2004 crash data to the 2005 roadway inventory data. Thus, researchers had to assume that the 2003 and 2004 AADT data were similar to the 2005 AADT data.

Initially, for several North Carolina projects it appeared that HSIS was missing roadway inventory files for some sections. After reviewing the data, it became apparent that the majority of the missing sections were in locations where two routes overlapped (e.g., I-40 and I-85 are the same road in some locations). Thus, even though the project was conducted on one roadway (I-85), the AADT data was associated with the second roadway (I-40) in the roadway inventory data. This issue caused some delays with obtaining the correct data, but was ultimately resolved.

When extracting Ohio data from HSIS database, the HSIS database administrators identified several problems with the 2000 and 2001 crash and roadway inventory data. In the corrected crash data sent to HSIS, the 2000 and 2001 crash data were matched to the 2002 roadway inventory data. Thus, researchers had to assume that the 2000 and 2001 AADT data were similar to the 2002 AADT data.

The 1997 to 2001 Washington roadway inventory data were not available. The 1997 and 1998 roadway inventory data were ultimately not needed since the crash data for these years were also not available. Researchers worked with the HSIS database administrators to obtain AADT data for 1999 and 2001 from another source. Researchers assumed that the 2000 AADT data were similar to the 2001 AADT data.

Work Zone Data Reduction To facilitate the matching of crash and work zone data, researchers entered the data collected from the project diaries of each project (daily and nightly hours of activity, number of lanes closed, closure locations, etc.) into electronic spreadsheet files. For each project, researchers then computed the number of hours and vehicle-miles travelled (VMT) for the following time periods of interest: Daytime when work activity was occurring and there was at least one lane closed, Daytime when work activity was occurring and there were no lane closures, Nighttime when work activity was occurring and there was at least one lane closed, Nighttime when work activity was occurring and there were no lane closures, Daytime when the work zone was inactive and there was at least one lane closed, Daytime when the work zone was inactive and there were no lane closures, Nighttime when the work zone was inactive and there was at least one lane closed, and Nighttime when the work zone was inactive and there were no lane closures.

These statistics are shown in Table A-8 through Table A-15. Independent of the time of year and project location, researchers defined daytime as 6:00 am to 6:59 pm (13 hours) and nighttime as

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7:00 pm to 5:59 am (11 hours). Researchers directly calculated the number of hours for each time period from the diary data. Researchers then applied the appropriate hourly traffic volume distribution percentages to the AADT data obtained from the HSIS roadway inventory file for each project location to estimate traffic exposure (i.e., VMT). Comparison Segment Identification After the work zone data had been collected and reduced, researchers selected comparison segments with similar roadway and traffic characteristics for each project. Initially, these comparison segments were located immediately upstream and downstream of the project locations. However, in order to develop more accurate annual factors and SPFs additional route-miles were required. These additional route-miles were in the same general region as the project locations in order to capture the influences of regional traffic demand changes, unusual weather effects, etc. To ensure that the comparison segments included only freeway facilities, researchers verified the roadway classification in the roadway inventory data. Table A-7 contains a summary of the comparison segment data. Further refinements to the comparison segments occurred after researchers received and reviewed the HSIS crash data. These changes are discussed in more detail later in this appendix.

Table A-7. Summary of Comparison Segment Data.

State Statistic California North Carolina Ohio Washington

Time Period 1995 to 2004 1995 to 2004 1997 to 2004 1993 to 2004 # of Years 10 10 8 10 a Mile-Years b 2505.73 3629.16 1998.68 2313.03 a Data were not available in 1997 and 1998. b Mile-Years is the total number of miles for the entire the time period. The exact number of miles included for each year is not necessarily the mile-years divided by the number of years.

Table A-8. Number of Hours by Work Status, Lane Closure Status, and Time Period for California Projects.

Work Active Work Inactive

Lane Closure No Lane Closure Lane Closure No Lane Closure Project Day Night Day Night Day Night Day Night

03-0A7814 105 1,973 1,948 101 0 0 5,904 4,658 03-2A4604 1,578 269 851 29 37 94 4,997 5,922 03-367004 885 33 584 10 33 185 1,775 2,545 04-0C2704 16 614 282 98 0 0 4,317 3,194 04-0C2904 16 192 10 32 0 0 1,886 1,393 04-0C5004 741 123 61 0 0 0 3,489 3,507 04-0C7014 107 1,969 527 138 0 0 7,453 4,734 04-0T0504 28 1,776 3,998 544 0 0 7,180 7,162 04-1R6701 1 506 0 0 0 0 4,107 2,971 04-0440U4 23 738 0 0 0 0 9,116 6,995 04-042234 8 909 2 7 0 0 2,759 1,427 04-045034 102 1,037 2,482 188 0 0 2,109 2,746 06-426004 11 190 20 168 0 0 931 456 06-397004 226 1,204 118 42 0 0 4,895 3,187 06-440104 26 1,130 3,140 182 0 0 4,153 4,882 06-499804 74 1,166 293 29 0 0 3,950 2,457

Total 3,945 13,829 14,314 1,567 69 279 69,019 58,235

Table A-9. VMT by Work Status, Lane Closure Status, and Time Period for California Projects.


Lane Closure No Lane Closure Lane Closure No Lane Closure Project Day Night Day Night Day Night Day Night

03-0A7814 4,532,406 27,277,228 87,602,697 1,498,305 0 0 274,356,996 81,377,054 03-2A4604 4,392,340 293,723 2,342,551 26,436 106,775 93,399 14,396,485 5,969,764 03-367004 7,373,105 139,283 4,782,890 37,774 280,763 543,805 15,064,424 7,544,873 04-0C2704 314,148 4,239,909 5,951,941 735,227 0 0 94,261,035 25,239,219 04-0C2904 376,815 1,568,080 309,283 285,768 0 0 59,040,090 16,097,409 04-0C5004 11,942,682 748,686 977,573 0 0 0 57,924,381 20,544,324 04-0C7014 12,582,140 65,368,227 70,501,868 5,872,685 0 0 1,013,250,236 258,272,47504-0T0504 624,353 13,668,792 105,160,426 4,808,859 0 0 196,791,304 72,464,387 04-1R6701 16,823 3,600,224 0 0 0 0 96,702,794 25,469,751 04-0440U4 172,568 1,717,112 0 0 0 0 71,223,682 19,741,691 04-042234 106,882 4,539,755 30,948 29,076 0 0 52,714,227 11,316,342 04-045034 2,215,123 7,224,323 56,495,409 1,421,719 0 0 47,149,387 23,171,134 06-426004 546,830 3,530,939 992,315 3,078,683 0 0 48,171,477 8,331,364 06-397004 6,731,481 14,085,268 3,741,760 427,873 0 0 158,912,821 36,397,404 06-440104 1,770,493 28,252,092 226,310,691 4,698,669 0 0 313,596,155 129,855,36806-499804 3,021,842 21,554,438 14,904,173 415,672 0 0 208,903,003 46,205,447

Total 56,720,030 197,808,081 580,104,525 23,336,749 387,538 637,204 2,722,458,497 787,998,007

Table A-10. Number of Hours by Work Status, Lane Closure Status, and Time Period for North Carolina Projects.


Lane Closure NO Lane Closure Lane Closure NO Lane Closure Project Day Night Day Night Day Night Day Night I-2201F 111 1,432 11,030 371 0 0 12,818 18,471

I-2204BA 70 1,777 4,124 1,114 0 0 6,675 6,305 I-2511BB 1,535 2,239 11,753 346 1,275 1,256 12,920 19,413 I-2807A 4,036 189 455 0 834 3,642 2,293 2,615 I-3102A 2,378 315 259 9 0 0 3,643 4,990 I-3308A 407 4,107 277 78 0 0 13,538 7,849 I-3309A 1,889 14 791 9 1,064 2,275 2,614 3,082 I-3606 2,546 35 440 5 360 1,943 5,274 5,310 I-4017 0 291 348 247 0 0 3,176 2,444 I-4025 1,951 35 312 0 1,567 2,848 2,982 2,882 I-4030 78 2 764 26 0 0 2,682 2,953 I-4036 17 278 61 131 0 0 1,248 714 I-4403 64 9 27 0 0 0 481 475 I-4408 920 18 203 4 0 0 1,946 2,575 I-4412 12 397 27 47 0 0 5,200 3,990 I-4414 49 1,145 63 96 0 0 4,984 3,071 I-4415 1,560 42 100 0 0 0 2,267 3,281 I-4741 345 153 20 0 0 0 1,430 1,365

W-4439 32 138 10 8 0 0 881 635 I-3602 277 1,810 2 75 0 0 5,806 3,263 Total 18,273 14,424 31,062 2,564 5,099 11,964 92,856 95,678

Table A-11. VMT by Work Status, Lane Closure Status, and Time Period for North Carolina Projects.


Lane Closure NO Lane Closure Lane Closure NO Lane Closure Project Day Night Day Night Day Night Day Night I-2201F 4,126,359 17,853,936 450,400,172 5,332,597 0 0 509,000,004 247,786,016

I-2204BA 2,502,037 21,508,628 166,801,059 15,502,450 0 0 269,684,852 86,445,494 I-2511BB 35,471,920 16,663,044 278,913,767 3,551,220 29,387,030 10,110,572 295,341,952 149,413,370I-2807A 28,240,681 410,975 3,039,213 0 5,125,079 8,344,137 15,389,210 5,810,965 I-3102A 52,897,524 3,378,641 5,665,874 91,210 0 0 80,841,082 35,734,868 I-3308A 8,824,419 33,770,097 7,326,069 818,525 0 0 337,524,978 64,875,522 I-3309A 15,754,453 79,705 6,630,331 46,112 8,397,292 6,127,951 21,227,166 8,372,790 I-3606 42,663,678 166,322 7,514,226 63,544 5,451,421 10,753,992 86,700,251 29,043,487 I-4017 0 7,033,000 28,330,785 6,387,564 0 0 251,898,999 65,388,453 I-4025 7,782,232 68,939 1,225,243 0 6,078,802 3,705,890 11,474,934 3,694,980 I-4030 1,406,766 25,999 14,037,511 328,914 0 0 48,415,484 17,604,366 I-4036 334,387 2,180,978 1,445,168 1,102,510 0 0 29,650,071 5,555,465 I-4403 299,520 10,655 127,374 0 0 0 2,251,782 742,669 I-4408 33,487,981 466,703 7,337,337 88,107 0 0 69,351,978 30,430,349 I-4412 272,831 2,706,896 600,237 475,057 0 0 117,541,911 30,119,889 I-4414 706,093 5,223,875 795,463 862,515 0 0 75,087,544 15,452,761 I-4415 25,923,279 377,579 1,628,247 0 0 0 38,515,910 18,202,565 I-4741 2,328,395 361,475 131,589 0 0 0 9,614,195 3,034,146

W-4439 650,078 899,575 213,060 50,948 0 0 18,369,365 4,458,233 I-3602 5,040,385 11,570,519 20,202 510,724 0 0 107,928,745 19,694,745 Total 268,713,019 124,757,540 982,182,927 35,211,998 54,439,624 39,042,541 2,395,810,415 841,861,136

Table A-12. Number of Hours by Work Status, Lane Closure Status, and Time Period for Ohio Projects.


Lane Closure NO Lane Closure Lane Closure NO Lane Closure Project Day Night Day Night Day Night Day Night 32-02 50 2,369 6,338 153 0 0 7,041 8,842 119-01 294 611 3,827 5 0 0 6,266 8,174 172-02 139 957 5,430 123 0 0 6,508 9,140 271-04 51 261 83 185 0 0 1,180 665 420-02 248 1,752 5,035 131 0 0 4,610 6,488 430-03 142 593 335 41 0 0 3,437 2,678 454-01 152 772 3,036 129 0 0 8,005 8,571 487-03 123 386 4 22 0 0 926 484 534-02 216 599 4,212 330 0 0 3,919 6,133 5013-02 126 154 3 7 0 0 2,498 2,061

Total 1,539 8,451 28,302 1,124 0 0 44,390 53,235

Table A-13. VMT by Work Status, Lane Closure Status, and Time Period for Ohio Projects.


Lane Closure NO Lane Closure Lane

Closure NO Lane Closure Project Day Night Day Night Day Night Day Night 32-02 99,174 1,564,806 13,069,909 112,762 0 0 14,847,129 6,418,893 119-01 3,479,715 2,648,164 45,440,278 29,782 0 0 84,711,594 35,940,515 172-02 2,082,896 4,931,609 82,634,784 1,005,811 0 0 97,893,645 46,835,793 271-04 740,059 1,400,233 1,162,752 954,834 0 0 18,031,845 3,405,890 420-02 10,381,982 25,958,704 213,253,695 2,409,365 0 0 198,488,952 93,622,575 430-03 1,160,460 1,762,597 2,914,212 125,975 0 0 29,595,384 7,841,804 454-01 1,428,142 2,662,943 28,546,199 584,769 0 0 77,596,804 27,821,386 487-03 2,621,189 2,919,737 85,846 168,167 0 0 20,860,209 3,722,841 534-02 1,660,009 1,610,156 32,918,344 982,986 0 0 32,252,288 16,720,173 5013-02 2,195,197 950,262 37,361 49,881 0 0 43,979,843 12,354,872

Total 25,848,823 46,409,211 420,063,378 6,424,333 0 0 618,257,692 254,684,741

Table A-14. Number of Hours by Work Status, Lane Closure Status, and Time Period for Washington Projects.


Lane Closure NO Lane Closure Lane Closure NO Lane Closure Project Day Night Day Night Day Night Day Night

5620 348 259 3,861 20 182 324 2,422 5,162 5694 48 1,741 3,089 584 0 0 12,931 11,271 5712 163 2,129 324 2 0 0 5,415 2,864 5822 108 6 0 0 0 0 958 896 5906 369 21 12 0 0 0 2,492 2,410 5951 512 16 0 0 0 0 970 1,238 5970 7 794 865 30 0 0 8,358 6,986 6061 1,007 135 151 4 60 230 1,083 1,578 6084 0 1,034 54 96 0 0 3,833 2,159 6116 26 362 37 0 0 0 1,328 815 6125 36 518 95 9 0 0 1,923 1,211 6317 31 1,567 3,995 398 151 56 6,692 7,176 6621 4 330 992 9 0 0 1,162 1,487 6630 2,439 247 233 2 1,007 2,211 2,068 2,403 6637 1,527 82 100 2 83 345 2,672 3,278 6718 254 26 69 2 0 0 990 1,083 6719 201 19 7 0 0 0 312 421 6790 217 10 5 0 68 191 283 283 Total 7,296 9,295 13,889 1,156 1,550 3,356 55,889 52,721

Table A-15. VMT by Work Status, Lane Closure Status, and Time Period for Washington Projects.


Lane Closure NO Lane Closure Lane Closure NO Lane Closure Project Day Night Day Night Day Night Day Night

5620 4,475,281 998,811 49,853,585 130,823 2,379,135 1,425,218 31,618,136 22,343,132 5694 1,160,273 12,735,973 73,647,397 4,714,523 0 0 313,789,732 92,090,036 5712 12,468,606 53,110,962 30,708,093 78,264 0 0 515,425,537 104,273,4475822 79,440 2,776 0 0 0 0 708,248 219,263 5906 4,948,857 147,966 165,485 0 0 0 33,507,421 10,739,004 5951 36,599,201 532,284 0 0 0 0 71,131,288 29,835,533 5970 92,357 2,811,513 11,400,801 120,251 0 0 114,311,387 32,530,895 6061 5,019,338 281,953 774,175 9,992 311,355 383,124 5,527,575 2,603,965 6084 0 5,564,513 1,018,788 495,800 0 0 75,136,400 15,406,839 6116 293,462 1,320,389 476,709 0 0 0 17,291,697 3,771,015 6125 489,004 2,822,642 1,807,811 64,761 0 0 36,689,585 8,102,354 6317 622,206 9,951,524 93,033,623 2,413,522 3,472,282 624,386 150,267,334 56,748,111 6621 220,004 6,413,402 58,885,512 178,616 0 0 65,471,615 28,524,647 6630 20,883,680 882,942 1,877,716 4,243 8,757,565 6,327,177 16,817,565 6,411,073 6637 4,531,561 101,959 293,127 1,766 251,648 345,247 7,985,942 3,233,114 6718 472,171 31,257 130,951 2,906 0 0 1,848,324 656,868 6719 194,736 10,271 6,710 0 0 0 318,254 136,226 6790 2,380,823 65,456 63,683 0 725,807 659,437 3,101,970 1,043,182 Total 94,930,999 97,786,593 324,144,166 8,215,468 15,897,791 9,764,590 1,460,948,010 418,668,705

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CRASH DATA EXTRACTION AND REDUCTION Crash Data Extraction In HSIS, the crash data and the roadway inventory data can be extracted for a section if the beginning and ending milepost are known. Thus, after each work zone data collection trip researchers prepared a detailed list of the mile point limits for each project and comparison segment, as well as specific years and dates of interest. The research team provided this list to the HSIS database administrators, who then extracted all the crash data. Initially, the research team only requested three years of before data. However, in order to develop more accurate annual factors and SPFs additional years of non-work zone freeway segment crashes were required. The specific years for which crash data were obtained are shown in Table A-7. Researchers utilized the comparison segment crash data from all of the years specified to develop the annual factors and SPFs. The before-work time period for each project was defined as the time from the first date for which crash data were available (e.g., 1/1/1993) to the day before the project start date. The during-work time period for each project is shown in Table A-2 through Table A-5. Researchers received the crash and roadway inventory data over an extended period of time due to the staggered nature of the requests and the following issues that arose during the data extraction process. The initial California crash dataset received from HSIS had several roadway segments

missing and did not include data from 2003 and 2004. At a later date, researchers were able to resolve the missing segment issues and obtain crash data for 2003 and 2004. However, the 2003 and 2004 California roadway inventory data were not available. As discussed previously, California DOT personnel matched the 2003 and 2004 crash data to the 2005 roadway inventory data. Thus, researchers had to assume that the 2003 and 2004 roadway inventory data were similar to the 2005 roadway inventory data.

When extracting Ohio data from HSIS database, the HSIS database administrators identified several problems with the 2000 and 2001 crash and roadway inventory data. Apparently, Ohio made some changes to their crash reporting procedures at that time, and not all of the reported crashes were included in the original dataset sent to HSIS (the number of crashes in these two years was about 20,000 lower than the other years). Once the HSIS database administrators received the corrected data files from Ohio, they had to reformat the data and perform other data manipulations in order to allow it to be used for HSIS purposes.

As discussed previously, in the corrected Ohio crash data sent to HSIS the 2000 and 2001 crash data were matched to the 2002 roadway inventory data. Thus, researchers had to assume that the 2000 and 2001 roadway inventory data were similar to the 2002 roadway inventory data.

The initial Washington crash dataset received from HSIS included a larger number of crashes than expected, especially since it did not include all of the desired time periods. Upon further review, researchers and HSIS database administrators discovered that “citizen” reported crashes had been included. In the final crash dataset, HSIS database administrators removed all of the “citizen” reported crashes and included all the desired time periods.

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As discussed previously, the 1997 and 1998 crash data for Washington were not available. In addition, the 1997 to 2001 Washington roadway inventory data were not available. Researchers assumed that the 1999 and 2000 roadway inventory data were similar to the 2001 roadway inventory data.

Crash Data Reduction As the crash data were received, researchers separated it into project limit and comparison segment subsets and before and during project time period subsets. Ultimately, researchers used the comparison segment crash data and the before-work period project limit crash data to develop estimates of crash frequencies that would be expected within the project limits of each work zone for the during-work time period, had the work not occurred at that location. Obviously, this required that the comparison segments and the project limits in the before-work time period themselves not contain work zones over the analysis horizon being considered. To ensure that this was the case, researchers first reviewed the duration and limits of the 64 projects for which field data were collected. In some cases, two or more projects occurring at separate times (e.g., years apart) had overlapping project limits. Using these data, researchers removed the known work zone data from the before-work period crash data. In addition, researchers reviewed the crashes recorded within each of these segments to determine how many of them were coded as being work zone-related. One would expect an occasional crash in each segment to be coded as work-zone related, due to routine maintenance work, utility work, etc. However, high concentrations of work zone crashes were considered indicative of a non-normal situation and appropriate adjustments in the project limits or comparison segments were made as necessary. In addition, for these segments researchers reviewed the number of lanes coded each year in the roadway inventory data. If the number of lanes increased between two years (e.g., two lanes in 1999 and four lanes in 2000), researchers assumed that major construction occurred during those two years. Thus, the crash data associated with the suspect time period and mile points were excluded from further analysis. Within the during-work period crash data subset, for each project researchers matched the dates and times of each crash to the project diary information in order to assign the crash to one of eight time period categories: Daytime when work activity was occurring and there was at least one lane closed, Daytime when work activity was occurring and there were no lane closures, Nighttime when work activity was occurring and there was at least one lane closed, Nighttime when work activity was occurring and there were no lane closures, Daytime when the work zone was inactive and there was at least one lane closed, Daytime when the work zone was inactive and there were no lane closures, Nighttime when the work zone was inactive and there was at least one lane closed, and Nighttime when the work zone was inactive and there were no lane closures.

The segregated datasets (comparison segments, before-work period, and during-work period) were then organized so that the more sophisticated statistical modeling efforts

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planned for these data could be performed. More specifically, for each year within a dataset the number of total crashes, injury and fatality crashes, rear-end crashes, sideswipe crashes, and fixed object crashes were calculated for each time period of interest for each roadway segment. For some states, crashes involving alcohol were also computed. Table A-16 through Table A-19 contain the crash data variables and codes used for each state.

Table A-16. California Accident Subfile Variables.

Crash Category Accident Subfile Variable a Accident Subfile Codes

Injury & fatality Collision severity (SEVERITY)

1 – Fatal 2 – Severe 3 – Other visible injury 4 – Compliant of pain

PDO Collision severity (SEVERITY) 5 – Property damage only Rear-end Type of collision (ACCTYPE) C – Rear end Sideswipe Type of collision (ACCTYPE) B – Sideswipe Fixed object Type of collision (ACCTYPE) E – Hit object Alcohol Primary collision factor (CAUSE1) 1 – Alcohol

a Explanatory name (Statistical Analysis Software [SAS] variable name) PDO – Property Damage Only

Table A-17. North Carolina Accident Subfile Variables. Crash Category Accident Subfile Variable a Accident Subfile Codes

Injury & fatality Worst Injury in Accident (SEVERITY)

1 – Fatal 2 – Class A injury 3 – Class B injury 4 – Class C injury

PDO Worst Injury in Accident (SEVERITY) 5 – No injury

Rear-end First harmful event (ACCTYPE) 21 – Rear end, slow or stop 22 – rear end, turn

Sideswipe First harmful event (ACCTYPE) 28 – Sideswipe, same direction b 29 – Sideswipe, opposite direction b 36 - Sideswipe

Fixed object First harmful event (ACCTYPE) 19 – Fixed object Alcohol Contributing factor #1 (CONTRIB1) c 30 – Alcohol use a Explanatory name (Statistical Analysis Software [SAS] variable name) b Categories added from 2000 onwards c Variable located in vehicle subfile PDO – Property Damage Only

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Table A-18. Ohio Accident Subfile Variables.


Injury & fatality Crash severity (SEVERITY)

1 – Fatal 2 – A injury 3 – B injury 4 – C injury

PDO Crash severity (SEVERITY) 5 – Property damage Rear-end Type of crash (ACCTYPE) 02 – Rear-end

Sideswipe Type of crash (ACCTYPE) 04 – Sideswipe-meeting 05 – Sideswipe-passing

Fixed object Type of crash (ACCTYPE) 13 – Fixed-object a Explanatory name (Statistical Analysis Software [SAS] variable name) PDO – Property Damage Only

Table A-19. Washington Accident Subfile Variables.


Injury & fatality Most severe injury (SEVERITY)

2 – Dead at scene 3 – Dead on arrival 4 – Died at hospital 5 – Disabling injury 6 – Non-disabling injury 7 – Possible injury 8 – Non-traffic injury 9 – Non-traffic fatality

PDO Most severe injury (SEVERITY) 1 – No injury

Rear-end Accident 1 type (COLTYPE1) 13 – Both moving – rear end b 14 – One stopped – rear end b

Sideswipe Accident 1 type (COLTYPE1) 11 – Both moving – sideswipe b 12 – One stopped – sideswipe b

Fixed object Accident 1 type (COLTYPE1) 50 – Struck fixed object

Alcohol Contributing circumstance #1 (CONTRIB1DRV) c 01 – Under the Influence of Alcohol

a Explanatory name (Statistical Analysis Software [SAS] variable name) b From same direction – both going straight c Variable located in vehicle subfile PDO – Property Damage Only

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SAFETY PERFORMANCE FUNCTION DEVELOPMENT Safety performance functions were developed from the reference groups using data from California, North Carolina, Ohio, and Washington, as part of the empirical Bayes (EB) procedure that was used to estimate the effect of work zones. In this evaluation, the SPFs were negative binomial (NB) regression models (consistent with the state of the art in the safety field) developed with crash frequency as the dependent variable and site characteristics as independent variables. The analysis focused on total crashes, total injury and fatal crashes, and total property damage only (PDO) crashes. The model form was log-linear. With this model form, the expected crash frequency is related to the independent variables as follows:

)....exp(* 22110 nn XXXLY ββββ +++= (1) where: Y is the expected frequency of crashes per year; L is the length of the section;

X1 through Xn are independent variables (e.g., traffic volume shoulder width, etc.); and 0β through nβ are coefficients that need to be estimated. In a negative binomial model, the variance is related to the mean as follows:

2))(()()( iii yEkyEyVar += (2) where: )( iyVar is the variance, )( iyE is the mean, and k is the dispersion parameter. Many previous studies have assumed k to be a constant value while estimating the NB models. Hauer (1) argued that assuming k as a constant provides too much weight to shorter sections and not enough weight to longer sections. He advocated estimating k on a per-mile basis, and this was the approach that was followed in this study. For the models that were estimated, the CURE procedure (2) was used to determine if the functional form of the independent variables was reasonable. Models were estimated using PROC GLIMMIX in Statistical Analysis Software (SAS).

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Independent Variables The following independent variables were included in the models: traffic volume, right and left shoulder width, influence of a ramp or interchange, number of lanes, and area type.

For models developed for daytime crashes (from 6:00 am to 6:59 pm), the daytime traffic volume (Day Vol) was used. For models developed for nighttime crashes (from 7:00 pm to 5:59 am), the nighttime traffic volume (Night Vol) was as used. To estimate the daytime and nighttime traffic volumes, information from permanent traffic counters on freeways was utilized. Table A-20 shows the factors that were used to estimate the day and night volumes in the four states based on the AADT.

Table A-20. Factors to Determine Day and Night Volumes from AADT.

California North Carolina Ohio Washington Day Volume 0.7690*AADT 0.7805*AADT 0.7644*AADT 0.7801*AADT Night Volume 0.2310*AADT 0.2195*AADT 0.2356*AADT 0.2199*AADT

It is interesting to note that the proportion of traffic volume by day and night did not differ very much among the four states. As previously discussed, in Washington, roadway inventory data were not available from 1997 to 2001. However, researchers were able to obtain AADT data. Hence, for Washington, traffic volume was the only independent variable in the SPFs. The right and left shoulder widths were the average of the values from both sides of the road. The influence of a ramp or interchange was introduced as an indicator variable (i.e., one for within the influence or zero for outside the influence). For California and Ohio, HSIS provided information on the location of ramps. If a roadway section was within 0.3 miles of an entry/exit ramp, it was considered within the influence of the ramp. For North Carolina, HSIS does not have information regarding the location of ramps. However, the research team was able to obtain information on the location of interchanges using a Traffic Engineering Accident Analysis System (TEAAS) database. In North Carolina, sections within 0.5 miles of an interchange were considered within the influence of an interchange. For California, North Carolina, and California, separate models were developed for four and five lane urban roads and four and five lane rural roads. In addition, models were developed for roads with six or more lanes; however, the development of these models differed by state. Initially, it was desired to separate out roads with six or seven lanes and roads with eight or more lanes. In California, large sample sizes allowed researchers to develop two sets of models: one for

roads with six or seven lanes and another for roads with eight or more lanes. Even though

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researchers performed the data checks described earlier, in HSIS, some segments in the reference group were classified as non-freeway. Hence, area type (rural or urban) and roadway class (freeway or non-freeway) were included as indicator variables. However, the non-freeway segments were not used in further analysis.

In North Carolina, one set of models were developed for roads with six or more lanes due to small sample sizes for roads with eight or more lanes. In these models, area type (rural or urban) was included as an indicator variable. Number of lanes was not included as an indicator variable since it was not found to be significant.

Similar to North Carolina, in Ohio, one set of models were developed for roads with six or more lanes. In these models, area type (rural or urban) and number of lanes (6 or 7 lanes versus 8 or more lanes) were included as indicator variables.

Safety Performance Functions Table A-21 through Table A-31 show the SPFs that were estimated. Parameter estimates along with standard errors are shown. The tables also show the dispersion parameter (k), two goodness of fit measures (Freeman Tukey R-square and Pseudo R-square), and some summary statistics for each model. Unlike linear regression, there is no universally accepted goodness of fit measures for NB models. The Freeman Tukey R-square (R-square (FT)) is a goodness of fit measure proposed by Fridstrom et al. (3) and usually tends to be higher for datasets with a large number of crashes. The other goodness of fit measure is the Pseudo R-square (R-square (Pseudo)) based on Miaou (4) and is preferred by some researchers for datasets with low number of crashes. Readers can use the parameter estimates in the table to calculate the average expected number of crashes per year per mile for a particular roadway type. As an example, to calculate the average expected number of total crashes during the daytime for urban 4 and 5 lane freeways within the influence of interchanges in North Carolina, the parameter estimates in Table A-25 can be used to give the following equation: Y = exp{1.2379 + 1.4408*ln(Day Vol/10000) + 0.5209 – 0.06523*(Right Shoulder Width)} Where, Y is the average expected number of total crashes per mile per year. This equation can also be written as follows:

widthshoulderrighteDayVolY __*06523.04408.1

10000805.5 −⎟

⎠⎞

⎜⎝⎛=

In this equation, 5.805 was obtained by 5209.02379.1 +e . To obtain the number of crashes for sections outside the influence of interchanges, 5.805 should be replaced by 3.448 (i.e., 2379.1e ). The signs for the parameter estimates were as expected in the SPFs. For example, the coefficient for the traffic volume variables were positive indicating that an increase in traffic volumes leads to an increase in crashes. Similarly, the coefficient for the indicator variable that represents

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whether a section is within the influence of an interchange/ramp was positive, confirming that more crashes occur near ramps and interchanges. Table A-21 through Table A-31 also show the annual factors that were developed for each roadway type and time period. The annual factor for a particular year was equal to the number of observed crashes divided by the number of predicted crashes based on the SPFs. Annual factors are used to account for the effect of changes in factors such as weather, crash reporting practices, and demography, over time. The annual factors for a particular roadway type were based on the SPFs that were estimated for total crashes. Seasonal Factors Since construction projects do not necessarily start or end at the beginning or end of the year, there is a need to account for the seasonal variation in crash patterns. To account for this issue, data on total number of crashes on freeways were assembled by month for four years during the study period for all the four states (HSIS provided this data). For each month, the expected number of crashes was calculated by dividing the total number of crashes by the number of days in a year and then multiplying by the number of days in that month. The ratio of observed and expected crashes was used to develop a seasonal calibration factor. Table A-32 shows the observed and expected number of crashes in each month and the ratio of the two.

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Table A-21. SPFs for Urban 4 and 5 Lane Multilane Divided Roads (Mostly Freeways) in

California.

Urban 4 and 5 Lanes Day Urban 4 and 5 Lanes Night

All Injury &

Fatal PDO All Injury &

Fatal PDO

Variables/Statistics Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept -0.3445

(0.07731) -1.0890

(0.1070) -0.9476

(0.08844) 1.0370

(0.04039) 0.3875

(0.09729) 0.4797

(0.05072)

ln(Vol/10000) 1.7644

(0.04285) 1.6077

(0.05853) 1.8524

(0.04832) 1.1174

(0.05484) 0.9990

(0.07869) 1.1688

(0.06764)

Vol/10000

Within Influence of Ramp 0.7490

(0.03097) 0.7069

(0.04081) 0.7731

(0.03417) 0.4636

(0.04050) 0.4012

(0.05844) 0.5218

(0.04952)

Outside Influence of Ramp

Right Shoulder Width -0.02536

(0.01043)

Left Shoulder Width

Freeway

Non-Freeway Dispersion parameter 0.0801 0.0929 0.0819 0.0676 0.0538 0.0775 R-square (FT) 0.606 0.475 0.573 0.487 0.346 0.379 R-square (Pseudo) 0.637 0.595 0.665 0.513 0.513 0.528 Crashes 14118 4824 9294 3724 1449 2275 Mile-Years 587.1 587.1 Crashes/Mile-Year 24.05 8.22 15.83 6.34 2.47 3.87 Vol (avg) 55,525 16,683 Vol (min) 15,764 4,736 Vol (max) 223,766 67,234 Observations 2953 2953 Annual Factor-1995 1.083 0.994 Annual Factor-1996 1.068 1.023 Annual Factor-1997 1.130 1.067 Annual Factor-1998 1.089 1.083 Annual Factor-1999 1.049 0.929 Annual Factor-2000 1.018 1.060 Annual Factor-2001 0.953 1.019 Annual Factor-2002 0.922 0.971 Annual Factor-2003 0.806 0.938 Annual Factor-2004 0.820 0.873

30

Table A-22. SPFs for Rural 4 and 5 Lane Multilane Divided Roads (Mostly Freeways) in

California.

Rural 4 and 5 Lanes Day Rural 4 and 5 Lanes Night

All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 1.0189

(0.09407) 0.2479

(0.1237) 0.3752

(0.1120) 0.9460

0.03127 0.1049

(0.04558) 0.3809

(0.03931)

ln(Vol/10000) 0.9562

(0.04765) 0.8836

(0.06309) 1.0083

(0.05667) 0.8729

(0.05552) 0.8979

(0.08133) 0.8453

(0.06905)

Vol/10000


(0.06801) 0.3951

(0.08852) 0.6340

(0.07748) 0.6826

(0.07423) 0.4680

(0.1132) 0.8198

(0.08718)


Right Shoulder Width

Left Shoulder Width

Freeway -0.3383

(0.06647) -0.3993

(0.08212) -0.2590

(0.07712)

Non-Freeway Dispersion parameter 0.1716 0.1687 0.1918 0.1048 0.1472 0.1000 R-square (FT) 0.571 0.437 0.499 0.443 0.294 0.338 R-square (Pseudo) 0.479 0.517 0.496 0.539 0.492 0.556 Crashes 4362 1715 2647 1699 707 992 Mile-Years 608.2 608.2 Crashes/Mile-Year 7.17 2.82 4.35 2.79 1.16 1.63 Vol (avg) 33,024 9,923 Vol (min) 6,997 2,103 Vol (max) 79,202 23,798 Observations 1897 1897 Annual Factor-1995 0.909 1.033 Annual Factor-1996 0.973 0.885 Annual Factor-1997 0.903 0.908 Annual Factor-1998 1.075 1.138 Annual Factor-1999 1.036 1.050 Annual Factor-2000 0.938 1.028 Annual Factor-2001 1.174 1.032 Annual Factor-2002 0.902 0.917 Annual Factor-2003 0.912 0.996 Annual Factor-2004 1.088 0.985

31

Table A-23. SPFs for 6 and 7 Lane Multilane Divided Roads (Mostly Freeways) in

California.

6 and 7 Lanes Day 6 and 7 Lanes Night

All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 1.8821

(0.08932) 0.9792

(0.1165) 1.4261

(0.09557) 0.8372

(0.07249) -0.1119

(0.1002) 0.3689

(0.08517)

ln(Vol/10000)

Vol/10000 0.1100

(0.006459) 0.09792

(0.008172) 0.1109

(0.006885) 0.2898

(0.02793) 0.2666

(0.03634) 0.2907

(0.03216)

Within Ramp influence (1 or 0)?

0.5451 (0.03048)

0.4533 (0.04034)

0.5853 (0.03298)

0.5171 (0.04218)

0.4531 (0.05809)

0.5619 (0.04909)


(0.006656) -0.04272

(0.008559) -0.02792

(0.007077)

Left Shoulder Width

Urban Freeway 0.7064

(0.04546) 0.7047

(0.06363) 0.7317

(0.05031) 0.4710

(0.06079) 0.5056

(0.08725) 0.4680

(0.07201)

Urban Non-Freeway 1.4267

(0.1086) 1.5473

(0.1360) 1.2905

(0.1178) 1.4105

(0.1343) 1.5457

(0.1695) 1.2616

(0.1584) Rural Freeway and Non-Freeway Dispersion parameter 0.0676 0.0783 0.0707 0.0874 0.0881 0.0991 R-square (FT) 0.573 0.468 0.533 0.459 0.334 0.376 R-square (Pseudo) 0.529 0.505 0.543 0.421 0.427 0.424 Crashes 18324 5619 12705 5403 1938 3465 Mile-Years 508.9 508.9 Crashes/Mile-Year 36.00 11.04 24.96 10.62 3.81 6.81 Vol (avg) 91,932 27,622 Vol (min) 19,059 5,727 Vol (max) 223,766 67,234 Observations 2984 2984 Annual Factor-1995 1.070 1.073 Annual Factor-1996 1.067 1.004 Annual Factor-1997 1.098 1.061 Annual Factor-1998 1.086 1.122 Annual Factor-1999 0.992 0.927 Annual Factor-2000 1.002 1.113 Annual Factor-2001 0.922 0.971 Annual Factor-2002 0.912 0.898 Annual Factor-2003 0.889 0.904 Annual Factor-2004 0.911 0.920

32

Table A-24. SPFs for Freeways with 8 or More Lanes in California.

8 + Lanes Day 8 + Lane Night

All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept -1.7484

(0.1059) -2.5467

(0.1431) -2.3327

(0.1168) 0.1038

(0.08386) -0.5857

(0.09043) -0.2507

(0.1042)

ln(Vol/10000) 1.9662

(0.04257) 1.8245

(0.05545) 2.0168

(0.04641)

Vol/10000 0.4913

(0.01556) 0.5185

(0.02026) 0.4786

(0.01756)

Within Ramp influence (1 or 0)?

0.5406 (0.02126)

0.5055 (0.02792)

0.5578 (0.02323)

0.5402 (0.03098)

0.4301 (0.04223)

0.6022 (0.03596)


Left Shoulder Width -0.01202

(0.002518) -0.02005

(0.003418) -0.02684

(0.005360) -0.01864

(0.004306)

Urban Freeway 0.4079

(0.05250) 0.4619

(0.07760) 0.4028

(0.05900) 0.1806

(0.07571) 0.2231

(0.09182)

Urban Non-Freeway Rural Freeway and Non-Freeway Dispersion parameter 0.0575 0.0628 0.0616 0.0809 0.0853 0.0855 R-square (FT) 0.643 0.538 0.607 0.470 0.345 0.399 R-square (Pseudo) 0.526 0.514 0.535 0.479 0.499 0.494 Crashes 39297 11881 27416 10193 3694 6499 Mile-Years 801.5 801.5 Crashes/Mile-Year 49.03 14.82 34.21 12.72 4.61 8.11 Vol (avg) 121,734 36,577 Vol (min) 21,894 6,578 Vol (max) 232,224 69,776 Observations 5129 5129 Annual Factor-1995 1.203 1.119 Annual Factor-1996 1.152 1.058 Annual Factor-1997 1.089 1.039 Annual Factor-1998 1.153 1.121 Annual Factor-1999 1.044 1.098 Annual Factor-2000 1.033 1.136 Annual Factor-2001 0.991 1.070 Annual Factor-2002 0.932 0.906 Annual Factor-2003 0.815 0.804 Annual Factor-2004 0.801 0.790

33

Table A-25. SPFs for Urban 4 and 5 Lane Freeways in North Carolina.


All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 1.2379

(0.1990) 0.2297

(0.2400) 0.8240

(0.2250) 1.6246

(0.1778) 0.2347

(0.05494) 1.2715

(0.2097)

ln(Vol/10000) 1.4408

(0.08289) 1.3472

(0.09590) 1.4876

(0.09318) 0.9750

(0.09886) 0.8289

(0.1279) 1.0560

(0.1139)

Vol/10000

Within Influence of Interchange 0.5209

(0.08734) 0.3253

(0.1016) 0.5974

(0.09664) 0.5224

(0.1019) 0.5203

(0.1292) 0.5142

(0.1168)

Outside Influence of Interchange


(0.01193) -0.05779 (0.01471)

-0.07202 (0.01371)

-0.03032 (0.01473)

-0.03764 (0.01747)

Left Shoulder Width Dispersion parameter 0.3062 0.3060 0.3624 0.3369 0.3611 0.4024 R-square (FT) 0.593 0.546 0.553 0.546 0.401 0.495 R-square (Pseudo) 0.425 0.433 0.421 0.301 0.283 0.305 Crashes 7267 2426 4841 2252 770 1482 Mile-Years 488.2 488.2 Crashes/Mile-Year 14.88 4.97 9.92 4.61 1.58 3.04 Vol (avg) 42,848 12,050 Vol (min) 22,010 6,190 Vol (max) 87,416 24,584 Observations 653 653 Annual Factor-1995 0.918 0.722 Annual Factor-1996 0.847 0.978 Annual Factor-1997 0.836 0.665 Annual Factor-1998 1.007 0.910 Annual Factor-1999 0.960 0.822 Annual Factor-2000 0.998 0.969 Annual Factor-2001 0.895 0.850 Annual Factor-2002 0.903 1.197 Annual Factor-2003 1.136 1.258 Annual Factor-2004 1.239 1.288

34

Table A-26. SPFs for Rural 4 and 5 Lane Freeways in North Carolina.


All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 0.4860

(0.0504) -0.7834

(0.07041) 0.1563

(0.05765) 0.5191

(0.2080) 0.07052

(0.06560) -0.2558

(0.07068)

ln(Vol/10000) 0.7397

(0.1335) 0.9940

(0.1844) 0.5446

(0.1555) 0.3602

(0.1545) 1.0261

(0.06727)

Vol/10000 0.2164

(0.04786) 0.1254

(0.06251) 0.2866

(0.05541) 0.6574

(0.1981) 1.0331

(0.06545)

Within Influence of Interchange

0.7014 (0.1038)

0.4956 (0.1336)

0.7851 (0.1169)

0.7016 (0.1124)

0.4225 (0.1696)

0.8217 (0.1293)



Left Shoulder Width -0.03103

(0.003062) -0.01007

(0.004202) -0.04151

(0.003517) -0.02364

(0.003620) -0.01545

(0.005452) -0.02824

(0.004318) Dispersion parameter 0.2689 0.2595 0.3243 0.2105 0.1352 0.2741 R-square (FT) 0.746 0.650 0.681 0.684 0.520 0.594 R-square (Pseudo) 0.711 0.729 0.700 0.622 0.774 0.571 Crashes 10221 3471 6750 4427 1472 2955 Mile-Years 2283.6 2283.6 Crashes/Mile-Year 4.48 1.52 2.96 1.94 0.64 1.29 Vol (avg) 25,036 7,041 Vol (min) 7,883 2,217 Vol (max) 74,148 20,853 Observations 1170 1170 Annual Factor-1995 0.888 0.743 Annual Factor-1996 0.986 1.009 Annual Factor-1997 0.838 0.897 Annual Factor-1998 0.933 0.919 Annual Factor-1999 0.853 0.852 Annual Factor-2000 0.948 0.967 Annual Factor-2001 0.921 0.972 Annual Factor-2002 1.170 1.167 Annual Factor-2003 1.308 1.277 Annual Factor-2004 1.027 1.074

35

Table A-27. SPFs for Freeways with 6 or More Lanes in North Carolina.

6+ Lanes Day 6+ Lanes Night

All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 0.7854

(0.06296) -0.2136

(0.07960) 0.3271

(0.07104) 0.3598

(0.06703) -0.6661

(0.09720) 0.1504

(0.1583) ln(Vol/10000)

Vol/10000 0.2610

(0.008637) 0.2465

(0.01024) 0.2677

(0.009600) 0.6559

(0.03331) 0.6527

(0.04711) 0.6537

(0.03825)

Within Influence of Interchange

0.4881 (0.04548)

0.4542 (0.05245)

0.5023 (0.04965)

0.5279 (0.04937)

0.5267 (0.06847)

0.5286 (0.05591)


Urban 0.1750

(0.05130) 0.1970

(0.06522) 0.1722

(0.05763) Rural Dispersion parameter 0.1915 0.1841 0.2144 0.1551 0.1923 0.1660 R-square (FT) 0.680 0.612 0.637 0.582 0.418 0.512 R-square (Pseudo) 0.550 0.588 0.545 0.490 0.488 0.490 Crashes 15238 5095 10143 4850 1730 3120 Mile-Years 857.3 857.3 Crashes/Mile-Year 17.77 5.94 11.83 5.66 2.02 3.64 Vol (avg) 63,418 17,835 Vol (min) 21,854 6,146 Vol (max) 123,319 34,681 Observations 1489 1489 Annual Factor-1995 0.921 0.753 Annual Factor-1996 0.992 1.008 Annual Factor-1997 0.806 0.809 Annual Factor-1998 0.948 0.994 Annual Factor-1999 0.900 0.900 Annual Factor-2000 0.911 1.159 Annual Factor-2001 0.925 0.933 Annual Factor-2002 1.139 1.040 Annual Factor-2003 1.163 1.190 Annual Factor-2004 1.058 0.992

36

Table A-28. SPFs for Urban 4 and 5 Lane Freeways in Ohio.


All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 0.8510

(0.07490) -0.4479

(0.1073) 0.5620

(0.08077) 1.5434

(0.03042) 0.2152

(0.04622) 1.2398

(0.03275)

ln(Vol/10000) 1.3687

(0.05494) 1.3374

(0.07603) 1.3607

(0.05883) 0.9441

(0.06310) 1.0425

(0.09538) 0.8858

(0.06743)

Vol/10000


(0.08141) 0.9737

(0.09628) 0.8371

(0.08575) 0.6963

(0.08906) 0.7871

(0.1133) 0.6467

(0.09395)

Outside Influence of Ramp Dispersion parameter 0.2655 0.2997 0.2840 0.2586 0.2534 0.2581 R-square (FT) 0.538 0.440 0.529 0.559 0.411 0.543 R-square (Pseudo) 0.505 0.539 0.499 0.422 0.524 0.403 Crashes 9944 2645 7299 3431 952 2479 Mile-Years 585.1 585.1 Crashes/Mile-Year 17.00 4.52 12.47 5.86 1.63 4.24 Vol (avg) 37,358 11,514 Vol (min) 9,555 2,945 Vol (max) 95,389 29,401 Observations 1025 1025 Annual Factor-1997 1.079 1.068 Annual Factor-1998 0.993 0.853 Annual Factor-1999 0.944 1.032 Annual Factor-2000 0.805 0.879 Annual Factor-2001 0.849 0.794 Annual Factor-2002 0.954 1.015 Annual Factor-2003 1.149 1.100 Annual Factor-2004 0.933 1.163

37

Table A-29. SPFs for Rural 4 and 5 Lane Freeways in Ohio.


All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 0.9561

(0.08979) -0.6766

(0.1749) 0.6795

(0.09705) 1.4365

(0.04314) -1.1326

(0.2287) 1.2199

(0.04524)

ln(Vol/10000) 0.9084

(0.1068) 0.9041

(0.1151) 0.7882

(0.09945) 0.8286

(0.1060)

Vol/10000 0.4154

(0.07290) 0.9842

(0.3107)


(0.3997) 0.8248

(0.4154) 0.8587

(0.2995) 1.0539

(0.6020) 0.7913

(0.3014)

Outside Influence of Ramp Dispersion parameter 0.2948 0.3224 0.3046 0.0908 0.3690 0.0541 R-square (FT) 0.853 0.745 0.837 0.874 0.570 0.865 R-square (Pseudo) 0.285 0.311 0.716 0.542 0.238 0.691 Crashes 3778 922 2856 2189 452 1737 Mile-Years 693.7 693.7 Crashes/Mile-Year 5.45 1.33 4.12 3.16 0.65 2.50 Vol (avg) 22,521 6,941 Vol (min) 9,050 2,790 Vol (max) 37,685 11,615 Observations 363 363 Annual Factor-1997 1.070 1.096 Annual Factor-1998 0.844 1.021 Annual Factor-1999 1.117 1.046 Annual Factor-2000 0.915 0.902 Annual Factor-2001 0.837 1.023 Annual Factor-2002 1.001 0.829 Annual Factor-2003 1.114 1.017 Annual Factor-2004 1.110 1.101

38

Table A-30. SPFs for Freeways with 6 or More Lanes in Ohio.

6+ Lanes Day 6+ Lanes Night

All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept -0.8581

(0.2615) -2.5223

(0.3383) -1.1268

(0.2738) 1.3877

(0.07203) -0.2447

(0.3278) 1.1526

(0.07845)

ln(Vol/10000) 1.6454

(0.09101) 1.6601

(0.1040) 1.6386

(0.09509) 1.0666

(0.08823) 1.1856

(0.1267) 0.9004

(0.09605)

Vol/10000


(0.06836) 0.9411

(0.07499) 0.9741

(0.07116) 0.7821

(0.06720) 0.7595

(0.08283) 0.7921

(0.07222)


Urban 0.8235

(0.1828) 1.2717

(0.2814) 0.7167

(0.1918) 0.5803

(0.3075)

Rural

6 or 7 lanes 0.1814

(0.05124) 0.2853

(0.05383) -0.3101

(0.06583) 8+ lanes Dispersion parameter 0.2744 0.2888 0.2931 0.2253 0.2444 0.2453 R-square (FT) 0.475 0.450 0.451 0.536 0.443 0.493 R-square (Pseudo) 0.336 0.396 0.326 0.293 0.401 0.264 Crashes 24256 6484 17772 7567 2344 5223 Mile-Years 719.9 719.9 Crashes/Mile-Year 33.70 9.01 24.69 10.51 3.26 7.26 Vol (avg) 71,721 22,106 Vol (min) 37,601 11,589 Vol (max) 123,267 37,993 Observations 1392 1392 Annual Factor-1997 0.859 0.867 Annual Factor-1998 0.820 0.786 Annual Factor-1999 0.952 0.931 Annual Factor-2000 0.902 0.912 Annual Factor-2001 1.003 1.046 Annual Factor-2002 1.005 1.126 Annual Factor-2003 1.165 1.130 Annual Factor-2004 1.044 1.099

39

Table A-31. SPFs for Freeways in Washington.

Day Night

All Injury &


Fatal PDO


(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.) Estimate

(S.E.)

Intercept 0.9076

(0.02321) -0.09266

(0.03457) 0.4422

(0.02794) 0.8797

(0.03718) -0.04886

(0.04986) 0.3647

(0.04575)

ln(Vol/10000) 0.8909

(0.03142) 0.9709

(0.04192) 0.8571

(0.03601) 0.5677

(0.04140) 0.5865

(0.05822) 0.5411

(0.05129)

Vol/10000 0.05042

(0.005366) 0.04394

(0.006713) 0.05156

(0.005969) 0.2369

(0.02405) 0.2583

(0.03229) 0.2301

(0.02949) Dispersion parameter 0.0930 0.0994 0.0954 0.0861 0.0797 0.1053 R-square (FT) 0.551 0.473 0.503 0.456 0.346 0.362 R-square (Pseudo) 0.691 0.725 0.699 0.685 0.751 0.657 Crashes 42001 17173 24828 11898 5059 6839 Mile-Years 2313.0 2313.0 Crashes/Mile-Year 18.16 7.42 10.73 5.14 2.19 2.96 Vol (avg) 55,663 15,691 Vol (min) 7,203 2,030 Vol (max) 249,830 70,424 Observations 13409 13409 Annual Factor-1993 0.886 0.938 Annual Factor-1994 0.996 1.046 Annual Factor-1995 0.997 1.008 Annual Factor-1996 1.182 1.158 Annual Factor-1999 1.034 1.020 Annual Factor-2000 0.992 0.938 Annual Factor-2001 1.057 1.056 Annual Factor-2002 0.971 1.043 Annual Factor-2003 0.934 0.911 Annual Factor-2004 0.947 0.920

40

Table A-32. Observed and Expected Crashes by Month.

NORTH CAROLINA OHIO

MONTH Observed Crashes

Expected Crashes

Observed/Expected

Observed Crashes

Expected Crashes

Observed/ Expected

January 4952 4978.77 0.995 13194 11139.87 1.184 February 4197 4496.95 0.933 8352 10061.82 0.830 March 4223 4978.77 0.848 10292 11139.87 0.924 April 4334 4818.16 0.900 9157 10780.52 0.849 May 4751 4978.77 0.954 11368 11139.87 1.020 June 5180 4818.16 1.075 10736 10780.52 0.996 July 5156 4978.77 1.036 10046 11139.87 0.902 August 5105 4978.77 1.025 10281 11139.87 0.923 September 4750 4818.16 0.986 9490 10780.52 0.880 October 5482 4978.77 1.101 11851 11139.87 1.064 November 5484 4818.16 1.138 12828 10780.52 1.190 December 5007 4978.77 1.006 13568 11139.87 1.218 CALIFORNIA WASHINGTON January 41407 41144.81 1.006 5921 5292.42 1.119 February 38005 37163.06 1.023 4378 4780.25 0.916 March 42131 41144.81 1.024 4688 5292.42 0.886 April 35757 39817.56 0.898 4540 5121.70 0.886 May 36757 41144.81 0.893 4457 5292.42 0.842 June 37564 39817.56 0.943 4718 5121.70 0.921 July 39591 41144.81 0.962 4952 5292.42 0.936 August 42483 41144.81 1.033 5274 5292.42 0.997 September 40046 39817.56 1.006 4820 5121.70 0.941 October 42958 41144.81 1.044 5549 5292.42 1.048 November 43929 39817.56 1.103 6308 5121.70 1.232 December 43819 41144.81 1.065 6709 5292.42 1.268

41

REFERENCES 1. E. Hauer. Overdispersion in Modeling Accidents on Road Sections and in Empirical Bayes

Estimation, In Accident Analysis and Prevention, Vol. 33(6), pp. 799-808, 2001. 2. E. Hauer and J. Bamfo. Two Tools for Finding what Function Links the Dependent Variable

to the Explanatory Variables, In Proceedings of the ICTCT 1997 Conference, Lund, Sweden, 1997.

3. L. Fridstrom, J. Ifver, S. Ingebrigtsen, R. Kulmala, and L.K. Thomsen. Measuring the

Contribution of Randomness, Exposure, Weather, and Daylight to the Variation in Road Accident Counts, In Accident Analysis and Prevention, Vol. 27(1), pp. 1-20, 1995.

4. S.P. Miaou, Measuring the Goodness-of-fit of Accident Prediction Models. Report No.

FHWA-RD-96-040. Federal Highway Administration, U.S. Department of Transportation, Washington, DC, 1996.

43

APPENDIX B:

EB CRASH ANALYSIS RESULTS

51

Appendix C:

MMUCC Guideline Data Elements CRASH DATA ELEMENTS COLLECTED AT THE SCENE WZ Critical

(1) WZ Related (2)

C1. Crash Case Identifier C2. Crash Date and Time Y C3. Crash County Y C4. Crash City /Place Y C5. Crash Location Y C6. First Harmful Event Y Y C7. Location of First Harmful Event Y C8. Manner of Crash /Collision Impact Y C9. Source of Information C10. Date and Time Crash Reported to Law Enforcement Agency C11. Weather Conditions Y C12. Light Condition Y C13. Roadway Surface Condition Y Y C14. Contributing Circumstances, Environment Y C15. Contributing Circumstances, Road Y Y C16. Relation to Junction Y C17. Type of Intersection Y C18. School Bus-Related Y C19. Work Zone-Related (Construction/Maintenance/Utility) Y Y CRASH DATA ELEMENTS DERIVED FROM COLLECTED DATA

CD1. Crash Severity Y CD2. Number of Motor Vehicles Involved Y CD3. Number of Motorists CD4. Number of Non-Motorists CD5. Number of Non-Fatally Injured Persons Y CD6. Number of Fatalities Y CD7. Alcohol Involvement Y CD8. Drug Involvement Y CD9. Day of Week Y MOTOR VEHICLE DATA ELEMENTS COLLECTED AT THE SCENE

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V1. Motor Vehicle Identification Number (VIN) V2. Motor Vehicle Unit Type and Number Y Y V3. Motor Vehicle Registration State and Year V4. Motor Vehicle License Plate Number V5. Motor Vehicle Make V6. Motor Vehicle Model Year V7. Motor Vehicle Model V8. Motor Vehicle Body Type Category Y V9. Total Occupants in Motor Vehicle V10. Special Function of Motor Vehicle in Transport V11. Emergency Motor Vehicle Use V12. Motor Vehicle Authorized Speed Limit Y V13. Direction of Travel Before Crash V14. Trafficway Description Y V15. Total Lanes in Roadway Y V16. Roadway Alignment and Grade Y V17. Traffic Control Device Type Y Y V18. Motor Vehicle Maneuver/Action Y V19. Area(s) of Impact V20. Sequence of Events Y Y V21. Most Harmful Event for This Motor Vehicle Y Y V22. Underride/Override V23. Hit and Run Y V24. Extent of Damage Y V25. Contributing Circumstances, Motor Vehicle V26. Motor Carrier Identification V27. Gross Vehicle Weight Rating V28. Commercial Motor Vehicle Configuration V29. Commercial Cargo Body Type V30. Hazardous Materials Placard (Cargo Only PERSON DATA ELEMENTS COLLECTED AT THE SCENE

Level 1: All Persons Involved P1. Date of Birth P2. Sex P3. Person Type Y Y P4. Injury Status Y

Level 2: All Occupants P5. Occupant’s Motor Vehicle Unit Number

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P6. Seating Position P7. Occupant Protection System Use P8. Air Bag Deployed P9. Ejection

Level 3: All Drivers P10. Driver License Jurisdiction P11. Driver License Number and Class P12. Driver Name P13. Driver Actions at Time of Crash P14. Driver Condition at Time of Crash P15. Violation Codes P16. Driver Distracted By

Level 4: All Drivers and Non-Motorists P17. Law Enforcement Suspects Alcohol Use P18. Alcohol Test P19. Law Enforcement Suspects Drug Use P20. Drug Test

Level 5: Non-Motorists P21. Non-Motorist Number P22. Non-Motorist Action Prior to Crash Y Y P23. Non-Motorist Actions at Time of Crash Y Y P24. Non-Motorist Condition at Time of Crash Y P25. Non-Motorist Location at Time of Crash Y Y P26. Non-Motorist Safety Equipment Y Y P27. Unit Number of Motor Vehicle Striking Non-Motorist

Level 6: All Injured Persons P28. Transported to Medical Facility By PERSON DATA ELEMENT DERIVED FROM COLLECTED DATA

PD1. Age PERSON DATA ELEMENTS OBTAINED AFTER LINKAGE TO OTHER DATA

Level 3: All Drivers PL1. Driver License Restrictions PL2. Commercial Motor Vehicle Endorsements PL3. Driver License Status PL4. Drug Test Result

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Level 6: All Injured Persons PL5. Injury Area PL6. Injury Description ROADWAY DATA ELEMENTS OBTAINED AFTER LINKAGE TO OTHER DATA

RL1. Bridge /Structure Identification Number RL2. Roadway Curvature Y RL3. Grade Y RL4. Part of National Highway System Y RL5. Roadway Functional Class Y RL6. Annual Average Daily Traffic Y RL7. Widths of the Lane(s) and Shoulder(s) Y RL8. Width of Median Y RL9. Access Control Y RL10. Railway Crossing ID RL11. Roadway Lighting Y RL12. Pavement Markings, Longitudinal Y RL13. Bikeway Y RL14. Delineator Presence Y RL15. Traffic Control Type at Intersection Y RL16. Mainline Number of Lanes at Intersection Y RL17. Side-Road Number of Lanes at Intersection Y RL18. Total Volume of Entering Vehicles Y (1) Data Elements indicated are those most critical to work zone and project management issues, and are expected to be of primary interest in managing WZ safety. (2) Data elements indicated are important to provide details normally considered in assessing the relationship of WZ crashes and elements to safety management. These elements should typically be compiled at the time of the event when the initial report is prepared and submitted within a highway agency.

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APPENDIX F:

NYSDOT ACCIDENT REPORTING PROGRAM – DATA ELEMENTS AND ATTRIBUTES

The data elements and attributes listed below are taken from an actual NYSDOT accident report data file. Some of these elements and attributes are described in some detail in the User Manual provided for the NYSDOT Construction Accident Reporting Program. However, no specific instructions or list of attributes was located for a number of these data elements. A – D Number – this element lists the contract number for the construction project involved, consisting of a seven character alpha-numeric code. It can also be used to identify highway permit locations and locations not related to a specific project. B – Report Number – this element is a unique identification number for each report, in the form xx-xxx-xxxx. The first two characters identify the DOT region where the event occurred, the next three are a sequential identifying number starting over at 001 each year, and the final four identify the calendar year in which the event happened. C – Accident Date D Accident Year E – Accident Time G – Accident Location – this narrative description lists the highway route number, road/street name, and the municipality and county. H – Ref Marker – this is a 12 character location identifier used on the state highway system that identifies highway location to the nearest 0.1 mile. Any locations not on a state highway are identified as 0000-0000-0000. I – O – these seven elements identify the contractor and subcontractors involved, the contractor’s competent person for the operation involved, DOT Region number, and the DOT project engineer (Engineer-in-Charge or EIC), including phone number. P – Severity – defines incident severity as Property Damage, Personal Injury, or Fatal. It codes only the single most serious injury in any crash or accident. Q – Employee – true/false attribute indicates whether or not a worker was involved in the event. R – Traffic - true/false attribute identifies traffic crashes that occur within a WZ. Traffic crashes that occur other than within a project WZ, such as offsite crashes involving employees in travel, are coded as “false” for this element..

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S – Equipment – True/false attribute identifies the involvement of any type of work vehicles, equipment, or WZ traffic control device or safety feature in the event. This element is used both for traffic crashes and for non-crash accidents. The specific types of vehicle or equipment is identified subsequently in elements AJ, AM, AN, and BT. T – Utility – True/false attribute is used to identify incidents involving contact with or damage to utility infrastructure. The specific type of utility involved is identified subsequently in elements BU, BV, BW, BX, BY, BZ, and CM. U – Offsite – True/false attribute is used to identify incidents that occur other than on a construction or permit project site. This includes those involving DOT employees in travel to a project site, at off-site manufacturing or batch plants, and DOT regional offices. Note: there is no specific single data element to identify construction accidents. Typically, construction accidents are associated with the attribute “false” in element “R – Traffic”. However, that alone does not confirm that an event is a construction accident. In effect, it is necessary to examine several data elements to confirm that an incident is a construction accident occurring within a WZ or otherwise on a project site. V – Description – this data element provides a 255 character narrative description of the incident prepared by the person submitting the report. No specific terminology or other instructions are provided. W – Weather – a standard list of attributes is provided to describe the weather at the time of the incident – includes clear, cloudy, snow, rain, and other. X – Pavement – a standard list of attributes is provided to describe the pavement surface condition at the incident location – includes dry, wet, snow/ice, and other. Y – Light Conditions – available attributes are daylight, night, dawn, and dusk. Z – Roadway Lighting - available attributes include daytime, night lights, and none. AA – Agency - Law enforcement agency involved, if any AB – Officer – name and badge number of police officer that submits the police accident report, or heads any other investigation of the incident. AC – Contributory Factors - 120 character narrative description of any factors that may have contributed to the occurrence or severity of the incident. No specific terminology or other instructions are provided. AD – Activity Controls – provides a narrative description of traffic control devices and safety features and procedures involved in or present at the incident. No specific terminology or other instructions are provided, and no standard list of attributes is provided. As a result, this data

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element varies widely from report to report, and by itself, cannot be used to categorize incident by specific types of traffic control or work procedure. AE – Utility Violation – narrative description used in utility contact incidents to identify any factors such as failure to mark underground utilities, failure to maintain markings, violating proximity requirements for overhead power lines, or any other factors that may have been related to the incident. . No specific terminology or other instructions are provided, and no standard list of attributes is provided. As a result, this data element varies widely from report to report, and by itself, cannot be used to categorize violations involved in utility contact incidents. AG – Lane Closure – true/false attribute indicates whether a travel lane was closed at the location of a traffic crash in a WZ. AH – Flagger Device – true/false attribute identifies whether a flagger was present at the crash location and involved in the crash in any way. This element does not identify the specific type of device used – paddle, flag, automated flagger device, etc. AI – Slow moving Operation – true/false attribute identifies incidents involving a slow-moving WZ operation. AJ – Shadow Vehicle – true/false attribute identifies whether a shadow vehicle, with or without a truck-mounted attenuator, was involved in the incident. It is necessary to check the various narrative data elements to determine whether a shadow vehicle was equipped with a truck-mounted attenuator. AK – Workzone Intrusion – true/false attribute identifies incidents involving a work space intrusion. In some cases, it appears that rear-end crashes into shadow vehicles and crashes in which a vehicle strikes a flagger are also coded as “true” for this element. While in most cases, it appears this element is consistent with the more precise term “work space intrusion”, some reports were noted where the element tales on a broader definition. AL – Ran off roadway – true/false attribute generally identifies crashes in which the vehicle leaves the travel lanes and shoulder prior to the first harmful event. This element is somewhat similar to MMUCC element C7. Location of First Harmful Event, in which several attributes can be selected to describe locations beyond the travel lanes and shoulders. It appears that in some cases, this element may be coded as “true” to denote a subsequent harmful event after a first harmful event in the travel lanes or on the shoulder. AM – Construction Equipment – a narrative description is entered to identify the type of work vehicle or equipment involved. It is unclear if a standard list of attributes is provided. A number of attributes such as truck-heavy, truck-light, pickup, backhoe, and crane are frequently reported, along with occasional use of other more specific attributes. The list of attributes reported also includes hand tools such as air hose, chipper gun, and skill saw, and even occasionally traffic control devices such as arrow board. AN – Construction Type – the function of this element is unclear, although it appears to provide a general classification of the type of construction equipment involved. Two attributes –

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machine- heavy and machine-light are the predominant attributes listed, although others are occasionally listed. It is unclear how this element relates to AM. Note: Elements BR, BS, and BT appear to overlap/duplicate elements AM and AN to some extent. However, the relationship between these elements is unclear. AO, AP, AQ, AR, AS, AT - true/false attributes identify whether various other reports are available for the incidents. These include police accident reports, worker compensation reports, DOT employee accident reports, and others. AU – Photos – true/false attribute identifies whether photographs are available for the incident. AV – Form Other – true/false attribute identifies whether any other forms – AO – AT – are available. AW – BE – this series of nine elements identifies dates the report was initiated, revised, and finalized, and the persons that prepared or revised the report. BF – Head-on – true/false attribute identifies the manner of crash in which two vehicles collide front to front. BG – Rear End - true/false attribute identifies the manner of crash in which two vehicles collide front to rear. BH – Left Turn – true/false attribute identifies manner of crash in which two vehicles collide while one is in the process of making a left turn. The actual manner of crash is not specified, and could involve head on, sideswipe, or angle crash. BI – Intersection – The purpose of this element is unclear. It appears to identify crashes that occur within a roadway intersection by a true/false attribute. However, in some cases, it is coded “true” for crashes that occur at a ramp merge area or at the intersection of a driveway and roadway. This element does not identify manner of crash. BJ – Sideswipe – true/false attribute identifies manner of crash “sideswipe”. It does not distinguish between same and opposite direction sideswipes. BK – Off Roadway – the function of this element is unclear, although in some cases, it appears to indicate crashes in which a vehicle comes to rest off the roadway. It is unclear how it relates to element AL – Ran off roadway. Note: Elements BF, BG, BH, and BJ generally describe the manner of collision, although these elements are not as specific as those included in the MMUCC. Elements BI and BK generally appear to describe either crash location within or beyond the roadway for only certain types of crashes.

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BL – BQ - true/false attributes defines the body location of employee injuries. These elements include head, arm, leg, foot, back, and other. These elements arte not used to define injuries to non-employees. BR – Crane – true/false attribute identifies crashes or accidents involving a crane. BS – Truck – true/false attribute identifies crashes or accidents involving a truck. This element does not distinguish between light and heavy trucks and pickups. BT – Equipment other – true/false attribute identifies crashes or accidents involving other equipment. The type of equipment is not defined. Note: The relationship between elements BR, BS, and BT and previous elements AM and AN is not clear. BU – BZ – these elements use true/false attributes to identify the type of utility infrastructure involved, including gas, water, electric, sewer, telephone, and cable TV. There does not appear to be any means to identify whether the utility was below ground or above ground, other than to refer to the narrative descriptions in elements V, AC, and AE. Element CM is used to identify by a true/false attribute incidents in which a utility type other than these was involved. CA – CC – true/false attributes identify person involved in offsite incidents as DOT employee, contractor employee, or other. CD – CG – these elements track the dates the report was received in and approved by the regional office and the main office. CH – FO id – the purpose of this element is unclear, but it appears to be a sequential number for multiple reports prepared by each project field office. CI – Proj. Operations – the function of this element is unclear. It appears the true/false attribute identifies incidents in which a work operation was directly involved, or a work operation was active in the vicinity of the incident at the time it occurred. CJ – Operations Type – attributes “day, night, and both” identify when the work operations on the project take place. This element is coded as NA for offsite incidents. CK – Traffic Other – The function of this element is unclear. True/false attribute appears to be related to certain types of manner of crash, such as non-collision between two vehicles, and pedestrian, bicycle, and motorcycle crashes. In some cases, this element appears to overlap attributes addressed in elements BF – BK. CL – Traffic Not – true/false attribute identifies vehicle crashes that occur within the limits of a construction or permit project, but that are considered not to be related to the WZ traffic controls or work operation.

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CM – Utility Other – true/false attribute identifies the involvement of a utility type other than those listed in elements BU – BZ, such as a traffic signal. CN – Workzone – the function of this element, which uses a true/false attribute, is unclear. It is associated with a “true” attribute for element “R – traffic” in some cases, but not in others. CO – MPT Install - the true/false attribute identify crashes that occur during the setup or installation of WZ traffic control. This element appears to cover crashes during adjustment of a WZ as well, such as relocating channelizing devices. CP – MPT Remove - the true/false attribute identify crashes that occur during the takedown or removal of WZ traffic control. CQ – Long Description – this element provides for a longer narrative description of the incident than that provided in element V. The allowable length of this narrative is unknown, as is the criteria for providing this longer narrative. This element was rarely completed in any of the reports reviewed.

appendix a: data collection, reduction, and analysis in...

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