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20 Figure 3.5. Traffic count stations in Pittsburgh, Pa. some of the variables in the original database are listed here. ways. Table 3.5 provides a sample of the work zone data from Other information, such as road condition, light condition, West Virginia. weather, and the sobriety of involved drivers, is listed in the Table 3.6 summarizes the availability of complementary original database. data in related states. Online sources from which data can be Crash and traffic volume data are typically saved in a data- downloaded are listed in the footnotes. base, but work zone data usually are not. This is especially true for completed road work. Most states have ongoing projects recorded in the database, but completed projects are not stored. Evaluation of Candidate A few states have incomplete data or data that are not in suffi- Data Sets cient condition for use. For example, as of August 2008 in the To help determine the feasibility of candidate databases, a state of Virginia, the 511 system has used a program called VA multidimensional criterion is established for the data sources. Traffic to log information. Before August 2008, there was lim- These dimensions include comprehensiveness, video data ited free-text-based information recording the scheduled start quality, vehicle data, linkages, and data format and structure. and end of road work. For the state of Pennsylvania, the work Table 3.7 provides a detailed explanation and definition for zone data are only available for projects that occur on state high- each feasibility dimension.

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21 To quantitatively evaluate each qualified candidate data- base, a composite feasibility score is computed to reflect the database's strengths and weaknesses, as displayed in Table 3.8. Each dimension receives a seven-point scale score, ranging from 1, representing a low score (e.g., small sample and data available only in raw form), to 7, representing a high score (e.g., large, representative sample and data in reduced form). In computing the score, each feasibility category is assigned a weight so that the sum of weights across all cat- egories totals 100 (e.g., the weight for the feasibility cate- gory comprehensiveness is 15). Within each feasibility category the various measures that constitute a category are assigned a score so that the sum of scores within a category is 10. For example, the comprehensiveness category includes four measures: (1) driver population, (2) types of road- ways, (3) types of trips, and (4) types of vehicles. These mea- sures are assigned a weight of 4, 4, 1, and 1, respectively. The weights are used to compute a weighted average score for each feasibility category. The feasibility category scores are then used to compute a weighted average overall score between 1 and 7. The quality of video data is vital to this project. Some dimensions, such as whether the driver's face and hand movements can be clearly seen or whether the sur- rounding vehicles can be accurately located by the radar sen- sors, receive greater weights to emphasize the importance of those data to this study. To further illustrate the scoring methodology, the pro- cedure is demonstrated using the 100-Car data set. The score for the comprehensiveness category is computed as the weighted average of the four measures that constitute this category as Color version of this figure: www.trb.org/Main/Blurbs/165281.aspx. 6 4 + 7 4 + 6 1+ 7 1 = 6.50 (1) Figure 3.6. Traffic count stations in Delaware. 10 This computation is repeated for each of the remaining Each candidate database is evaluated on each dimension to feasibility categories (video data quality, vehicle data, link- demonstrate the suitability for further analysis. At the same ages, and data format and structure). The overall score is time, the legal restriction for each data set is examined. Cer- then computed as the weighted average of the various cat- tain data sets have IRB restrictions, meaning that the data col- egory scores as lected in that study are restricted for external usage and need to be eliminated from the candidate pool. Some studies col- lected video data at a lower frequency and, therefore, are not 6.50 15 + 6.20 40 + 6.11 20 + 2.67 20 + 7.00 5 = 5.6 (2) suitable for this study. 100 Table 3.2. Location of Traffic Station Link ID Counter Sensor Physical Location Latitude Longitude 507002 1 1 0.205 mi from Country Club 37.21934 -80.4056

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22 Table 3.3. Sample of Traffic Count Data in Virginia Link ID Direction Lane Start Date and Time Interval Class Quality Class 15 507002 1 1 3/15/2007 7:00 15 1 4 507002 1 1 3/15/2007 7:15 15 1 6 507002 1 1 3/15/2007 7:30 15 1 20 507002 1 1 3/15/2007 7:45 15 1 26 507002 1 1 3/15/2007 8:00 15 1 20 507002 1 1 3/15/2007 8:15 15 1 26 507002 1 1 3/15/2007 8:30 15 1 32 507002 1 1 3/15/2007 8:45 15 1 20 507002 1 1 3/15/2007 9:00 15 1 22 507002 1 1 3/15/2007 9:15 15 1 8 507002 1 1 3/15/2007 9:30 15 1 10 507002 1 1 3/15/2007 9:45 15 1 10 507002 1 1 3/15/2007 10:00 15 1 6 507002 1 1 3/15/2007 10:15 15 1 4 507002 1 1 3/15/2007 10:30 15 1 7 507002 1 1 3/15/2007 10:45 15 1 11 507002 1 1 3/15/2007 11:00 15 1 8 507002 1 1 3/15/2007 11:15 15 1 6 507002 1 1 3/15/2007 11:30 15 1 10 507002 1 1 3/15/2007 11:45 15 1 18 507002 1 1 3/15/2007 12:00 15 1 4 507002 1 1 3/15/2007 12:15 15 1 16 507002 1 1 3/15/2007 12:30 15 1 18 507002 1 1 3/15/2007 12:45 15 1 12 507002 1 1 3/15/2007 13:00 15 1 18 507002 1 1 3/15/2007 13:15 15 1 9 Table 3.4. Crash Sample Data from Washington, D.C. No. of No. of No. of No. of Passengers Passengers Date Time Report Type Street Block Vehicles Injuries in Car 1 in Car 2 01/22/07 19:20 1/22/07 Injury Good Hope Rd. 2300 2 1 1 2 01/22/07 19:20 1/22/07 Prop. Damage Benning Rd. 3330 1 0 1 0 01/23/07 12:20 1/23/07 DC Property Benning Rd. 4500 2 0 1 1 08/12/07 11:25 8/12/07 Injury Southern Ave. 4400 2 1 1 0 08/12/07 14:00 8/12/07 Hit and Run 57th St. 100 2 0 1 0

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23 Table 3.5. Sample Work Zone Data from West Virginia County Route Project Description Miles Start Date Completion Date Braxton WV 4 Replace Drainage, GassawaySutton Road 0.68 9/25/2008 9/28/2008 Braxton I-79 Reset Bearings, Sutton/Gassaway Bridge 0.22 5/28/2008 7/28/2008 Braxton I-79 Resurfacing, County 19/26Flatwoods 2.76 6/28/2008 10/31/2008 Braxton I-79 Resurfacing, FlatwoodsBurnsville Road 3.74 5/28/2008 10/31/2008 Braxton I-79 Resurfacing, WV 5Burnsville 3.95 6/28/2008 10/31/2008 Brooke WV 2 Beech BottomWellsburg Road 4.32 7/28/2008 10/28/2008 Brooke WV 2 FollansbeeCoketown Road 2.09 7/1/2008 10/28/2008 Table 3.6. Environmental Data of Candidate Studies The results show that the NTDS and the NTNDS score the highest, with high scores for video, vehicle, and format struc- Traffic Work Crash State Count Zone Log Log ture measures. Although these data sets are limited in terms of population or vehicle coverage, the weights assigned to Virginia Yes Yesa Yes these categories were lower; thus the final score is still consid- Delaware Yes No Yes ered high relative to the other studies. The next two studies are the 100-Car Study and the DDWS FOT. Noteworthy is Maryland Yes No Yes the fact that the truck and teen driver studies scored low in b c Washington, D.C. Yes Yes Yes the comprehensiveness category because they are restricted New York d Yes e Yes Yes either to specific vehicle types or specific driver populations. New Jersey Yes f No Yesg Given that the focus of this project is to investigate the feasi- Pennsylvaniah Yes Yes Yes bility of using video data to characterize driver behavior i j before nonrecurring congestion, this feasibility category is West Virginia Yes Yes Yesk not assigned a high weight. Michigan Yes No Yes All the data sources are accessible to the research team, aData before August 2008 incomplete. although some limitations may apply. For example, the teen bData available at http://ddot.dc.gov/DC/DDOT/About+DDOT/Maps/Traffic+ study conducted by VTTI, which studied minors, would Volume+Maps. According to DDOT, the data were collected using portable counters every 3 years and are converted to Annual Average Daily Traffic (AADT) require special procedures before any data mining could be as shown on the map. conducted. Specifically, data for this study are strictly limited cData available online at http://app.ddot.dc.gov/information_dsf/construction/ to VTTI researchers, and data reduction can be conducted index.asp?pro=COM&wardno=1. dOnline real-time data available at www.511ny.org/traffic.aspx. only in a separate laboratory in which data reductionists can- eOnline data available at http://gis.nysdot.gov/tdv/. not be seen by other personnel. Special instructions should be fOnline data available at www.state.nj.us/transportation/refdata/roadway/traffic_ counts/. given to reductionists regarding what to do if they find sensi- gOnline data available at www.state.nj.us/transportation/refdata/accident/ tive data or if they meet participants socially and other such rawdata01-03.shtm. conduct instructions. The resulting qualified data sets are hOnline real-time data available at www.dot7.state.pa.us/TravelerInformation/. iAADT data online at www.wvdot.com/3_roadways/rp/TA%20Traffic%20files/ listed in Table 3.8. As can be seen from the table, Projects 6, SecBcounts.htm. 7, 8, and 11 score relatively higher than the other projects jReal-time work zone information online www.transportation.wv.gov/highways/ overall. These projects collected data with fewer flaws in video traffic/Pages/roadconditions.aspx. Historical work zone data available for the past 23 years. data. Postprocessing of that data will require fewer monetary kwww.transportation.wv.gov/highways/traffic/Pages/default.aspx. and human resources.

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24 Table 3.7. Definitions of Dimensions Feasibility Dimensions Definitions Institutional Review Board (IRB) Do the consent forms used in the study allow for the data to be released to third parties? Comprehensiveness Driver population Does the data set contain a sample of heterogeneous drivers (e.g., teens, older adults, novices)? Types of roadways Are multiple driving locations (freeways, urban settings, rural roads) represented in the data set? Types of trips Does the data set contain different trips that occurred at different times during the day (e.g., peak and nonpeak)? Types of vehicles Does the data set contain multiple vehicle types? Video Data Quality Driver's hand and foot movements captured Can the driver's hands and feet be clearly seen in the video? Driver's face captured Does the video resolution allow for eyeglance reduction? Is it possible to see driverpassenger interactions and other sources of distraction? Front view Do the camera views allow verification of interaction between the vehicle and other vehicles in sight? Side view Do camera views outside the vehicle allow the researcher to see what the driver is responding to by the side of the vehicle? Rear view Will the following vehicle be seen in the video? Vehicle Data Lane location for each target Are radar data available for calculating lane locations for other targets? Projected collision time Is TTC available in the data set or can it be calculated by data reductionists? Speed Is vehicle speed available? Headway Is headway available (either distance or time headway)? Accelerometer Is acceleration measured? Braking Is braking behavior recorded in the data? GPS Are GPS data available to identify the vehicle's location? Lane-changing behavior Is lane-changing behavior coded in the data or in the reduced data? Lateral placement Is the lateral placement of the vehicle measured? Linkages Ability to link to environmental data Is it possible to link the data to environmental data (such as weather) using the time and location information? Ability to link to operational data Will it be possible to link the vehicle data with the surrounding operational data, such as traffic volume or congestion? Ability to link to special-event data Is the time stamp valid to link the data to a surrounding special event? Ability to link to incident data Are the crash data available to be linked to the data sets? Ability to link to traffic control devices Can any traffic control devices (e.g., traffic light, stop sign, yield sign) be linked to the data set? Ability to link to work zone data Can work zone data be linked to the data set? Data Format and Structure Sampling rate suitability Is the sampling rate of the data collection sufficient to understand driver behavior and traffic conditions outside the vehicle? Event or continuous Does the data set contain continuous driving behavior, or just segments that are event-triggered? Reduced or raw Is the data set already in a format that would allow for efficient analysis (reduced), or are the data only available in a raw format?

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25 Table 3.8. Scale Scores of Candidate Studies Project 5: Road Project 7: Project 2: Departure Drowsy Project 8: Project 11: ACAS Crash Driver Naturalistic Naturalistic Field Warning Project 6: Warning Truck Teen Operational System 100-Car System Driving Driving Feasibility Dimensions (Score) Weight Test (FOT) FOT Study FOT Study Study Legal Restrictions (0/1) 1 1 1 1 1 1 Comprehensiveness 15 6.40 6.40 6.50 3.40 3.80 5.30 Driver population 4 7 7 6 4 5 3 Types of roadways 4 7 7 7 4 4 7 Types of trips 1 7 7 6 1 1 6 Types of vehicles 1 1 1 7 1 1 7 Video Data Quality 40 3.40 3.40 6.20 7.00 7.00 6.20 Driver's hand and foot movements captured 2 1 1 7 7 7 7 Driver's face captured 2 7 7 7 7 7 7 Front view 2 7 7 7 7 7 7 Side view 2 1 1 3 7 7 3 Rear view 2 1 1 7 7 7 7 Vehicle Data 20 7.00 5.33 6.11 6.00 7.00 6.33 Lane location for each target 1 7 4 5 7 7 5 Projected collision time 1 7 1 7 7 7 7 Speed 1 7 7 7 7 7 7 Headway 1 7 1 7 7 7 7 Accelerometer 1 7 7 7 7 7 7 Braking 1 7 7 7 7 7 7 GPS 1 7 7 5 4 7 7 Lane-changing behavior 1 7 7 5 7 7 5 Lateral placement 1 7 7 5 1 7 5 Linkages 20 5.5 5.5 2.67 3.17 5.50 5.83 Ability to link to environmental data 2 7 7 2 3 7 7 Ability to link to operational data 2 7 7 2 3 7 7 Ability to link to special-event data 2 4 4 2 4 4 4 Ability to link to incident data 2 7 7 2 3 7 7 Ability to link to traffic control devices 2 6 6 6 3 6 6 Ability to link to work zone data 2 2 2 2 3 2 4 Data Format and Structure 5 5.20 5.20 7.00 7.00 7.00 7.00 Sampling rate suitability 1 7 7 7 7 7 7 Event or continuous 6 7 7 7 7 7 7 Raw or reduced 3 1 1 7 7 7 7 Overall Score (17) 5.1 4.7 5.6 5.5 6.2 6.1