Roadway Measurement System Evaluation (2011)

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C H A P T E R 2 Roadway Measurement System Evaluation RodeoChallenges and Issues Overall, the rodeo itself went quite well, with very good feed- back from the participants and SHRP 2. However, when the data results were delivered by the teams, it became apparent that there were some issues with respect to consistency and quality of the data. The largest challenge encountered was obtaining the desired data in the correct format from each of the participants. Before the rodeo, the rodeo data collection plan, data ele- ments list, and data delivery templates appeared to be sufficient for the participants to perform the data reduction and provide the research team with the desired data. The participants were provided with 1 month to review the data elements list, and they did not raise any questions prior to the rodeo. It subsequently became apparent that the participants did not understand how to fill out the data templates properly and provide the research team with the data in the proper requested format. Some of the data format issues were readily corrected by the research team, while others resulted in requests for clarification from the participants. Unfortunately, even after the study team requested clarification from one of the teams, its data could not be matched with the reference data within a rea- sonable level of effort. Therefore, its data was not included in the final data analysis. In addition, three of the teams did not provide the requested three repetitions of each rodeo site; only subsequently did two of these teams provide the requisite data. All of the teams provided copies of their digital image files. Only six teams provided their GPS traces, and only four pro- vided road profile and geometrics data. Analysis of the final data elements provided by the teams revealed that none of the teams provided all the requested data elements. This is summarized by overall data type in Table 2.1. For the reasons cited, Teams 06, 08, and 10 were not evaluated. Teams 08 and 10 did not provide sufficient data to evaluate; the data from Team 06 could not be matched to the reference data.7to reference their data for reporting some of the data elements, such as International Roughness Index (IRI) and cross-slope. This resulted in greater effort by the research team to obtain complete data from several of the participants and then to rework some of the data to make them usable in the S03 analysis. As more in-depth analysis of the final data set proceeded, it was observed that the participants may not have fully under- stood the data reduction and processing descriptions in the rodeo data collection plan and data elements list. The provision of more in-depth descriptions of the data elements, including the following, may have improved the teams’ understanding of the data requirements: • Descriptions of the data elements; • Instructions regarding where to reference the data elements; • Details regarding where to measure the data elements; • How to measure the data elements; • How the data elements are reported, including units; and • Methodologies for processing directly measured data into desired final data, for example, instructions related to how to take the sliding grade measurements from the vehicles and use them to develop vertical curvature data. The above data collection and reporting issues should be addressed through the development of a detailed data collection manual focused on the needs of the SHRP 2 Safety Program. Data Elements Several of the data elements in the rodeo data elements list are desired data for roadway safety research but not routinely collected by typical roadway inventory data collection firms, such as those that participated in the rodeo. However, with the advent of the newer data collection technologies, such as LIDAR systems and scanning laser systems, these desired data elements were included to determine if the participants couldThe participants also seemed to have difficulty using the pro- vided georeference coordinates for the “0” reference locations

8Team 08 Team 09 Team 10 1 rep Partial — 1 rep x — 1 rep Partial — 1 rep Partial — 1 rep x — 1 rep x — 1 rep Partial — 1 rep x — 1 rep Partial — 1 rep x — 1 rep Partial — 1 rep — — 1 rep Partial — 1 rep Partial — 1 rep Partial — 1 rep Partial 1 rep Partial — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — orted some of the data elements for the particular Table 2.1. Summary of Final Data Sets ReceivedItems Collected Team 01 Team 02 Team 03 Team 04 Team 05 Team 06 Team 07 Right-of-Way Inventory (Assets) Barrier Systems Partial x — — x No Match Partial On-Street Parking x x x x x No Match x Pavement Markings Partial x Partial Partial x No Match Partial Roadside Obstacles Partial Partial — x — No Match Partial Rumble Strips x — — — x No Match — Sidewalks x x x x Partial No Match x Signs x Partial x Partial x No Match x Street Lighting x x x x x No Match x Intersections Configuration & Dimensions x x Partial x x No Match Partial Traffic Control x x x x x No Match x Signalized Intersections x — Partial x x No Match x Stop-Controlled Intersections x x x x x No Match x Driveways x x x x x No Match x Lanes Partial Partial Partial — Partial No Match Partial Median x x x x — No Match Partial Ramps x — x x — No Match x Shoulder x x x — x No Match — Geometrics Grade x — No Match — No Match No Match x Roadway Cross-Slope x — No Match — No Match No Match x Clear Zone Cross-Slope & Width — — — — — — — Horizontal Curvature Partial — — — No Match No Match Partial Sight Distance — — — — — — — Vertical Curvature — — — — — — — Road Profile — — x — x x x Pavement Texture — — — — — No Match x Pavement Edge Drop-off — — — — — — — Pavement Marking Reflectivity — — — — — — — or Retroreflectivity Note: x = the team reported data for all data elements of this data type; — = the team did not report any of the data elements for the data type; Partial = the team rep data type.

9indeed collect them. Therefore, the following data types and elements were included: • Pavement marking retroreflectivity; • Pavement edge drop-off; • Horizontal curvature; • Sight distance; • Vertical curvature; and • Clear zone width and slope. Three participants provided some data on horizontal curva- ture. However, these data did not meet the accuracy require- ments for the rodeo. The rest of these data types and elements were not provided by any of the teams evaluated in the analy- sis of the final data set. The following discussion regarding data elements that were not provided by any of the participants offers some insight into why they were not offered by the participants. • Pavement Marking Retroreflectivity. While this is an important data element from the standpoint of determining the visibility of pavement markings, it can only be measured directly through use of a retroreflectometer. When asked at the rodeo kick-off meeting, none of the participants claimed to be able to collect this data. The automated data collection vendors that used a scanning laser or LIDAR system could, theoretically, have measured the reflectance of the pavement markings. The reflectance would then have to be correlated back to measurements of retroreflectivity measured with a retroreflectometer. • Pavement Edge Drop-off. Edge drop-off can be an impor- tant parameter in rural, run-off-road accidents. However, this cannot be measured from right-of-way images as part of a roadway evaluation because of the location in the images and the small size of the measurements (typically less than 2 in.). It is theoretically possible to measure edge drop-off with a scanning laser; however, this has not yet been proven in the industry. • Vertical Curvature. This item is not directly measured from the automated data collection equipment, and is not a stan- dard item reported as part of a roadway inventory project. With the automated equipment measuring to the 0.001% of grade and with reporting grade as a sliding value over the wheelbase of the vehicle collecting the data, identifying a vertical curve can require significant analysis, including determining how much change in grade represents a verti- cal curve. • Sight Distance. Sight distance is similar to vertical cur- vature because it is not directly measured, nor is it a stan- dard item reported as part of a roadway inventory project. Determining sight distance requires engineering analysisof the horizontal and vertical alignment data for the roadway. • Clear Zone Width. This measurement could be made from right-of-way cameras by measuring the distance from the edge of the lane to the first obstacle encountered, assuming that the obstacle is also captured in the same image as the edge of the lane. In theory this could be measured using a scanning laser or LIDAR system; however, this has not been proven and would require significant programming and data analysis. • Clear Zone Slope. This cannot be measured by the vehicle- mounted systems in use today, such as the Applanix POS LV, because these systems measure slope as it relates to the attitude of the data collection vehicle. In theory, these sys- tems could be used in conjunction with a scanning laser or LIDAR system to produce this information within the mea- surement limitation of the system. This technology, how- ever, also has not been proven. • Superelevation. The automated data collection equipment measures superelevation as part of cross-slope. For this to be reported separately, an acceptable range of cross-slope val- ues would need to be established, and anything outside of this range would be reported as superelevation. • Horizontal Point of Tangency (PT). This item is not rou- tinely reported from automated data collection systems. Typically, the radius of curvature is very large (thousands of feet), indicating a relatively tangent section, unless a vendor processes the data to filter out locations where the radius of curvature is too large. To determine the PT for a curve, it is necessary to establish the maximum radius of curvature before the roadway is considered “straight” and to use the DMI or GPS location of this point as the PT. • Location of Traffic Signal. Participants did not appear to know where to measure the location of the traffic sig- nal head. An important data type for analysis of rural, run-off-road accidents is roadway geometrics. This data type includes grade, roadway cross-slope, and curvature. The teams partic- ipating in the rodeo seemed to have difficulty providing these types of data. Eight out of 10 teams claimed before the rodeo that they could collect common roadway geometric data; however, only five teams provided any geometric data as part of their final data set. Three out of the five teams that provided geometric data did not provide their data in a format that would allow the data to be readily matched to the individual test sites and the reference data. The two teams that did pro- vide geometric data did not provide it in the requested format; however, they included GPS coordinates with their data so the research team was able to link it back to the rodeo test sites. The two teams that provided data that could be evaluated

10provided grade, roadway cross-slope, point of curvature, radius of curvature, and length of curve. The lack of geometric data provided by the participants was of concern to the project Expert Task Group. Therefore, on September 4, 2009, SHRP 2 issued a request to the 10 teams to reprocess selected data elements on Sites 6 and 7 to be deliv- ered by October 16, 2009. This request was followed by a con- ference call on September 14, 2009, between SHRP 2, the rodeo participants, and the S03 research team to discuss the data to be collected and offer the participants an open forum to ask questions. Subsequently, SHRP 2 decided to prequalify all rodeo participants and asked them to submit the requested geometric data with their submission for the S04B RFQ/P to be released in 2010. The selected data elements to be reprocessed by each participant include the following: • General information on experience collecting geometric data: grade, cross-slope, horizontal and vertical curvature; • Geometric calibration and verification procedures; • Typical accuracies obtained for geometric data; • Site 6—Horizontal curvature data (radius of curvature, length of curve, and point of curvature); and• Site 7—Cross-slope, grade, length of pavement for grade measurement, lane width, shoulder width (paved width), and signs (GPS coordinates and MUTCD code). Lessons Learned The lessons learned during the rodeo and the subsequent data analysis can be summarized as follows: • Most automated data collection firms required that data collection requirements be very clearly specified. • Data delivery templates are not sufficient to ensure correct delivery of data. They should be populated with at least one row of “dummy” data for each data element to be collected. • The participating data collection firms appeared to have dif- ficulty using GPS coordinates to reset their zero-reference location. • The apparent inability of many of the teams to provide the requested data in the desired format is likely indicative of their reluctance to spend a large amount of resources for a demonstration project for which they did not receive any compensation.