1 PILOT STUDY: PAVEMENT VISUAL CONDITION AND FRICTION AS A PERFORMANCE MEASURE FOR WINTER OPERATIONS Nishantha Bandara, Ph.D., P.E. Department of Civil Engineering Lawrence Technological University 21000 West Ten Mile Road Southfield, Michigan 48075 Phone: 248-204-2602 Fax: 248-204-2568 nbandara@ltu.edu Submission Date: August 1, 2014 Word Count: 3139 (words), 3250 (13 tables and figures), 6389 (Total)
2 ABSTRACT Snow and ice removal and clearing roadways after a winter storm account for major portion of roadway agencies located in snowy region s maintenance budget. With the decreasing funds and increasing demand from the motoring public for mobility, roadway agencies are continuously looking for new innovative approaches for winter maintenance operations. One of the main focus of these approaches include performance measures for winter maintenance. This is specifically important when roadway agencies use contractors to perform winter maintenance tasks. In this study, two performance measures are studied extensively. These include, visual pavement condition behind the snow plow and pavement friction behind the snow plow. Visual pavement condition was observed and recorded into one of five categories while driving behind a snow plow. Pavement friction behind snow plow is measured using a Continuous Friction Measuring (CFT) device. Since visual pavement condition provides driver s perception of the winter driving condition and friction measurements provide an objective measurement of the safety of the roadway under winter condition, a correlation of these provides a basic guideline for winter maintenance and performance measurement of winter maintenance. During the past winter, multiple data collection cycles were performed behind different type of snow plows along I-96 in Livingston County. These preliminary data was used to obtain relationships between winter storm severity, snow plow type and the selected performance measures.
3 INTRODUCTION According to Federal Highway Administration (FHWA), nearly 70 percent of nation s roadways are located in snowy region and approximately 70 percent of the United States population lives in these snowy areas. Snow and ice reduces friction between vehicle tire and pavement surface, causing slower speeds and increased risk of vehicle crashes. In average, vehicle speeds in arterial roads are reduced by 3 to 13 percent while in freeways reduced by 5 to 40 percent during heavy snow events. Based on 2002-2012 crash data, the 10 year average number of crashes during different types of winter weather conditions are shown below (1). Table 1: Winter Weather Annual Average Accident Data in 2002 to 2012 Winter Weather Total Number of Total Injured Total Fatalities Condition Crashes Snow/Sleet 211,188 58,011 769 Icy 154,580 45,133 580 Snow/Slushy 175,233 43,503 572 Roadway agencies spent approximately 20 percent of their maintenance budget for winter maintenance. Due to rising costs of winter maintenance activities and increased demand for mobility from the public, roadway agencies constantly looking for better methods and materials for winter maintenance. One of the main item that roadway agencies are looking at recent times include using performance measures for winter maintenance. Traditionally roadway agencies used materials spent, hours spent and labor costs for winter maintenance as agency performance measures for accounting and reporting purposes. In addition to above input and output based performance measures, outcome based performance measures provides a true picture of the winter maintenance activities. Since more and more roadway agencies are using contractors for their winter maintenance activities, these outcome based performance measures can be easily used for contractor evaluations. PERFORMANCE MEASURES FOR WINTER MAINTENANCE NCHRP Web-Only Document 136, describes three types of performance measures for winter maintenance; input measures, output measures and outcome measures (2, 6). Input measures include the resources used for winter maintenance operations such as, equipment, materials and labor. Most of the roadway agencies keep records of these input measures for accounting purposes in terms of number of snow plows used for winter operations, volume or weight of materials used and labor-hours etc. If the agency is using contractors for winter maintenance operations, these input measures can be directly used as pay items.
4 Output measures include physical outputs for the resources that are used in winter maintenance. These include lane miles per unit of time plowed, truck plowing speed, material application rates, payments for winterizing etc. Outcome measures reflect the end result of winter maintenance during and after a winter storm event typically from the motorist s perspective. Common types of outcome measures include; Visual characteristics of road condition Bare pavement regain time Roadway friction Vehicle speed during winter storms and time to regain normal traffic speed Reduction in crashes Since this paper is based on visual pavement condition and roadway friction under winter conditions, more details of those measures are given below. Visual Characteristics of Road Conditions Visual characteristics of road conditions during various points of winter storm include the following (3); Centerline bare, Wheel path bare, Loose snow covered (percent area and depth), Packed snow covered (percent area and depth), Bare (percent area), Thin ice covered (percent area), Thick ice covered (percent area), Dry, Damp, Slush (percent area and depth, Frost and Wet. A Pavement Snow and Ice Condition (PSIC) table was developed for the NCHRP report 526 (3) using the above visual characteristics of the roadway with traffic flow and other visual information of the road to develop a level of service of the road. PSIC helps an agency to determine the level of maintenance activities related to maintain certain level of service. Some agencies use pictorial reference templates to compare existing pavement conditions to aid to observers. Roadway Friction The coefficient of friction between vehicle tire and pavement can be increased by winter maintenance activities such as snow plowing, deicing, anti-icing and sanding of the roadway. Coefficient of friction between vehicle tires and pavement can be measured using friction testers. Although in the United States friction testing is not primarily used for winter performance measures. However, number of European countries and Japan uses them regularly. Friction can be measured/predicted using three methods; predicting friction using climate, traffic and other roadway conditions, direct friction measurements using an extra wheel installed on vehicles or by traction control systems. There are several types of equipment available for friction measurements; deceleration devices, locked wheel devices, side force devices, fixed slip devices and variable slip devices (4). NCHRP Web Document 136 (2) lists three operational uses of friction measuring devices; they can be used to measure quality of winter maintenance operations, can be used as source of road user information to inform motorists of hazardous locations and also to determine amount of de-icing materials used on the roadway. Some of the
5 problems with current friction measuring practices involve, number of different types of friction measuring devices available and used by roadway agencies and lack of standards to compare the results from different types of devices. For the NCHRP report 136 (2), the researchers conducted a survey among highway agencies to gather information on different types performance measures for winter maintenance used by these agencies. The following table lists outcome based performance measures used by different agencies. Table 2: Outcome based Performance Measures used by Different Agencies (NCHRP 136, 2007) Measure Approach Time to reasonable near-normal winter a. Visual inspection by maintenance conditions personnel (AK, CA, NV, NM, NY) b. Reports from field personnel (IA, CA, NV, NM, NY) c. Visual inspection by law enforcement (NM) Customer satisfaction a. Annual season at end of season (AK) b. Internet survey (CA) Travel speed Time to bare pavement Total time of road closure Total time of chain restrictions Time to single bare wheel track Time to two bare wheel paths Time to treat critical areas Friction a. Automatic traffic recorders (NY, IA) a. Visual inspection by maintenance personnel (CO, MD, NV, OH, WA, ON) b. Reports from field personnel (CO, MD, MO, NV, OH, WA) c. Visual inspection by law enforcement (WI) a. Accounting records of hours closed (CA) a. Records of chain restriction hours (CA, CO) a. Reports from field personnel (IA, KS) a. Reports from field personnel (KS) a. Reports from field personnel (MO) a. Testing (OH, ON) b. Established friction coefficient (Sweden) As seen from the above table, majority of agencies surveyed during NCHRP Project 6-17 (2), use visual pavement condition as a performance measure for winter maintenance. The safety of this approach can be evaluated by investigating any correlation between visual pavement condition and pavement friction during winter operations.
6 DATA COLLECTION METHODS FOR SELECTED PERFORMANCE MEASUREMENTS Data for this study was obtained from an on-going research study for Michigan Department of Transportation (MDOT) titled Evaluating the Use of Tow Plows in Michigan. The following methodology was used to collect data necessary for each selected performance measurements. Visual Pavement Condition A pavement condition evaluation scale was developed for this study based on prior literature and discussions with winter maintenance personnel from Michigan Department of Transportation (MDOT) as shown in the following figure. This pavement condition evaluation scale was incorporated into commercially available Dynatest SURVEY program to use in this study. Bare Surface Condition Description Bare Pavement Picture Centerline Bare (CL Bare) Entire lane is cleared of snow, ice and slush. Wheel Track Bare (WT Bare) Loose Snow/Slush (Loose Snow) Only wheel tracks are bare, snow/ice/slush in the other areas Loose snow/slush covered Snow Covered (Snow) Entire roadway is covered with packed snow and ice Figure 1: Winter Pavement Condition Evaluation Scale
7 Visual pavement condition data was collected while driving behind snow plows and recorded at 528 feet intervals. The condition of the pavement was keyed into the SURVEY software into one of the 5 categories listed in Figure 1 and a photograph of the pavement condition was recorded for future use. After the data collection process is complete, quality checks were performed by comparing recorded data against the photographs taken at the same location. Friction Friction testing was performed using Dynatest 6875 Continuous Friction Tester (CFT). This tester uses a two-axis force transducer mounted on a retractable fifth wheel located under the vehicle bed adjacent to the left wheel of the vehicle. Friction values between road surface and test tire were measured for 500 feet at 1000 feet intervals. The average friction values for each 500 feet test sections were recorded and summarized. Continuous friction testers are categorized as fixed-slip testers and generally measure maximum friction value between the test tire and pavement as shown in the following figure. The maximum friction value simulates ABS braking action. Figure 2: Friction vs. Slip (5)
8 SUMMARY OF DATA Sample data for this study was obtained for winter maintenance road sections assigned to Michigan Department of Transportation s Brighton maintenance garage. Brighton garage is located in Livingston County, Michigan. Visual pavement condition data and pavement friction data were collected on three winter storms during 2013-2014 winter season. The data collection was performed along I-96 freeway within Livingston County, Michigan behind a regular snow plow and a Tow Plow. Surface condition behind the Tow Plow/Regular Plow was visually evaluated and recorded on Dynatest Survey data collection software. Friction values between road surface and test tire were measured for 500 feet at 1000 feet intervals. The average friction values for each 500 feet test sections were recorded and summarized. A summary of collected data during three winter storms are show in the table below. Table 3: Summary of Collected Data Winter Storm Event Storm 1 1/1/2014 Storm 2 1/5/2014 Storm 3 2/1/2014 Winter Storm Condition 5.5 of Snow for 12 hours (Avg. Temp. 12 F) 10.2 of Snow for 22 Hours (Avg. Temp 22 F) 7.4 of Snow for 22 hours (Avg. Temp. 26 F) Lane/Plow Type WB Middle Lane Tow Truck WB Slow Lane Tow Plow EB Fast Lane Regular Plow WB Slow Lane Tow Truck WB Outside Shoulder Tow Plow EB Fast Lane Regular Plow WB Slow Lane Tow Truck WB Shoulder Tow Plow EB Middle Lane Tow Truck EB Slow Lane Tow Plow Average MU Average Operating Speed of the Plow Pavement Surface Condition behind Snow Plow N/A N/A WT Bare 100% 0.33 38.2 WT Bare 100% 0.27 34.5 WT Bare 100% 0.18 38.6 WT Bare - 100 % NA N/A WT Bare 43% Loose Snow 43% CL Bare 14% 0.16 35.4 Loose Snow 91% WT Bare 9% 0.13 35.85 WT Bare 50% Loose Snow 50% N/A N/A Loose Snow 100% 0.12 37.4 Loose Snow 74% WT Bare 26% N/A N/a Loose Snow 62% WT Bare 27% Snow Covered 11%
9 N. Bandara WB Fast Lane Regular Plow EB Fast Lane Regular Plow 0.11 37.8 0.11 38.5 Loose Snow 85% WT Bare 15% Loose Snow 91% CL Bare 6% WT Bare 3% The following figure shows the amount of friction loss during winter storms by comparing baseline friction values collected during the summer time to a sample of data collected during a winter storm behind a snow plow. In average, the friction loss during a winter storm account for a minimum of 0.69 to 0.33 (52% loss at the storm 1) and 0.69 to 0.13 (81% loss during the storm 3). However, it should be noted these high friction losses occur immediately behind the snow plow and once the salt or other chemicals start to work this friction losses may not be this high. Friction Data Behind Tow Plow WB I 96 Slow Lane 1.2 1 Mu 0.8 0.6 towplow Storm 1 0.4 WBI96 Baseline 0.2 0 2000 3000 8000 13000 18000 23000 28000 33000 Distance (ft) from NB US 23 Ramp to WB I 96 Figure 4: Friction Loss due to Winter Storms
10 The sample of collected data for each storm are shown in figures below. Figure 5: Pavement Condition behind Tow Plow and Regular Plow for Storm 1 As seen in the above figures, wheel track bare condition was observed immediately behind both tow plow and regular plow. This is an expected condition due to same type of cutting edges are used in both tow plow and underbody plow. Figure 6: Friction Data behind Tow Plow and Regular Plow for Storm 1 The above friction plots show similar values in average behind both type of plows, ranging from 0.33 for tow plow and 0.27 for regular plow. It should be noted these measurements were taken on different lanes of I-96 freeway.
11 Figure 7: Pavement Condition behind Tow Plow and Regular Plow for Storm 2 Visual pavement condition behind the tow truck of the tow plow show wheel track bare condition while behind regular plow shows loose snow for more than 90 percent of the lane area for the Storm 2. Figure 8: Friction Data behind Tow Plow and Regular Plow for Storm 2 Above friction plots behind snow plows show similar average friction values ranging from 0.18 for the tow plow and 0.16 for the regular plow.
12 Figure 9: Pavement Condition behind Tow Plow and Regular Plow for Storm 3 Visual pavement condition during the Storm 3 behind both snow plows show loose snow condition for the majority of lane areas. Figure 10: Friction Data behind Tow Plow and Regular Plow for Storm 3 Similarly, average friction values behind both snow plows show similar values ranging from 0.13 for the tow plow and 0.11 for the regular plow. The above pavement condition pictures and friction values show, differences in pavement condition and pavement friction in different type of winter storms, despite of the visual similarity seen behind both plows during each storm. During the winter storm 1 the average air temperature was approximately 12 F and snow was light and dry. This resulted 100% wheel track bare condition behind both tow plow and regular plow. Also pavement friction shows relatively high values (average MU of 0.33 and 0.27 for tow plow and regular plow respectively). During winter storm 3, the average air temperature was 26 F and snow was wet and heavy. This resulted loose snow behind both snow plows and relatively low friction values (average mu of 0.13 and 0.11). This clearly shows, the pavement condition behind the snow plow and pavement friction behind
13 snow plow, is not only correlated to snow amount but also to type of snow event (dry snow, wet snow etc.). RESULTS A comparison study was performed using collected hundreds of visual pavement condition data behind the snow plows and pavement friction values from the three storms during 2013-2014 winter season. Friction values were summarized (maximum, average, minimum) for each observed field conditions (Wheel track bare and loose snow). The following figure shows change in friction values with different winter pavement conditions. Since only the wheel track bare and loose snow conditions were observed during the field evaluations, only those conditions are shown. Friction Coefficient, MU 0.7 0.6 0.5 0.4 0.3 0.2 Max Average Min 0.1 0 Bare Centerline Bare Wheel Track Bare Loose Snow Snow Covered Winter Pavement Condition Figure 11: Pavement Visual Condition and Measured Friction Values As seen from the above figure, there are marked differences in measured friction values for different winter pavement conditions. The visual pavement condition behind snow plows show wheel track bare or loose snow during most of the snow storms. Therefore the above chart can be used for 90 percent of the snow storms. This develop chart can be used as a safety predictor and performance measure predictor for winter operations. Work is underway to include more data from different types of snow storms to update this chart with other visual pavement conditions.
14 SUMMARY AND CONCLUSIONS With shrinking budgets and increasing demand for better mobility from the public, highway agencies are constantly searching for better and improved methods for winter maintenance. Performance measures for winter maintenance plays a critical role in this aspects especially for highway agencies who uses contractors for winter maintenance operations. Many highway agencies currently use visual pavement condition as an outcome based performance measure among other traditional performance measures. However, there is no proper understanding related to the safety level of different pavement conditions under winter storms. A relationship with pavement friction levels and visual pavement condition under winter conditions was developed in this pilot study. This relationship can be used by the highway agencies to predict the safety level of the roadway under different pavement conditions. More work is underway to incorporate all possible visual pavement conditions and corresponding friction values to provide a complete understanding of how friction levels change with different pavement conditions. REFERENCES 1. Snow and Ice, Road Weather Management Program, Federal Highway Administration, Washington D.C. http://ops.fhwa.dot.gov/weather/weather_events/snow_ice.htm. Accessed July 7, 2014. 2. Maze, T.H., Albrecht, C., Kroeger, D and Wiegand, J., Performance Measures for Snow and Ice Control Operations, NCHRP Web-Only Document 136, Transportation Research Board, National Cooperative Highway Research Program, Washington D.C., 2007. 3. Blackburn, R.R., Bauer, K.M., Amsler, D.E., Boselley III, S.E., and McElroy, A.D., Snow and Ice Control: Guidelines for Materials and Methods. NCHRP Report 526, Transportation Research Board, National Cooperative Highway Research Program, Washington D.C., 2004. 4. Al-Qadi, I, Loulize, A., Flintsch, G.W., Roosevelt, D.S., Decker, R., Wambold, J.C., and Nixon, W.A., Feasibility of using Friction Indicators to Improve Winter Maintenance Operations and Mobility, NCHRP Web Document 53 (Project 6-14), Transportation Research Board, National Cooperative Highway Research Program, Washington D.C., 2002 5. Henry, J.J., Evaluation of Pavement Friction Characteristics: A synthesis of Highway Practice, NCHRP Synthesis 291, Transportation Research Board, National Research Council, Washington, D.C., 2000 6. Adams, T.M., Danijarsa, M., Martinelli, T., Stanuch, G., and Vonderohe A., Performance Measures for Winter Maintenance, Transportation Research Record: Journal of the Transportation Research Board, No. 1824, Transportation Research Board, National Academics, Washington, D.C., 2004, pp 87-97.