Proceedings of the 4th World Congress on Engineering Asset Management Athens, Greece

28 - 30 September 2009

RISK-BASED APPROACH FOR MANAGING ROAD SURFACE FRICTION OF ROAD ASSETS

Noppadol Piyatrapoomi (Ph.D.) a and Justin Weligamage (M.Eng.Sc, MBA) a

a Road Asset Management Branch, Queensland Department of Transport and Main Roads, Brisbane, Queensland Australia.

In Australia, road crash trauma costs the nation approximately A$18 billion annually whilst the United States estimates an economic impact of around US$230 billion on its network. Worldwide, the economic cost of road crashes is estimated to be around US$518 billion each year. It is therefore in both the sociological and economic interests of society that attempts are made to reduce, as much as possible, the level and severity of crashes. There are many factors that contribute to road crashes on a road network. The complex manner in which human behaviour, environmental and vehicle failure factors can interact in many critical driving situations, making the task of identifying and managing road crash risk within a road network quite difficult. While road authorities have limited control over external factors such as driver behaviour and vehicle related issues, some environmental factors can be managed such as road surface friction (or skid resistance of road surface). A risk-based method for managing road surface friction (i.e. skid resistance) is presented in this paper. The risk-based method incorporates ‘risk’ into the analysis of skid resistance and crash rate, by linking the statistical properties of skid resistance to the risk of a crash. By examining the variation of skid resistance values throughout the network, the proposed methodology can establish an optimal ‘investigatory level’ for a given level of crash risk, along with a set of statistical ‘tolerance’ bounds in which measured skid resistance values can be found. The investigatory level is a threshold level for triggering a detailed investigation of a road site to identify whether a remedial treatment should be made. A road category of normal demand, spray sealed surface, speed zone greater than 80 km and annual average daily traffic less than 5,000 vehicles was used in demonstrating the application of the method.

Key Words: skid resistance, wet crashes, risk-based approach, investigatory level, road surface

1 INTRODUCTION

The skid resistance of a road surface is a condition parameter which quantifies the road’s contribution to friction between the surface and a vehicle tyre. Technically speaking, it is the retarding force that is generated by the interaction between the road surface and the tyre under a locked (non-rotating) wheel. A wheel obtains such as state when the frictional demand exceeds the available friction force at the interface of tyre and road. Therefore, skid resistance is an important factor during events in which these phenomena are likely to occur, such as the high “demand activities” of accelerating, decelerating and cornering. While it is generally accepted that adequate skid resistance levels are maintained in dry conditions, skid resistance decreases substantially in wet driving conditions. It has been noted in US studies that around 20% of road crashes occur in wet driving conditions, a number which is increasing [1]. It is therefore the skid resistance level in wet conditions that is of interest when looking at crash occurrence.

A common method applied in practice for managing skid resistance is to set what are known as investigatory levels for the various road categories. The investigatory levels are set as an intermediate form of surface friction management, in that they do not automatically signal that maintenance work is required. If, through normal roadway friction investigation, a particular road site is measured to have a skid resistance value below the relevant investigatory level, a more thorough site investigation and test are performed to determine if additional remedial action is needed. The use of an investigatory level allows the roadway to be assessed taking all factors, including its skid resistance, into account. This provides an extra layer of control in the management process by checking that only those sites which are in most need of maintenance are targeted first, optimising both the materials and budget available to the road authority. The selection of suitable investigatory levels has been the focus of much research, and so too the subject of this project.

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Most investigatory levels adopted by road authorities in Australia were based on UK studies and were adjusted to suit Australia with some modifications [2, 3]. However, risks of road crashes associated with these adopted investigatory levels are unknown. Many research studies attempted to assess risk of road crashes in relation to skid resistance on their historical data using regression or correlation analysis however they reported no conclusive results [1, 4, 5, 6, 7]. A joint research project among Queensland Department of Transport and Main Roads (QDTMR) and the Cooperative Research Centre for Integrated Engineering Asset Management (CIEAM) and the Queensland University of Technology was established to explore other methods in assessing risk of road crashes and skid resistance and potentially in establishing investigatory levels explicitly associated with risk of road crashes.

The final output of this project is a result of the application of the methodology to actual roadway system data. Several informed recommendations have been developed which aim to improve the methodology for managing surface friction based on risk associated with wet road crashes.

The measured skid resistance value of a section of road does not stay constant throughout the life of the road surface. Indeed, skid resistance is affected by a variety of different factors, with the eventual result usually being a decrease in the available skid resistance of the road over time. External factors such as the speed, volume and type of traffic using a particular section of road, the local climatic conditions, and the various types of product used in the road all contribute to the period of time for which the skid resistance remains suitable for the safe manoeuvring of vehicles. Selecting and maintaining the skid resistance of the road surface to such levels is an important issue for road authorities, both as a quality issue in the daily driving requirements of the public, and for safety maximisation during high demand incidents such as road crashes. The hypothesis of this research is that current investigatory levels for each particular road category must be linked to an associated risk of road crash for that category, thereby incorporating risk into the management process. Given the wide range of conditions of a roadway, even for those sites within the same category, it is proposed to be more appropriate to derive a range of values on which a measured value may lie. This idea, effectively giving a certain ‘tolerance’ to the Investigatory Level, also incorporates one of the fundamental properties of the skid resistance across a site, category or indeed the entire network: the variability in the measured skid resistance value. Further than that, the linkage between skid resistance and crash rate can only ever be described in a probabilistic sense, and as such, defining a range in which the skid resistance can fall is more appropriate for the purposes of effective decision making.

2 SITE CATEGORIES OR DEMAND CATEGORIES FOR SKID RESISTANCE

The primary aim in managing skid resistance for a road network is to provide skid resistance for vehicles to manoeuvre safely in any roadway condition. It would seem desirable to simply maintain all parts of a road network to a high level, thus ensuring adequate skid resistance. However, to gain a high skid resistance level for all sections of a road network would in most cases be overly expensive. Thus, the purpose of the management process is trying to equalise crash risk across the road network, rather than simply trying to provide high skid resistance level for the whole road network. Higher skid resistance is provided to those road sites which require increased levels of friction, such as corners, intersections or roundabouts. The term “site or demand categories” was established to categorise road sites that require different skid resistance demands for safe manoeuvring. Three levels of demand categories were adopted in Queensland, namely normal, intermediate and high demand categories [2] as shown in Table 1.

Table 1 shows typical demand categories adopted by Queensland Department of Transport and Main Roads. Different skid resistance investigatory levels have been given for these three demand categories. The investigatory levels given in Table 1 are the international friction indices (IFI). This table separates the road network across so-called ‘demand’ categories, as well as across various speed ranges. As can be seen from the table, manoeuvres in high demand areas would be expected to require more friction support than manoeuvres in normal demand areas. As mentioned, the investigatory levels are set as an intermediate form of roadway management, in that they do not automatically signal that maintenance work is required. If, through normal roadway testing, a particular road site is measured to have a skid resistance value below the relevant investigatory level, a more thorough site investigation and test are performed to determine if additional remedial action is needed.

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Table 1

Current Queensland Department of Transport and Main Roads Investigatory Levels for skid resistance

Demand category Description of Site F60 Investigatory Level

40 – 50

km/h 60 – 80

km/h 100 – 110

km/h

High

Curves with radius < 100 m. Roundabouts Traffic light controlled intersections. Pedestrian/school crossings. Railway level crossings. Roundabout approaches.

0.3 0.35 N/A

Intermediate

Curves with radius < 250m. Gradients > 5% and > 50m long. Freeway and Highway on/off ramps. Intersections. Curves with advisory speed > 15 km/h below speed limit.

0.25 0.3 0.35

Normal Manoeuvre – free areas. 0.2 0.25 0.3

3 METHODOLOGY

This session presents a proposed method based on the application of probability theory in assessing risk of road crashes associated with skid resistance and in establishing investigatory levels. Information in relation to this methodology is also given in references 6, 7 and 8. The proposed method allows risk of road crashes to be explicitly incorporated into decision-making in establishing skid resistance investigatory levels. The steps in the analysis include

1. Categorise road network Given an initially large road network to study, it is desirable to separate the network down into a large number of smaller

roadway sections. This is done not only to make the data more manageable to analyse, but also to allow roadway to be split up according to a particular set of characteristics. There are a variety of different environmental and structural properties which can affect the skid resistance of a roadway, and to be able to test if these have any relationships with skid resistance and crash rates, the various road sections must be grouped according to these characteristics. This process is also important when recommendations must be made for the various ‘demand’ categories.

2. Obtain historical road data under the particular categorisation Once the particular category and road surface condition variables are selected, all available data from the road network

must be collected. An important point of this analysis is that it is based on historical data in such a way that the information provided by the analysis also increases and improves as the amount of data increases. The future intention of the analysis is that it is incorporated into the information management system of the QDTMR.

3. Divide road network sample into small sections suitable for the analysis. Once the data has been separated and collected according to the category of interest, the relevant road sections are divided

into small segments of equal length. This allows the variability of skid resistance to be examined over a variety of distances. For example, there may be a particular category for which the variation over small segments is of interest, whereas for another category, larger segments may be sufficient. Once each segment is defined, a cumulative probability distribution (Fx) of skid resistance can be formed for each segment. Figure 1 shows the cumulative probability distributions of skid resistance for all segments of a category.

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Figure 1: The cumulative probability distributions of skid resistance for all road segments of a certain category

4. Count the number of road crashes and identify the segments where crashes occur 5. Map the related crash data to these distributions After all cumulative probability distributions are created for the road segments within the category, those sections on which

crashes occurred are identified.

6. Divide and categorise crash rates across all distributions and road sections This step is fundamental to the methodology in that it involves the selection of suitable envelope distributions of skid

resistance (the terms envelope and investigatory curve will be used interchangeably when referring to the derived distribution). These distributions essentially split the crash sample in such a way that a certain percentage of crashes occur on segments of road whose skid resistance curves fall below the envelope. Alternatively, the split can be measured in ‘risk’ related terms. The figure indicates that 15% of crashes occur on road surfaces that have a cumulative probability distribution of skid resistance greater than the boundary F(x2). Seventy per cent of crashes occurred within the boundary of the two cumulative probability distributions of F(x1) and F(x2). Eighty-five per cent of crashes occurred on road surfaces having a cumulative probability distribution of skid resistance below the boundary F(x2).

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Figure 2: Envelope distribution functions are fitted to the data points, such that a certain percentage of crash related sections are found below the particular curve

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A cumulative probability distribution of recorded skid resistance for a road segment is the cumulative chance of the skid resistance that is likely to occur in that road segment.

A comparison of the recorded skid resistance cumulative probability distributions gives information relating to how the different segments compare in terms of variation in skid resistance. Apart from their use in this analysis, these plots can also provide other information that may be of interest to road engineers; i.e., a visual indication of particular road sections that may have a faulty road surface.

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7. Establish the distributional characteristics for the appropriate crash rates selected as per the management policy The method by which the distributional properties of the envelope distribution are obtained is dependent on the way in

which the curve is produced. The simplest method is to fit the curves by visual inspection (either empirical or parametric, such as a normal distribution) or used a probability-based goodness-of-fit-test. This is the method suggested by Piyatrapoomi (2008) as a preliminary method to obtain curves that provide useful information to decision makers [6].

8. Assess and establish Investigatory Levels for each variable Given a particular envelope distribution, the Investigatory curve, along with a range or interval can then be selected. The

mean value of the distribution would normally be stated as the base Investigatory Level; with the interval set at a certain number of standard deviations either side of this mean value.

9. Repeat the analysis for the remaining road condition variables

10. Develop a management framework that incorporates the relationships established in Step 8 Once a suitable distribution has been selected to guide investigatory decisions, this can then be applied by management and

practitioners in the maintenance regime of the road network. Once management has selected a suitable ‘risk’ value which meets departmental policy, the associated investigatory distribution can be evaluated and applied when examining road sections in the network.

4 ANALYSIS AND RESULTS

The goals of the research project were based around two specific research problems. The first of these was to examine the relationships that exist between crash rate or risk of crash and skid resistance. The analysis of these relationships was then expected to be linked in with a wider research problem; that of producing a methodology which allows management to determine appropriate skid resistance investigatory levels that incorporate the inherent risk of crashes into consideration. The analysis of the skid resistance – crash relationship was also expected to contribute to decisions relating to the current demand category split up for investigatory levels; a separation which up until now had been primarily based on studies from other countries.

The analysis process began with the selection of appropriate analysis categories. Several different categorisations were initially used, and these changed over time as various road condition variables were tested. The two main variables that were used throughout the analysis were seal type and speed zone.

Figure 3: Analysis tool

The methodology developed for this project involves the extraction, manipulation and analysis of very large data sets. A calculation tool was developed specifically for the purpose of the project which allows efficient timely extraction and analysis to be performed. The software has a dual purpose: firstly, it allows the relationships between skid resistance and crash rate to be examined via the method outlined above. Secondly, it provides a beta software model on which future implementations may be based on. Figure 3 shows a calculation tool that was used for the analysis.

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For demonstration purposes, a category of normal demand, of spray seal surface, speed zone greater than 80 km/h, and annual average daily traffic less than 5000 vehicles was presented. The skid data and crash data were recorded in 2004, the total road kilometre in this category for the analysis was approximately 2667 kilometres, and the number of wet crashes was 27. The total road length was divided into small equal segments of 3 kilometres.

Figure 4 shows the result of the analysis. Each cumulative probability distribution shown in the figure represented the variability of skid resistance within a 3 kilometre segment. The figure shows only the cumulative probability distributions of skid resistance of the road segments where wet crashes occurred. The figure shows the boundaries that divided road crashes into different crash risk or crash rate expressed in terms of the number of crashes per 10 million vehicle kilometre travelled or percent of crashes. The essence in this method is to select a boundary curve which is the investigatory level or ‘investigatory curve’ for an acceptable crash risk. For example, decision-makers may accept a risk of having 11 wet crashes per 10 million vehicle kilometre travelled, i.e. in this case the 85% percent boundary. However, this final step involves many more additional inputs than those produced by the analysis presented. Management, before deciding on the investigatory curves must combine these results with other economic and logistical information. For example, while the results may suggest a certain high value in investigatory curve for a large proportion of the network, it may simply not be economically feasible to maintain the entire section to such a high degree. Alternatively, it may not be logistically possible to obtain the required volume of aggregate needed to achieve such a skid resistance value. In these situations, management must combine all the information at hand to produce an optimal solution to the maintenance process.

Figure 4: Investigatory distributions and associated crash risks for the SNa80b5k category with wet crashes only

5 PROPOSED METHOD OF MANAGING SKID RESISTANCE

In the proposed method, the management of skid resistance on the road network is based on monitoring skid resistance and comparing them with an established investigatory curve as shown in Figure 5 rather than comparing the measured skid resistance with a single value of investigatory level as given in Table 1.

Figure 6 demonstrates an example of a comparison between an investigatory curve and a cumulative probability distribution of measured skid resistance of a road section. Figure 6 demonstrates that even though some measured skid resistance values are less than the investigatory curve, it may not trigger a site investigation since the measured skid resistance values which are less than the investigatory curve are in low percentage. In this method, site investigation will not be triggered for every value of skid resistance that falls below the investigatory level, and in practice it may not be feasible to investigate every place where skid resistance falls below the investigatory level. Figure 7 demonstrates a comparison between the investigatory curve and a cumulative probability distribution of measured skid resistance of a road section that may require site investigation. In this example, the percentage of measured skid resistance is considered significant and also exhibits very low values in skid resistance. The probability-based method allows asset managers to identify more clearly the severity of road sections and better prioritise site investigation than the current practice which compares the measured skid resistance values with a single investigatory level. However as mentioned economic implication, societal expectation, government policies in relation to the tolerance level of crash rate and other logistical information such as availability of local material combined with the results of this analysis must be the basis of input information in establishing appropriate investigatory curves. Validation of

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the decision-making must be carried out with crash data that occur after the proposed methodology has been implemented. The investigatory curves can be refined and improvement of the skid resistance management process can be developed through the validation process. The calculation tool developed in this project will be able to facilitate and enhance further improvement and development of the skid resistance management process recommended.

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Figure 5: Example of an investigatory curve

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Figure 6: Example of a comparison between an investigatory curve and a cumulative probability distribution of skid resistance (that does not trigger site investigation)

Percentage area where measured skid resistance falls below the investigatory curve

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Figure 7: Example of a comparison between an investigatory curve and a cumulative probability distribution of skid resistance (that triggers site investigation)

6 CONCLUSIONS

The paper outlined the need to manage skid resistance of road network for road safety and proposed a methodology for establishing investigatory level which is used as an intermediate form of skid resistance management. In managing skid resistance, if a particular road site is measured to have a skid resistance value below the relevant investigatory level, a more thorough site investigation and test are performed to determine if additional remedial action is needed. The paper presented a step-by-step methodology in assessing the crash risk and skid resistance using probability-based approach and establishing investigatory level. An investigatory level suggested in this paper was in the form of a probability curve rather than a single investigatory value. The paper also presented how the investigatory curve would be used for managing skid resistance on the road network. The management of skid resistance on road network based on probability theory was presented. A road category of normal demand, spray sealed surface, speed zone greater than 80 km and annual average daily traffic less than 5000 vehicles was used in demonstrating the application of the method.

7 REFERENCES

1 Kuttesh, J.S. (2004) Quantifying the Relationship between Skid Resistance and Wet Weather Accidents for Virginia Data. Master Thesis, Virginia Polytechnic Institute and State University, Virginia, USA.

2 Weligamage J. (2006) Skid Resistance Management Plan. Road Asset Management Branch, Queensland Department of Main Roads, Queensland, Australia.

3 Austroads (2005) Guidelines for the Management of Road Surface Skid Resistance. AP-G83/05, Austroads, Sydney, Australia4

4 Seiler-Scherer L. (2004) Is the Correlation Between Pavement Skid Resistance and Accident Frequency Significant? Conference Paper STRC, Swiss Transport Research Conference, Switzerland.

5 Viner H.E., Sinhal R. & Parry A.R. (2005) Linking Road Traffic Accidents with Skid Resistance – Recent UK Developments. Proceedings of the International Conference on Surface Friction, Christchurch, New Zealand

6 Piyatrapoomi N, Weligamage J, Bunker J, & Kumar A. (2008) ‘Identifying relationship between skid resistance, road characteristics and crashes using probability-based risk approach’ The International Conference on Managing Road & Runway Surfaces to Improve Safety, 11 - 14 May 2008, Cheltenham England

Percentage area where measured skid resistance falls below the investigatory curve

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7 Piyatrapoomi N, Weligamage J, & Kumar A.(2008) ‘Probability-based method for analysing relationship between skid resistance and road crashes’10th International Conference on Application of Advanced Technologies in Transportation , May 27th-31st, 2008, Athens, Greece

8 Piyatrapoomi N, Weligamage J, & Bunker J. (2007) Establishing a Risk based Approach for Managing Road Skid Resistance. Australasian Road Safety Research, Policing & Education Conference 2007 ‘The Way Ahead'17 – 19 October 2007 Crown Promenade, Melbourne, Australia

Acknowledgments

The authors wish to acknowledge the Queensland Department of Transport and Main Roads and the Australian Cooperative Research Centre (CRC) for Integrated Engineering Asset Management for their financial support. The authors also wish to thank staff at Asset Management Branch in the Department of Main Roads, Queensland in Australia for providing technical data and support. The views expressed in this paper are of the authors and do not represent the views of the organisations.

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