Identifying relationship between skid resistance and road crashes using probability-based approach

Road accidents are of great concerns for road and transport departments around world, which cause tremendous loss and dangers for public. Reducing accident rates and crash severity are imperative goals that governments, road and transport authorities, and researchers are aimed to achieve. In Australia, road crash trauma costs the nation A$15 billion annually. Five people are killed, and 550 are injured every day. Each fatality costs the taxpayer A$1.7 million. Serious injury cases can cost the taxpayer many times the cost of a fatality. Crashes are in general uncontrolled events and are dependent on a number of interrelated factors such as driver behaviour, traffic conditions, travel speed, road geometry and condition, and vehicle characteristics (e.g. tyre type pressure and condition, and suspension type and condition). Skid resistance is considered one of the most important surface characteristics as it has a direct impact on traffic safety. Attempts have been made worldwide to study the relationship between skid resistance and road crashes. Most of these studies used the statistical regression and correlation methods in analysing the relationships between skid resistance and road crashes. The outcomes from these studies provided mix results and not conclusive. The objective of this paper is to present a probability-based method of an ongoing study in identifying the relationship between skid resistance and road crashes. Historical skid resistance and crash data of a road network located in the tropical east coast of Queensland were analysed using the probability-based method. Analysis methodology and results of the relationships between skid resistance, road characteristics and crashes are presented

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues

Reliability of optimal intervals for pavement strength data collection at the network level

In road asset management, knowledge of current condition and understanding of deterioration rates of pavement strength is essential input parameter for estimating fund allocations for maintenance and rehabilitation work. However, the cost of collecting data on road pavement strength is relatively high. In a previous pilot study, a procedure was developed for optimising longitudinal sampling intervals for collection of pavement strength data for use in network level of road asset management for the State of Queensland, Australia. The findings indicated that pavement strength data could be collected at 1000-meter intervals rather than at 200-meter intervals for a tropical region of northeast Queensland, Australia. This paper presents the results of the continuing research to assess the reliability of the usage of the 1000-metre interval pavement strength data in predicting budget estimates for road maintenance and rehabilitation at the network level. In the reliability assessment, the 95th percentile budget estimates were compared with the budget estimates calculated from 1000-metre interval pavement strength data. The results indicated that the differences between the 95th percentile budget estimates and the budgets estimated from the 1000-metre interval pavement strength data were less than four per cent for 10-,15-, 20- and 25-year budget estimates, and were approximately 12.25 per cent for 5-year periods

A probability method for assessing variability in budget estimates for highway asset management

There are inaccuracies in predicting maintenance and rehabilitation costs for road networks due to the variability and uncertainties in road network condition. To realistically predict maintenance and rehabilitation costs, stochastic characteristics of road network condition should be considered in the estimate. It may, however, not be feasible or practicable to include every single stochastic characteristic of road network conditions in the analysis. To explore this possibility in assessing variations in cost estimates, an analysis was conducted to identify input parameters that are critical for predicting road deterioration condition. Findings indicated that the variability in pavement strength significantly contributed to the variability of predicting road pavement deterioration. Based on this information, discrepancies in cost estimates due to the variability of pavement strength for road maintenance and rehabilitation were subsequently assessed. This paper presents the results of an analysis that was undertaken to identify critical input parameters for road pavement deterioration prediction. The paper also presents a probability method developed for assessing the variation in road maintenance and rehabilitation

A probability-based analysis for identifying pavement deflection test intervals for road data collection

This paper presents a probability-based method of goodness-of-fit test to determine spacing intervals for pavement deflection tests at the network level. One of the issues in road asset management is the high cost of collecting pavement deflection data at the network level. In a pilot study, Falling Weight Deflectometer Deflection tests were conducted on a 92-kilometer road length of a national highway in Queensland, Australia. The majority of the tests were performed at 200-meter spacing for both inner and outer wheel paths. A probability-based analysis using goodness-of-fit technique showed that the mean, standard deviation and probability distribution of deflection data collected at 1000-meter intervals were similar in value to the mean, standard deviation and probability distribution for data collected at 200-meter intervals. The findings indicated that pavement deflection data could be collected at 1000-meter intervals rather than at 200-meter intervals. This could result in a substantial decrease in the cost of data collection of road pavement deflection data while still achieving similar pavement performance prediction outcomes

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance

Identifying differences in safe roads and crash prone roads using clustering data mining

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilise in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of the method is to partition the road network into a set of groups (classes) based on common data and characterise the class crash types to produce a crash profiles for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support

A probability method for assessing variation in budget estimate for highway asset management

There are inaccuracies in predicting maintenance and rehabilitation costs for road networks due to the variability and uncertainties in road network condition. To realistically predict maintenance and rehabilitation costs, stochastic characteristics of road network condition should be considered in the estimate. It may, however, not be feasible or practicable to include every single stochastic characteristic of road network conditions in the analysis. To explore this possibility in assessing variations in cost estimates, an analysis was conducted to identify input parameters that are critical for predicting road deterioration condition. Findings indicated that the variability in pavement strength significantly contributed to the variability of predicting road pavement deterioration. Based on this information, discrepancies in cost estimates due to the variability of pavement strength for road maintenance and rehabilitation were subsequently assessed. This paper presents the results of an analysis that was undertaken to identify critical input parameters for road pavement deterioration prediction. The paper also presents a probability method developed for assessing the variation in road maintenance and rehabilitation

Reliability of optimal intervals for pavement strength data collection at the network level

In road asset management, knowledge of current condition and understanding of deterioration rates of pavement strength is essential input parameter for estimating fund allocations for maintenance and rehabilitation work. However, the cost of collecting data on road pavement strength is relatively high. In a previous pilot study, a procedure was developed for optimising longitudinal sampling intervals for collection of pavement strength data for use in network level of road asset management for the State of Queensland, Australia. The findings indicated that pavement strength data could be collected at 1000-meter intervals rather than at 200-meter intervals for a tropical region of northeast Queensland, Australia. This paper presents the results of the continuing research to assess the reliability of the usage of the 1000-metre interval pavement strength data in predicting budget estimates for road maintenance and rehabilitation at the network level. In the reliability assessment, the 95th percentile budget estimates were compared with the budget estimates calculated from 1000-metre interval pavement strength data. The results indicated that the differences between the 95th percentile budget estimates and the budgets estimated from the 1000-metre interval pavement strength data were less than four per cent for 10-,15-, 20- and 25-year budget estimates, and were approximately 12.25 per cent for 5-year periods

A probability-based approach for assessing the relationship between road crashes and road surface conditions

Skid resistance is a condition parameter characterising the contribution that a road makes to the friction between a road surface and a vehicle tyre. Studies of traffic crash histories around the world have consistently found that a disproportionate number of crashes occur where the road surface has a low level of surface friction and/or surface texture, particularly when the road surface is wet. Various research results have been published over many years and have tried to quantify the influence of skid resistance on accident occurrence and to characterise a correlation between skid resistance and accident frequency. Most of the research studies used simple statistical correlation methods in analysing skid resistance and crash data.----- ------ Preliminary findings of a systematic and extensive literature search conclude that there is rarely a single causation factor in a crash. Findings from research projects do affirm various levels of correlation between skid resistance and accident occurrence. Studies indicate that the level of skid resistance at critical places such as intersections, curves, roundabouts, ramps and approaches to pedestrian crossings needs to be well maintained.----- ----- Management of risk is an integral aspect of the Queensland Department of Main Roads (QDMR) strategy for managing its infrastructure assets. The risk-based approach has been used in many areas of infrastructure engineering. However, very limited information is reported on using risk-based approach to mitigate crash rates related to road surface. Low skid resistance and surface texture may increase the risk of traffic crashes.----- ----- The objectives of this paper are to explore current issues of skid resistance in relation to crashes, to provide a framework of probability-based approach to be adopted by QDMR in assessing the relationship between crash accidents and pavement properties, and to explain why the probability-based approach is a suitable tool for QDMR in order to reduce accident rates due to skid resistance