A correlation between pavement skid resistance and wet-pavement related accidents in Thailand.

Abstract

Abstract. This research aims to study a correlation between a pavement skid resistance and wet-pavement related accidents in order to determine the minimum friction threshold so called 'Investigatory Level (IL)' for the roads in Thailand. An accident database, a skid resistance database and a traffic volume of road network from the department of Highways, totalling 19 routes and 386kilometers, were used in the analysis of this study. In the analysis, 500-meter subsection intervals are used to determine a correlation between an average pavement skid resistance and wet-pavement accident rate using a non-linear regression analysis model. It was discovered that the pavement skid resistance has a major influence on the accident rate, depending on various types of road geometry. Moreover, the preliminary investigatory level is determined by using a past accident rate information as a reference for an investigatory level basis. We discover that the single carriageway (non-event) road is at highest threat due to low pavement skid resistance. The recommended investigatory level for each of the five road geometries all site, single carriageway, dual carriageway, horizontal alignment and curve road categories are 035, 0.50, 0.30, 0.30, and 0.40, respectively, which are in accordance to the investigatory level values in other countries.

Factors Affecting Road Traffic: Identifying Drivers of Annual Average Daily Traffic Using Least Absolute Shrinkage and Selection Operator Regression

“…The variable is also statistically significant for the majority of vehicle types in “B” and “C” roads, although it is not significant for “U” roads, because there are few dual carriageways in these roads. More vehicles are expected to drive through dual carriageways—as opposed to single—because of safety and increased speed limits ( 60 – 62 ) and lower design standards for single carriageways ( 63 ). Studies have shown that accidents are less likely to occur on dual carriageways ( 64 , 65 ), while the occurrence of these roads is key, particularly in urban environments, because they normally reduce the travel time of individuals and, therefore, provide access to significant services such as schools, workplaces, and healthcare facilities ( 66 ).…”

Application of Machine Learning Models to the Analysis of Skid Resistance Data

“…The investigation of skid resistance prediction from a classification perspective involves splitting the target data (friction coefficient values) into three classes (Figure 5), with threshold values chosen based on the literature [29][30][31]:…”

“…The distributions of the three classes (low-medium-high) between the training (71 samples) and the testing (31 samples) data set are presented in Figure 6 and summarized in Table 2. The investigation of skid resistance prediction from a classification perspective involves splitting the target data (friction coefficient values) into three classes (Figure 5), with threshold values chosen based on the literature [29][30][31]:…”

Review of Factors Controlling Skid Resistance at Tire‐Pavement Interface

“…Kuttesch [11] quantified the relationship between skid resistance and wet weather accidents and reported that the wet accident rate is on average 44% higher for sites with skid number less than or equal to 30 than for sites with skid number greater than 30. Saplioglu et al [12] investigated the effect of skid resistance on accidents that occurred at an urban intersection and found an increase of 70% in accident rate when the skid resistance number reduces from 53.5 to 38. e wet accident rate is inversely proportional to the skid resistance for all kinds of carriageways [13,14]. Skid resistance is influenced by various factors of pavement surface, tire characteristics, and environmental-related parameters.…”