01519728

Component

Rainy weather and wet roads are considered hazardous conditions for driving. Countermeasures should be taken to reduce the risks of driving in such conditions, but measuring the added risk and key crash contributing factors under such conditions is very challenging. With a humid subtropical climate, the annual precipitation in Louisiana is about 64 inches, twice above the national average. Approximately 11% of total crashes in Louisiana happened during rainy weather, and nearly 25% of total fatal crashes happen in rainy weather annually. Reducing the number of crashes and crash severity is critical to the state “Zero Deaths Destination” highway safety strategies. The data mining technique is becoming immensely popular in dealing with huge dataset. It helps to identify the hidden patterns from a large and complex database, which is why these methods are being utilized in diversified areas. There are many data mining techniques that have been applied to traffic crash data analysis in the recent past. However, very little research work utilizes the association rules mining technique to discover knowledge from the traffic crash dataset. This data mining technique generates simple rules that introduce the association between different factors. This paper demonstrates how to apply this data mining methods to discover hidden patterns in rainy weather crash data with eight years of Louisiana data (2004-2011). No dependent variable is developed in this exploratory application contrary to many popular safety performance models. The findings of the research shows that ‘single vehicle run-off crashes’ is the most frequent item in rainy weather. This crash type is particularly associated with a few roadway features like ‘on grade-curve’ aligned roadways, curved roadways, and roadways with no streetlights at night. In rainy weather, Property Damage Only (PDO) and sideswipe (same direction) crashes are also significant in numbers. Moderate injuries are dominant in single vehicle crashes. Roadways with poor illumination are associated with straight level aligned roadways in rainy weather crashes. Young drivers (15-24) are vulnerable in run-off crashes when the roadways had poor illumination and are curve-aligned. The findings will help the highway authorities to determine countermeasure selection and safety improvement.

This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation.

01503729

14-1540

English

19p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; References; Tables

Countermeasures; Crash data; Crash risk forecasting; Data mining; Highway safety; Rain; Traffic crashes; Weather conditions

Louisiana

Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning; I80: Accident Studies

Transportation Research Board Annual Meeting 2014 Paper #14-1540

TRIS, TRB, ATRI

Jan 27 2014 2:34PM