01551789

Component

https://doi.org/10.3141/2514-01

A large portion of crashes occur at intersections, and most such crashes are associated with driver mistakes. Severe mistakes may lead to serious injuries; therefore, it is necessary to investigate the factors that contribute to driver error and how those factors influence driver behavior. More information on these contributing factors can help researchers develop cost-effective countermeasures that might help mitigate driver error. The primary objective of this study was to examine key contributors to driver error that took place at uncontrolled, sign-controlled, and signalized intersections. An ordered-probit statistical model and a data-mining technique called "association rules" were implemented to explore these relationships. The results of both approaches were consistent. Association rules were found to be capable of discovering patterns in the data that could not be found in the ordered-probit statistical model. The secondary objective of this study was to provide new insights on how to improve intersection safety by adding to the knowledge regarding the contributing factors of those driver errors. Most, if not all, errors are related to human factors; thus, they can effectively be corrected through a holistic approach that involves engineering, enforcement, and education.

01590073

15-1575

English

pp 1–9

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369367

Print

References (35) ; Tables (6)

Countermeasures; Driver errors; High risk locations; Human factors in crashes; Intersections

Highways; Safety and Human Factors; I83: Accidents and the Human Factor

PRP, TRIS, TRB, ATRI

Dec 30 2014 12:35PM

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• Assessing Safety Improvements to Pedestrian Crossings Using Automated Conflict Analysis
• Assessment of Pedestrian Risk at Crossings with Kinematic–Probabilistic Model
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• Copula-Based Joint Model of Injury Severity and Vehicle Damage in Two-Vehicle Crashes
• Crash Patterns at Signalized Intersections
• Effects of Geodemographic Profiles of Drivers on Their Injury Severity from Traffic Crashes Using Multilevel Mixed-Effects Ordered Logit Model
• Evaluation of Signalized-Intersection Crash Screening Methods Based on Distance from Intersection
• Fault Determination for Crashes in Vermont: Implications of Distance from Home
• Identifying Optimal High-Risk Driver Segments for Safety Messaging: Geodemographic Modeling Approach
• Level of Service of Safety Revisited
• Longitudinal Analysis of Rural Interstate Fatalities in Relation to Speed Limit Policies
• Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas
• Predicting Crashes on Expressway Ramps with Real-Time Traffic and Weather Data
• Predicting Driver Injury Severity in Single-Vehicle and Two-Vehicle Crashes with Boosted Regression Trees
• Simulated Traffic Conflicts: Do They Accurately Represent Field-Measured Conflicts?
• Understanding Factors Affecting Frequency of Traffic Conflicts Between Electric Bicycles and Motorized Vehicles at Signalized Intersections
• Validation of Crash Modification Factors Derived from Cross-Sectional Studies with Regression Models