01337995

Component

https://doi.org/10.3141/2237-05

http://scholar.google.com...gard&publication_year=2011

Road collisions lead to great human and financial costs for society. Although some progress has been made, this worldwide issue needs more attention, as the costs increase. Proactive methods for road safety analysis that do not depend on collision occurrences are needed. Collection and analysis of microscopic data (road user trajectories) about all traffic events with and without a collision are the only ways to gain insight into collision factors and processes; that is, the chains of events that lead to collisions. The first phase of the project reported in this paper used microscopic data extracted from video sensors and data mining techniques to identify patterns in the traffic event database. Decision trees, the k-means algorithm, and the hierarchical agglomerative clustering method were used to analyze the relationship between interaction attributes and outcome (collision or not) and identify groups of interactions with similar attributes. This approach was demonstrated on a data set collected in Kentucky of 295 traffic events and contained 213 conflicts and 82 collisions. The decision tree confirmed the importance of evasive action in the interaction outcome. Three clusters were found from speed indicators extracted from road users’ trajectories: the cluster containing the fewest collisions had the lowest speeds of the three. This result hints at the existence of conflicts that are dissimilar from most collisions and may therefore not be suitable for surrogate safety analysis.

01365008

11-0117

English

pp 41-50

2011

Transportation Research Record: Journal of the Transportation Research Board

9780309222914

Print

Figures (7) ; Photos (2) ; References (33) ; Tables (1)

Crash analysis; Crash causes; Crashes; Data mining; Microscopic traffic flow; Safety factors; Traffic conflicts; Traffic crashes

Surrogate measures of safety

Kentucky

Data and Information Technology; Highways; Safety and Human Factors; I80: Accident Studies

TRIS, TRB

Feb 17 2011 5:20PM

• Accident Prediction Models for Winter Road Safety: Does Temporal Aggregation of Data Matter?
• Analysis of Crash Rates and Surrogate Events: Unified Approach
• Analysis of Speeding Behavior: Multilevel Modeling Approach
• Data-Mining Techniques for Exploratory Analysis of Pedestrian Crashes
• Effect of Arterial Signal Coordination on Safety
• Estimation of Real-Time Crash Risk: Are All Freeways Created Equal?
• Fully Bayesian Approach to Investigate and Evaluate Ranking Criteria for Black Spot Identification
• High-Resolution Detector and Signal Data to Support Crash Identification and Reconstruction
• Identifying Crash Distributions and Prone Locations by Lane Groups at Freeway Diverging Areas
• Improved Time-to-Collision Definition for Simulating Traffic Conflicts on Truck-Only Infrastructure
• Methodologies for Aggregating Indicators of Traffic Conflict
• Modeling Highway Safety and Simulation in Rainy Weather
• Performance of Safety Indicators in Identification of Black Spots on Two-Lane Rural Roads
• Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency
• Surrogate Safety Measures for Optimizing Investments in Local Rural Road Networks
• U.S. National Household Travel Survey Used to Validate Exposure Estimates by the Quasi-Induced Exposure Technique