01031541

Component

https://doi.org/10.3141/1968-10

http://scholar.google.com...avis&publication_year=2006

Because of growing concern over traffic safety and rising congestion costs, recent research efforts have been redirected from the traditional reactive traffic management (crash detection and clearance) toward on-line proactive solutions for crash prevention. Such a solution for high-crash areas is explored by the identification of the most relevant real-time traffic metrics and the incorporation of them in a model to estimate crash likelihood. Unlike earlier attempts, this model is based on a unique detection and surveillance infrastructure deployed on the freeway section that has the highest crash rate in Minnesota. State-of-the-art infrastructure allowed the video capture of 110 live crashes, crash-related traffic events, and contributing factors while measuring traffic variables (e.g., individual vehicle speeds and headways) over each lane in several places in the study area. This crash-rich database was combined with visual observations and analyzed extensively to identify the most relevant real-time traffic measurements for detecting and developing an on-line model of crash-prone conditions. This model successfully establishes a relationship between quickly evolving real-time traffic conditions and crash likelihood. Testing was performed in real time during 10 days not previously used in model development, under varied weather and traffic conditions. The crash likelihood model—and, in turn, the detection algorithm—succeeded in detecting 58% of the crashes, with a 6.8 false decision rate.

01037954

English

pp 83-91

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099773

Print

Figures (2) ; References (34) ; Tables (2)

Algorithms; Freeways; High risk locations; Real time information; Traffic safety; Video imaging detectors

Observations; Proactive assessment; Video technology

Minnesota

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Mar 3 2006 11:03AM

• Application of Genetic Neural Networks to Real-Time Intersection Accident Detection Using Acoustic Signals
• Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management
• Forecasting Shared-Use Vehicle Trips with Neural Networks and Support Vector Machines
• Frameworks to Represent Uncertainty When Level of Service Is Determined
• Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method
• Incorporating Wavelet Decomposition Technique to Compress TransGuide Intelligent Transportation System Data
• Kalman Filter-Based Tracking System for Automated Inventory of Roadway Signs
• Moments Analysis for Improving Decision Reliability Based on Travel Time
• Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys
• Predicting Directional Design Hourly Volume from Statutory Holiday Traffic
• Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters
• Travel Time Estimation with Correlation Analysis of Single-Loop Detector Data
• Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data