01334320

Component

https://doi.org/10.3141/2237-07

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

In-ground loop detectors have recently been used by many researchers to investigate the links with real-time crash risk and the traffic data. An issue that has been raised, but not explicitly addressed in these studies, is how the results from one freeway might transfer to another. A study was done to examine the relationship between crash risk and real-time traffic variables from a freeway corridor (eastbound I-4 in Orlando, Florida) and then to apply the models to three other freeway corridors (westbound I-4 and northbound and southbound I-95). Traffic data used in the study were collected with loop detectors as well as radar detectors already installed on these freeways. The traffic information was collected for crash as well as random noncrash cases so that a binary classification approach could be adopted. The random forest–based models provided a list of significant variables based on the average reduction in the Gini indices to the overall forest classification. The periods between 5 and 10 min before and between 10 and 15 min before the crash were taken into consideration so that these models could provide the crash risk in advance. Average occupancy of upstream station and average speed and coefficient of variation of volume for downstream stations were found to have a significant effect on crash risk. Application of multilayer perceptron neural network models showed that although the model developed for the I-4 corridor works reasonably well for the westbound I-4 corridor, the performance was not as good for the I-95 sections. This observation indicates that the same model for crash risk identification may work only for corridors with very similar traffic patterns.

01365008

11-1645

English

0000-0002-3456-7932

pp 60-66

2011

Transportation Research Record: Journal of the Transportation Research Board

9780309222914

Print

Figures (3) ; References (11) ; Tables (5)

Crash data; Crash locations; Crash risk forecasting; Detectors; Freeways; Highway corridors; Highway safety; Neural networks; Real time information; Traffic crashes

Orlando (Florida)

Data and Information Technology; Highways; Safety and Human Factors; I73: Traffic Control; I82: Accidents and Transport Infrastructure

TRIS, TRB

Feb 17 2011 5:52PM

• 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
• 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
• Mining Microscopic Data of Vehicle Conflicts and Collisions to Investigate Collision Factors
• 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