Title

Temporal Variations In Traffic Flow And Ramp-Related Crash Risk

Abstract

This study proposes the method of predicting the temporal variation in crash risk on freeway ramps and at the intersections of ramps (the junction of ramps with crossroads). Using a 5-year ramp-related crash data on a freeway in Orlando, Florida, the study develops probabilistic models that relate the frequency of crashes with the factors such as ramp type (off-ramps and on-ramps), ramp configuration, daily and hourly ramp traffic volume, and 5-minute average speed and volume in the mainline close to ramps 5∼10 minutes prior to the time of crashes. The study estimates crash rates (the expected number of crashes divided by ramp traffic volume) as surrogate measures of ramp-related crash risk using log-linear models. The parameters of the models showed that the frequency of ramp-related crashes generally increased with ramp traffic volume and crash rates were higher on off-ramps than on-ramps. It was also found that higher volume and lower speed at the locations near ramps significantly contributed to higher crash risk. Considering the temporal variation in traffic condition, it was found that crash rates were higher during dawn (12∼6 am) compared to the other time periods of day and they were higher on loop and outer connection ramps than diamond ramps. The study demonstrates how the models can be applied to freeway traffic management in predicting temporal variation in ramp-related crash risk based on ramp geometric and traffic flow characteristics. Findings of the study suggest some traffic control strategies that may mitigate high ramp-related crash risk. Copyright ASCE 2006.

Publication Date

12-1-2006

Publication Title

Applications of Advanced Technology in Transportation - Proceedings of the Ninth International Conference on Applications of Advanced Technology in Transportation

Number of Pages

244-249

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1061/40799(213)40

Socpus ID

35448938095 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/35448938095

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