01479214

Component

https://doi.org/10.3141/2396-07

http://scholar.google.com...ojas&publication_year=2013

A number of intelligent transportation system technologies are deployed for disseminating information to travelers. One of the most important parameters for assessing the impacts and benefits of these deployments that influence route selection is the diversion rates of travelers. In this research, a method was developed to estimate traffic diversions on the basis of main-line detector data without the need for off-ramp detectors. In addition, the estimation allows the assessment of the guidelines and procedures for disseminating information and evaluating the impacts on alternative routes. The validity of the developed methodology was verified by comparing the estimated values with real-world data. Case studies of the developed method indicated that the average diversion rate was about 10% to 35% for three-lane and four-lane freeways, depending on the number of lanes blocked. A linear relationship between the average diversion rate and the lane blockage ratio was also developed; this relationship indicated that there was a general trend of increase in diversion with the increase in the lane blockage ratio.

01519063

13-3786

English

pp 54–60

2013

Transportation Research Record: Journal of the Transportation Research Board

9780309294836

Print

Figures (5) ; References (18) ; Tables (2)

Driver information systems; Incident detection; Lane closure; Traffic data; Traffic diversion; Traffic incidents

Data and Information Technology; Highways; Operations and Traffic Management; I72: Traffic and Transport Planning

PRP, TRIS, TRB, ATRI

Feb 5 2013 12:44PM

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• Self-Learning Adaptive Ramp Metering: Analysis of Design Parameters on a Test Case in Toronto, Canada
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• Smart Event Traffic Management: Impact on the Central Florida Regional Transportation Network and Lessons Learned
• Synthesizing Route Travel Time Distributions from Segment Travel Time Distributions
• Traffic Queue Estimation for Metered Motorway On-Ramps Through Use of Loop Detector Time Occupancies
• Use of Sensor Data to Identify Secondary Crashes on Freeways