Benefit

Dynamic ramp metering strategies designed to actively counter developing bottlenecks can reduce vehicle delay up to 48 percent.

Experience using real-time traffic data to improve ramp metering and mainline performance on Highway-100 in Minneapolis, Minnesota.


06/01/2015
Hwy100NB; Minneapolis; Minnesota; United States


Summary Information

A new metering strategy was implemented on Highway-100 Northbound (100NB) in Minneapolis, Minnesota. The strategy activated metering controls for each ramp depending on mainline conditions and the current traffic demand at a given ramp.

To assess system impacts, traffic performance on the mainline and ramp were analyzed before and after the system was implemented. Traffic data were collected during weekday afternoon peak periods (Tuesdays, Wednesdays, and Thursdays). To account for seasonal variations in traffic demand, the performance of the new metering strategy was compared with that of the old strategy during the same months of the previous year.

FINDINGS

Results of the field test indicated substantial improvements in both mainline and ramp traffic performance as compared to the old strategy that used a stratified algorithm. By dynamically configuring the bottleneck-based zone structure to be controlled in real-time, the new strategy did not require pre-specified associations between ramps and potential bottlenecks, thus increasing its flexibility in dealing with incidents or unexpected events. Further, the turn-on/off times of each ramp meter were automatically determined with consideration given to the mainline traffic states. The results below were excerpted from the source report.

Impacts on Travel Delay


Before/After Comparison of Total Delayed Vehicle Hours Traveled
October-November (2011-Before vs 2012-After)

Before
After
Change
Average
432.3359
224.5156
-48.1%
Variance
46174.83
20186.8
-56.3%
Stddev
214.8833
142.0802
-33.9%

Before/After Comparison of Total Delayed Vehicle Hours Traveled
April-May (2012-Before vs 2013-After)
Before
After
Change
Average
457.6556
377.9004
-17.4%
Variance
23668.66
37924.77
60.2%
Stddev
153.8462
194.7428
26.6%

Before/After Comparison of Total Delayed Vehicle Hours/Total Entered Vehicles
October-November (2011-Before vs 2012-After)
Before
After
Change
Average
0.017575
0.008942
-49.1%
Variance
8.03E-05
3.55E-05
-55.7%
Stddev
0.00896
0.005961
-33.5%

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Source

Development of Active Traffic Management Strategies for Minnesota Freeway Corridors

Author: Kwon, Eil and Chongmyung Park

Published By: Minnesota DOT

Source Date: 06/01/2015

Other Reference Number: Report No. MN/RC 2015-26

URL: http://www.dot.state.mn.us/research/TS/2015/201526.pdf

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Goal Areas

Mobility

Typical Deployment Locations

Metropolitan Areas

Keywords

ramp meters

Benefit ID: 2017-01136