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Traffic Performance Measurement Using High-Resolution Data –
The SMART-SIGNAL System
Dr. Henry Liu
Department of Civil EngineeringUniversity of Minnesota — Twin Cities
March 1, 2011
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An automatic and continuous data collection system from existing traffic signals
A performance measurement system for intersection queue length and arterial travel time, especially under congested traffic conditions
A performance tuning system for optimization of traffic signal parameters
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SMART-SIGNAL System Architecture
DetectorsSignal
Local Data Collection Unit
Data Server at Master Cabinet
... ...
FIELD
DetectorsSignal
Local Data Collection Unit
DetectorsSignal
Local Data Collection Unit
TMC
DatabasePreprocessed Data
Performance Measures
DSL Communication
Road Travelers
Traffic Engineers
USERSMonitor Diagnosis Fine-tuning Travel Decision
Direct/Internet Access Internet Access
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Terminal Box
DAC
Data Collection
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SDLC
Serial Port #1
Serial Port #2
Power
Ethernet
Plug-and-Play Device for TS2 Cabinet
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http://signal.umn.edu
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11 intersections on France Ave. in Bloomington (March 07 – June 09)
6 intersections on TH55 in Golden Valley (Feb. 08 – Sept. 09)
3 intersections on PCD in Eden Prairie (Current)
6 intersections in the City of Pasadena, California (Iteris, Spring 2011, On-going)
14 intersections on TH13 (Spring 2011, Expected)
10 intersections on TH55 (Spring 2011, Expected)
SMART-Signal Implementation Sites
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Prairie Center Dr. and Technology Dr. (Eden Prairie)
Feb 4, 2010
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Queue length estimation– Delay, Level of Services, number of stops
Identification of oversaturated conditions– Oversaturation Severity Index (OSI)
Travel time estimation– Personal trip delay, number of stops, carbon
footprint on travel
Developed Algorithms
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Vehicle classification / speed estimation– Both arterial and freeway applications
Queue length / travel time prediction– Modeling of Arterial Traffic Flow
Fine-tuning signal timing parameters– Offsets, Green Splits, “Break Points” for time of day
…
Algorithms Under Development
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Lessons Learned from SMART-SIGNAL
Although traffic is traditionally modeled as “continuous flow”, traffic, after all, is discrete.
Measuring traffic flow parameters using the data collected at the individual vehicle level
Don’t aggregate data before useful information being derived
Technological advances support such data collection at affordable prices
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Publications Liu, H. and Ma, W., (2009) A virtual vehicle probe
model for time-dependent travel time estimation on signalized arterials, Transp. Res. Part C, 17(1), 11-26.
Liu, H., Wu, X., Ma, W., and Hu, H., (2009) Real-Time queue length estimation for congested signalized intersections, Transp. Res. Part C, 17(4), 412-427.
Wu, X., Liu, H. and Gettman, D. (2010) Identification of Oversaturated Intersections Using High-Resolution Traffic Signal Data, Transp. Res. Part C, 18(4), 626-638.
Wu, X., Liu, H, and Geroliminis, N. (2010) An Empirical Analysis on the Arterial Fundamental Diagram, Transp. Res. Part B, 45, 255-266.
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Acknowledgements
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THANK YOU!
Dr. Henry Liu612-625-6347henryliu@umn.edu
SMART-Signal Web Site: http://signal.umn.edu
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