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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