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Department of Civil Engineering University of Minnesota — Twin Cities
Traffic Performance Measurement Using High-Resolution Data – The SMART-SIGNAL System Dr. Henry Liu Department of Civil Engineering University of Minnesota — Twin Cities March 1, 2011
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SMART-Signal: Systematic Monitoring of Arterial Road Traffic Signals
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 2
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SMART-SIGNAL System Architecture
Detectors Signal Detectors Signal Detectors ... ... Local Data Collection Unit Local Data Collection Unit Local Data Collection Unit Data Server at Master Cabinet FIELD DSL Communication Database Preprocessed Data Performance Measures TMC Direct/Internet Access Internet Access Traffic Engineers Road Travelers Monitor Diagnosis Fine-tuning Travel Decision USERS
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Terminal Box Data Collection DAC
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Plug-and-Play Device for TS2 Cabinet
Serial Port #1 SDLC Ethernet Serial Port #2 Power Plug-and-Play Device for TS2 Cabinet
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http://signal.umn.edu 32-bit Micro Controller Ethernet Interface
File Storage Interface Debug Interface for Software Updating
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SMART-Signal Implementation Sites
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) 7
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Prairie Center Dr. and Technology Dr. (Eden Prairie)
Feb 4, 2010
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Developed Algorithms 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 9
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Algorithms Under Development
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 … 10
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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 Technological advances support such data collection at affordable prices, so there is no technical barriers. The individual vehicle level means that we measure each vehicle’s response to a detector. For example, for a loop detector, we want the information related to when a vehicle touches the detector and when the vehicle leaves the detector. Here I don’t mean vehicle signature, where the detection resolution is even higher.
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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), 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), Wu, X., Liu, H. and Gettman, D. (2010) Identification of Oversaturated Intersections Using High-Resolution Traffic Signal Data, Transp. Res. Part C, 18(4), Wu, X., Liu, H, and Geroliminis, N. (2010) An Empirical Analysis on the Arterial Fundamental Diagram, Transp. Res. Part B, 45, New data collection techniques open doors to further investigation of arterial traffic flow Field has been well-studied before, including our own colleague Panos Michalopoulos. While Panos opted to develop new sensors, I stick to the existing and see how we can get more out of this.
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Acknowledgements
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THANK YOU. Dr. Henry Liu 612-625-6347 henryliu@umn
THANK YOU! Dr. Henry Liu SMART-Signal Web Site:
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