Presentation on theme: "Department of Civil Engineering University of Minnesota — Twin Cities"— Presentation transcript:
1 Department of Civil Engineering University of Minnesota — Twin Cities Traffic Performance Measurement Using High-Resolution Data – The SMART-SIGNAL SystemDr. Henry LiuDepartment of Civil EngineeringUniversity of Minnesota — Twin CitiesMarch 1, 2011
2 SMART-Signal: Systematic Monitoring of Arterial Road Traffic Signals An automatic and continuous data collection system from existing traffic signalsA performance measurement system for intersection queue length and arterial travel time, especially under congested traffic conditionsA performance tuning system for optimization of traffic signal parameters2
3 SMART-SIGNAL System Architecture DetectorsSignalDetectorsSignalDetectors......Local Data Collection UnitLocal Data Collection UnitLocal Data Collection UnitData Server at Master CabinetFIELDDSL CommunicationDatabasePreprocessed DataPerformance MeasuresTMCDirect/Internet AccessInternet AccessTraffic EngineersRoad TravelersMonitorDiagnosisFine-tuningTravel DecisionUSERS
7 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
8 Prairie Center Dr. and Technology Dr. (Eden Prairie) Feb 4, 2010
9 Developed Algorithms Queue length estimation Delay, Level of Services, number of stopsIdentification of oversaturated conditionsOversaturation Severity Index (OSI)Travel time estimationPersonal trip delay, number of stops, carbon footprint on travel9
10 Algorithms Under Development Vehicle classification / speed estimationBoth arterial and freeway applicationsQueue length / travel time predictionModeling of Arterial Traffic FlowFine-tuning signal timing parametersOffsets, Green Splits, “Break Points” for time of day …10
11 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 levelDon’t aggregate data before useful information being derivedTechnological advances support such data collection at affordable pricesTechnological 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.
12 PublicationsLiu, 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 flowField 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.