24, March 2014 Queue Length Estimation Based on Point Traffic Detector Data and Automatic Vehicle Identification Data.

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Presentation transcript:

24, March 2014 Queue Length Estimation Based on Point Traffic Detector Data and Automatic Vehicle Identification Data

Contents Detector Data AVI data Case Study 1 Case Study 2 Methods

FIU/UCF Joint Project  Joint project between FIU and UCF  Mohammed Hadi, Ph.D., P.E. (PI)  Haitham Al-Deek, Ph.D., P.E. (UCF PI)  Omer Tatari, Ph.D (Co-PI)  Yan Xiao (Co-PI)  Somaye Fakharian (FIU Ph.D. student)  Frank A. Consoli, P.E. (UCF Ph.D. Student)  John Rogers, P.E. (UCF Ph.D. Student)

Progress Diagram Phase 1 Phase 2 Phase 3 Turnpike, 1.8 Mile 4 detectors All methods Real World Data SR 826(0.947, Detectors,0311 mile Simulation (CORSIM) Real world Comparison of simulation and real world Estimation of Density..

Detector Data  Speed Based  Base on speed, the link is  1: Fully congested, if the speed based on both detectors< 40 M/Hr  2: Uncongested: if the speed based on both detectors >= 40  3: Partially Congested: If the speed based on one detector < 40  Volume Based  Arrival vehicle > Departure Vehicle

AVI Data  Speed Based  1: Average Speed for the segment < 40 is totally congested  2: Average Speed>= 40 is totally Uncongested

Combination of Detector and AVI Data  Speed Based  1: For fully congested(based on Detector), average speed based on AVI  2: For totally uncongested (based on Detector): average speed based on AVI  3: Partially Congested: base on relationship sped and travel time, the partial queue is estimated.

Combination of Detector and AVI Data  Travel Time Based  1: For fully congested(based on AVI), average travel time is calculated  2: For totally uncongested (based on AVI): average travel time is calculated  3: Partially Congested: the linear regression for each travel time between fully congested and fully uncongested.

Methods For Queue Length Estimation AVI (Average Speed) Combination of AVI and Detector(Travel Time and Speed) Detector (Average Speed and Volume) Queue Length Estimation

Case Study 1: (Turnpike)

Table (Turnpike)

Diagram (Cumulative Volume)

Diagram (Volume)

Case Study 2: (SR 826- Simulation CORSIM)  Based on simulation  Sensitivity multiplier by 200%  Two link are congested  Link 1: o.48 mile  Link 2: 0.46  Detector 1: mile from upstream  Detector 2: 0.23

Case Study 2: (SR 826- Simulation CORSIM)

Case Study 2: (SR 826- Real world  Based on detector data  Based on AVI data, matching each individual vehicle from link 1 to link 2  All methods are used for Turnpike(Detector, AVI, Combination of detector and AVI)  Cumulative volume (Based on detector data)

Case Study 2: (SR 826- Real World)

Diagram (Cumulative Volume)

Case Study 2: (SR 826- Comparison of Simulation and Real World)

Density Estimation

Question? AVIDetector Combination