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Enhanced Monitoring and Planning of Network Infrastructure with Remote Data Collection Progress Report Mark Hickman The University of Arizona Pitu Mirchandani.

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Presentation on theme: "Enhanced Monitoring and Planning of Network Infrastructure with Remote Data Collection Progress Report Mark Hickman The University of Arizona Pitu Mirchandani."— Presentation transcript:

1 Enhanced Monitoring and Planning of Network Infrastructure with Remote Data Collection Progress Report Mark Hickman The University of Arizona Pitu Mirchandani Arizona State University March 10, 2011

2 Outline 1.Brief Scope of Project 2.Status of Task 1 3.Status of Task 2 4.Status of Task 3

3 Brief Scope of Project Three Major Tasks: 1.Data fusion of simultaneous data collected from airborne and ground sensors for infrastructure monitoring 2.Use of remotely collected data for developing better models for network planning and emergency operations 3.Develop tools and enhance “enabling” technologies for airborne data collection. Brief Scope Task 1 Task 2 Task 3

4 Potential Benefits of Research Availability of affordable technologies to do the above for routine and commercial applications. Better calibration of infrastructure planning models: weaving, estimating queue lengths, traffic impacts of development, bottleneck analysis, etc. Real-time estimation of network conditions using multiple data sources including airborne sensors Logistics for airborne systems for real-time monitoring and incident/disaster management. Brief Scope Task 1 Task 2 Task 3

5 Project Team PI’s Mark Hickman (UA) Pitu Mirchandani (ASU) Ron Askin (ASU) Yi- Chang Chiu (UA) Other Researchers David Lucas (Research Engineer, ASU, MALTA related research) Xueyan Du (RA, UA, TRAVIS–related ) Xianbiao Hu (RA, UA, MALTA- related) Peng Sun (RA, ASU, TRAVIA-related) Zouyang Zhou (RA, ASU, data fusion)) Brief Scope Task 1 Task 2 Task 3

6 Technical Advisory Board Tom Buick – Consultant (was with Maricopa DOT) Dave Gibson – FHWA Turner Fairbanks Greg Jordan – Skycomp Inc Sarath Joshua –MAG (Maricopa planning agency) Jane Lappin – Volpe Center, USDOT Scott Nodes – Arizona DOT Jim Schoen – Kittelson Inc. Aichong Sun – PAG (Pima planning agency) Dale Thompson – FHWA Turner Fairbanks (First TAB meeting was held April 27, 2010) Brief Scope Task 1 Task 2 Task 3

7 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Major focus on Data Collection and Data Fusion. Existing data sources: 1-sec loop data from City of Tucson, City of Tempe, Maricopa County DOT Video data from Living Laboratory (City of Tucson) Historical travel times on freeways and major arterials from probe vehicles, from MAG and PAG congestion studies Historical traffic volumes on freeways and major arterials, from MAG and PAG count programs Probe vehicle data from buses, from Sun Tran (City of Tucson) and Valley Metro (City of Tempe) Task 1.1: New Simultaneous Data Collection 20 hours of flight time (10 hours per year) for airborne surveillance on Speedway and I-10 in Tucson and in Tempe and US 60 in Phoenix GPS based tracking during airborne data collection Brief Scope Task 1 Task 2 Task 3

8 Data Collection Technologies Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data

9 Brief Scope Task 1 Task 2 Task 3

10 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Videos: Swan and Speedway Intersection I-10 / Miracle Mile Brief Scope Task 1 Task 2 Task 3

11 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Task 1.2: Develop Model-based Statistical Approaches to estimate (off-line data fusion) Flows Speeds Queue lengths on arterials Densities on freeways. Calibrate model with ground truth Develop software tool based on data fusion approach Brief Scope Task 1 Task 2 Task 3

12 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Brief Scope Background Task 1 Task 2 Task 3 Role of Partners Objective: Estimate system state represented by  (density) and v (speed) in each cell of the freeway segment Measurements w/o remote sensing: We may get detector measurements of flow (r) and speed (v) at the positions of the dark boxes. Kalman-type filter: Ref: Wang and Papageorgiou (2005)

13 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Macroscopic model: Second order traffic flow model known as MetaNet model [validated by Papageorgiou in Paris, 1989]

14 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Convert boundary variables d(k) and model parameters p(k) into state variables

15 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data PM Example: Estimation at a detection point [ref: Wang and Papageorgiou, 2005] Brief Scope Task 1 Task 2 Task 3

16 Data from airborne sensors Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Brief Scope Task 1 Task 2 Task 3

17 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Data fusion architecture with airborne data Brief Scope Task 1 Task 2 Task 3

18 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Data fusion architecture with airborne data o Measurements: trajectories through remote sensing (speed, density, spacing,..) o System state: speed, density o The spacing usually represents the safety distance related to speed. o The spacing and density are related. o A new mesoscopic model will be used only to process the remote image area, the rest of the freeway is still estimated using the macroscopic model

19 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Task 1.3: Additional Data Collection Task 1.4 : Develop Model-based Statistical Approaches for real-time estimation Task 1.5: Mobile platform routing and scheduling logistics Task 1.6: Logistics of mobile sensors, considering placement of new fixed detectors TO DO NEXT Brief Scope Task 1 Task 2 Task 3

20 Task 1: Near Real-Time Traffic Monitoring with Ground-based and Remotely Sensed Data Develop routing and scheduling logistics for mobile platforms With current fixed sensors With new fixed sensors (include location decisions in logistics model) Testing and demonstration of algorithms: Simulate traffic network using DynusT. Monitor density and speed on each link i If d i > d max or v i < v min then link will be monitored remotely by air Define monitoring route (currently heuristically). Visualize in Google Earth. Brief Scope Task 1 Task 2 Task 3

21 Demonstration  Beaverton, OR, network simulated in DynusT. (currently traffic flow is not activated)  Some locations are assumed to “need” monitoring  Route determined by current heuristic. Red link = active monitoring; Blue link = deadheading to next link to be monitored. Run demonstation (will load command window, followed by Google Earth) Brief Scope Task 1 Task 2 Task 3

22 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations 2.1 Enhanced Calibration: Currently we calibrate MALTA with census and ground data. In this task we will also use data from remote sensors – airborne imagery and vehicles with GPS locations. JUST STARTED Brief Scope Task 1 Task 2 Task 3

23 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations –Detailed vehicle trajectories –Datasets range from light to congested traffic conditions –Expect to have 100k+ data points Brief Scope Task 1 Task 2 Task 3

24 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations o Quadratic Optimization Brief Scope Task 1 Task 2 Task 3 Greenshield Type 1 Greenshield Type 2

25 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations Brief Scope Task 1 Task 2 Task 3 Data

26 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations Task 2.2: Enhanced Traffic Management: to include arterial contra-flow and signal metering at intersections for evacuation. TO DO Brief Scope Task 1 Task 2 Task 3

27 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations Task 2.3 Simulate Data Collection: will allow us to simulate data collection from airborne sensors for performance measurement. TO DO Brief Scope Task 1 Task 2 Task 3

28 Task 2: Use of Airborne Sensors for Enhanced Network Planning and Emergency Operations It is essential that one monitors the unfolding evacuating scenario in developing evacuation strategies. Task 2.4 Experiments on Emergency Evacuations: will allow us to test different sensor configurations during emergencies, e.g., strategies with only ground-based sensor data, with only airborne sensor data, and a some intermediate levels of both sensor types. TO DO Brief Scope Task 1 Task 2 Task 3

29 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation The goal of Task 3 is to develop affordable technologies to monitor traffic performance for routine and commercial applications. Subtask 3.1: Enhancement of GUI and manual tracking features We will improve the interface for manual tracking, so that a user may be allowed to identify existing vehicles and vehicles subsequently entering the field of view, so that they are tracked by the software. Brief Scope Task 1 Task 2 Task 3

30 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.1: Enhancement of GUI and manual tracking features

31 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.1: Enhancement of GUI and manual tracking features

32 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.1: Enhancement of GUI and manual tracking features 1. Auto Detect Mode 2. Manual Detect Mode Menus for parameters and options Making sure you don’t exit inadvertently

33 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.1: Enhancement of GUI and manual tracking features Help menus:

34 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.2: Development and testing of road mask concepts Exact identification of vehicle positions from airborne imagery, to better locate vehicles within specific lanes, requires the clear identification of roadways. Road masks will allow us to do this in real-time since it will decrease image size for processing. Brief Scope Task 1 Task 2 Task 3

35 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.2: Development and testing of road mask concepts Brief Scope Task 1 Task 2 Task 3 Procedure: Have a geo-referenced MAP of area Take image of traffic (with approximate compass N) Match image with map and identify location of camera image Include mask by cropping of non-road part of image Process remainder of image Output processed data

36 Demo for Location Identification

37 Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and Simulation Subtask 3.3: Investigation of remote sensing for monitoring vehicle emissions DLR is investigating remote sensors for monitoring emissions and integrating emissions sensing in ANTAR. The UA-ASU-DLR research team will study the incorporation of remote emission sensing also in TRAVIS. TO DO Brief Scope Task 1 Task 2 Task 3

38 Summary The proposed project consisting of 3 major tasks: Data fusion for traffic and infrastructure monitoring Use of remotely collected data for developing better models for network planning and emergency operations Develop “enabling” technologies for airborne data collection The anticipated benefits are: Better calibration of infrastructure planning models Real-time estimation of network conditions in emergency and disaster conditions Development of affordable technologies for airborne traffic monitoring for routine, emergency and commercial applications

39 Conclusion End of Presentation! Any questions? Brief Scope Task 1 Task 2 Task 3


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