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Traffic Flows from Airborne Platforms

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How has airborne imagery been used for traffic monitoring and management? ... Time Headway. Platoon Dispersion. Incident Management. Technology Development ... – PowerPoint PPT presentation

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Title: Traffic Flows from Airborne Platforms


1
Traffic Flows from Airborne Platforms
Mark Hickman University of Arizona International
Workshop on Satellite Based Traffic
Measurement Berlin September 9, 2002
2
Outline
  • How has airborne imagery been used for traffic
    monitoring and management?
  • What research is going on now?
  • What does the future hold?

3
History in the US
  • 1927 Aerial photography used to measure highway
    congestion (traffic density)
  • 1940s 70s Traffic research from airborne
    imagery
  • Variety of data collection methods
  • Applications areas traffic flow theory, platoon
    behavior, intersection operations, accident
    analysis, parking studies, O-D flow estimates,
    network performance assessment
  • Manual data reduction and analysis

4
History in the US
  • 1990s Limited work
  • Fixed-wing aircraft for level-of-service analysis
  • Experiments in intelligent transportation systems
    (ITS) applications for airborne traffic
    management
  • 2000s National Consortium on Remote Sensing in
    Transportation - Flows

5
National Consortium on Remote Sensing in
Transportation - Flows
  • Rationale for research
  • Current sensors and image processing technology
  • Mobility and non-intrusiveness of sensors
  • Quality and quantity of data
  • Goal Improve efficiency of transportation
    system planning and management by integrating
    remotely sensed data with ground-collected data

6
Research Themes of NCRST-F
  • Traffic Monitoring
  • Traffic Management
  • Freight and Intermodal Analysis
  • Common Methodological Issues

7
Traffic Monitoring and Management
  • Use remotely sensed data on-line in real time /
    near real time to reduce traffic congestion
  • Use remotely sensed data off-line for performance
    monitoring and to develop strategies in response
    to recurring traffic congestion

8
Framework
Sensor
Traffic flows
Use video and camera images, image processing and
algorithms to measure traffic variables Major
characteristic Develop new techniques based on
spatial characteristics of traffic flows
9
Focus of Research at University of Arizona
  • Develop off-line techniques
  • Measures of flow, speed, density, intersection
    delay
  • Methods to determine highway level of service
  • Information from vehicle trajectories
  • Visualization of traffic from airborne imagery
  • Develop on-line techniques
  • Individual vehicle speeds and trajectories

10
Data Collection Setup
11
Applications for Highway Level of Service
  • Methodological issues
  • Data collection procedures
  • Manual image processing techniques
  • Traffic variable extraction and analysis
  • Traffic variables of interest
  • Freeway densities
  • Arterial travel times
  • Intersection delay

12
Freeway Experiment
  • 10 km along I-10
  • Morning peak
  • Helicopter, video camera, GPS
  • Aircraft speed 100 km/h
  • Aircraft height 300 m
  • Field of view 275 m

Source Yahoo! Maps
13
Freeway Video
FREEWAY.WMV
14
Analysis of Freeways
  • Level of service measure Density
  • Passenger cars per unit distance per lane
  • Proposed method measures LOS directly
  • Identify freeway segment types
  • Determine number of lanes, length of roadway
  • Determine number and mix of vehicles from imagery
  • Compute density and LOS directly

15
Freeway LOS Results
16
Signalized Intersection Experiment
  • Site Speedway Boulevard and Euclid Avenue
  • 3 min study period (2 cycles)
  • Time 815 a.m.
  • Field of View 400 m
  • 10 sec vehicle counting interval

17
Intersection Video
INTERSECTION.WMV
18
Analysis of Intersections
  • Field data procedure for stopped delay and
    control delay taken from the Institute of
    Transportation Engineers (ITE)
  • Hovering and/or fixed-wing aircraft

19
Intersection LOS Results
20
Urban Arterial Experiment
  • Site Speedway Boulevard
  • Time Shoulder of peak (830-900 a.m.)
  • Field of view 250 m
  • Simultaneous ground travel time data collection
    test car and video cameras at end points

21
Arterial Street Methodology
  • Data collection
  • Calculate mean travel time for each run
  • Compute average (space-mean) speed for level of
    service

22
Arterial Video
ARTERIAL.AVI
23
Arterial Methodology
  • Produces higher number of observations than test
    car
  • Eliminates driver subjectivity
  • Captures within-platoon, between-platoon
    variability

24
Arterial Travel Time Results
25
Other Applications
  • Additional traffic information from the aerial
    video
  • Turning volumes
  • Lane utilization
  • Vehicle spacing
  • Vehicle trajectories
  • Incident effects
  • and queuing

26
(No Transcript)
27
Incident Management
28
Technology Development
  • Improved imagery characteristics
  • GPS used to geo-reference data
  • Inertial measurement systems capture camera
    position
  • Real-time image transmission to ground
  • Automated image processing

29
Current Technology Research
  • Orthographic data with GPS, IMU
  • Unpiloted aircraft

http//www.geodatasystems.com/
30
Automated Image ProcessingReal-time Speed
Determination
  • Use image properties to identify vehicles
  • Register sequence of images
  • Manual determination of ground control points
  • Automated image registration using GPS and IMU
    data
  • Subtract images
  • Match vehicles and estimate speeds

31
Image Processing Approach
32
Example
2 sec sample frames
33
Example
Georeferencing
Vehicle Matching Individual Vehicle Speeds
34
Automating Image ProcessingRegistration of Video
Imagery
  • Idea eliminate effect of video camera movement
  • Method automatically register frames using fixed
    locations in the image
  • Result smooth point of view in imagery, and
    capability of individual vehicle tracking

35
Vehicle Tracking
  • Capability to track individual vehicles
  • Data and analysis ideas
  • Speeds
  • Acceleration / deceleration characteristics
  • Lane changing behavior
  • Turning behavior
  • And more

36
Intersection Video
Unregistered Video orig_tuc.avi Registered
Video tuc_int_340.avi Registered
Video track_cars_cc.avi with Tracking
37
What does the future hold?
  • Technology capabilities
  • Methodology exists, is now being automated
  • Some technology is mature, others are maturing
  • Costs
  • Automatic data reduction can cut costs
    significantly
  • Equipment is dropping in price
  • Imagery provides data on many traffic variables
  • Airborne imagery may be competitive, especially
    in terms of cost per unit of traffic data
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