Upload
trinhminh
View
227
Download
2
Embed Size (px)
Citation preview
atlas
Airborne Traffic Flow Data
and Traffic Management
Mark Hickman and Pitu Mirchandani
University of Arizona, ATLAS Center
Greenshields 75 Symposium
July 8-10, 2008
atlas
Outline
• Basics of airborne traffic data
• Macroscopic traffic flow characteristics
– Historical perspectives
– Current applications
• Microscopic traffic flow characteristics
– Historical perspectives
– Current research activities
• A look ahead
atlas
Basics of Airborne Traffic Data
• Use video and camera images, and in some cases
automated methods, to measure traffic variables
• Develop analysis techniques and measures of flow
based on spatial characteristics of traffic patterns
• Goal: Improve efficiency of transportation system by
integrating airborne data with ground-collected data
atlas
Basics of Airborne Traffic Data
Major Characteristic: We are no longer restricted to point detection,
and we can use a spatial detection paradigm
Media: Video and camera images
MEDIA
Sensor
Traffic
flows
Research effort: Use video and camera images, image processing and
algorithms to measure traffic variables
atlas
Macroscopic Flow: Historical Perspective
A. N. Johnson (1928), “Maryland Aerial Survey of
Highway Traffic Between Baltimore and Washington,”
Proceedings of the Highway Research Board.
Washington, D.C., Vol. 8, pp. 106–115.
Johnson was Dean of Engineering at the University of
Maryland and was investigating the traffic impacts of
widening of the two-lane highway between Baltimore
and Washington
atlas
Johnson’s
Aerial
Photography
Source: Johnson (1928)
atlas
Historical Perspective: Johnson
Source: Johnson (1928)
atlas
Historical Perspective: Johnson
Source: Johnson (1928)
atlas
Historical Perspective: Greenshields
“[H]igh altitude haze, shadows of buildings, trees, and the
movement of the blimp are difficulties which, it is believed, are
overshadowed by the complete and accurate record of all that
happens within the area studied” (p. 291)
“It is felt that several hours of such observations will reveal more
than days of less complete data. From this standpoint it could well
be that aerial photographs will prove comparatively cheap” (p. 297).
B. D. Greenshields (1947), “The Potential Use of Aerial Photographs in Traffic
Analysis,” Proceedings of the Highway Research Board. Washington, D.C., Vol. 27,
pp. 291–297.
Historical Perspective: Wagner and May (1963)
Traffic density contour maps from helicopter observations
atlas
atlas
Historical Perspective
Research to identify macroscopic characteristics:
• Forbes and Reiss (1952): traffic flow from video
• Jordan (1963): freeway speed, flow, density
• Rice (1963): urban traffic congestion due to access, incidents
• Cyra (1971): freeway volumes and speeds
• Makigami et al. (1985): freeway speed, density, bottlenecks
• Angel et al. (2002 onward): travel times, intersection delays
• Chandnani and Mirchandani (2002): speeds
• Agrawal and Hickman (2004): queue lengths
• Coifman et al. (2004, 2006): origin-destination flows, intersection
delay, parking lot utilization
• Etc.
atlas
Technology
UAS’s (or UAV’s): Coifman et al. (2004, 2006)
Source: Coifman et al. (2006)
atlas
Microscopic Flow Characteristics
from Airborne Data
Data
Collection
Video Image
Processing
Trajectory
Processing
Application
Post-processing
Raw Video Vehicle in Image
Vehicle Position
and Time
Registration
Vehicle detection
Vehicle tracking
Scaling Road mask
Source: Hickman and Mirchandani (2006)
Correspondence Projection
atlas
Microscopic Flow: Historical Perspective
Manual data reduction
• Treiterer et al. (1966 onward)
• Smith and Roskin (1985)
Automated data reduction
• University of Arizona / ATLAS and the Ohio State University
• Technical University of Delft
• DLR
Microscopic Flow: Treiterer
Source: Treiterer and Taylor (1966)
atlas
University of Arizona / ATLAS
VEHICLE DETECTION
CONNECTED COMPONENT
LABELING
TRACKING
DISPLAY
IMAGE REGISTRATION
atlas
Vehicle Tracking in Imagery
• Combination of short-term and long-term tracking of
connected components (“blobs”) in registered
imagery
• Short term: Components are investigated to detect
moving vehicles and screen out false positives
• Long term: Once identified, a blob should not be
“lost” in the image sequence; a location predictor is
also used
atlas
IMAGE 1
IMAGE 2
atlas
Registered Video with Tracking
Aerial Video
I-10 at Elliott in Phoenix, AZ
atlas
University of Arizona / Ohio State University
Current research:
• Investigating of truck movements at border crossings
and at work zones
• Using ground counts, selected vehicle identification
(GPS or license plates), and airborne observations
• Investigating of travel times, delays, queuing in order
to develop strategies for improved operations
Frame 1300 (0 sec)Mariposa (Arizona – Sonora) Border
atlas
I-10 Work Zone, Tucson AZ
atlas
DLR: ANTAR and
TrafficFinder
Source: Ruhe et al. (2007)
atlas
DLR
199965
199970
199975
199980
199985
199990
199995
200000
200005
200010
200015
200020
200025
200030
200035
200040
200045
200050
200055
0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750
Distance along Roadway (m)
Tim
e (
Se
c)
Source: Hipp (2006)
atlas
DLR
Congested flow
Synchronized flow
Impeded free flow
Free flow
Source: Ruhe et al. (2007)
atlas
A Look Ahead
• Airborne imagery has a long history in traffic analysis
– Spatial paradigm for data collection
– Allows collection of a wide variety of performance
measures
• Macroscopic flow characteristics are regularly
collected this way today
• Tools for individual vehicle tracking and analysis are
available
atlas
A Look Ahead
• Mechanisms for data collection and reduction exist
– Macroscopic flow characteristics
– Microscopic flow characteristics
• Applications
– Congestion studies
– Real-time traffic management
– Understanding of traffic flow
– Calibration of traffic simulation models