Airborne Traffic Flow Data and Traffic Management Mark Hickman and Pitu Mirchandani University of Arizona, ATLAS Center Greenshields 75 Symposium July 8-10, 2008
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
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
Basics of Airborne Traffic Data Sensor MEDIA Traffic flows Media: Video and camera images Research effort: Use video and camera images, image processing and algorithms to measure traffic variables Major Characteristic: We are no longer restricted to point detection, and we can use a spatial detection paradigm
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
Johnson s Aerial Photography Source: Johnson (1928)
Historical Perspective: Johnson Source: Johnson (1928)
Historical Perspective: Johnson Source: Johnson (1928)
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
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.
Technology UAS s (or UAV s): Coifman et al. (2004, 2006) Source: Coifman et al. (2006)
Microscopic Flow Characteristics from Airborne Data Raw Video Vehicle in Image Vehicle Position and Time Data Collection Video Image Processing Trajectory Processing Application Post-processing Registration Vehicle detection Vehicle tracking Scaling Road mask Correspondence Projection Source: Hickman and Mirchandani (2006)
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)
University of Arizona / ATLAS IMAGE REGISTRATION VEHICLE DETECTION CONNECTED COMPONENT LABELING TRACKING DISPLAY
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
IMAGE 1 IMAGE 2
Registered Video with Tracking Aerial Video I-10 at Elliott in Phoenix, AZ
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
Mariposa Frame (Arizona 1300 (0 Sonora) sec) Border
I-10 Work Zone, Tucson AZ
DLR: ANTAR and TrafficFinder Source: Ruhe et al. (2007)
Time (Sec) 200055 200050 200045 200040 200035 200030 200025 200020 200015 200010 200005 200000 199995 199990 199985 199980 199975 199970 199965 DLR 0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750 Distance along Roadway (m) Source: Hipp (2006)
DLR Congested flow Synchronized flow Impeded free flow Free flow Source: Ruhe et al. (2007)
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
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