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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
6. SUMMARY OF RESULTS
The experimental results obtained with the two data sets
confirmed that LIDAR and optical imagery from airborne
platforms can deliver valuable traffic flow data. In addition,
the initial performance analyses of the representative data
sample have shown a good potential for automated
processing. Table 4 provides generic performance metrics,
which compare the potential of various airborne remote
sensing technologies to obtain traffic flow data.
Sensor LiDAR Digital camera
Platform Airplane Airplane Helicopter Satellite
General
characteristics
Spatial extent Good Good Limited Excellent
Temporal extent Moderate Moderat Excellent Weak
e
Process
Vehicle extraction Simple Difficult Difficult Complex
Vehicle Simple Feasible Feasible Limited
classification
Vehicle tracking Not Limited Good Not
feasible feasible
Velocity estimate Moderate Good Excellent Not
feasible
Flow computation Feasible Good Excellent Not
feasible
Table 4. Performance comparison metrics.
7. CONCLUSIONS
The feasibility and efficiency of using airborne remote
sensing to traffic monitoring were demonstrated. Airborne
sensors, LiDAR and frame imagery in particular, provide
high spatial and temporal resolution data that can effectively
support modeling and management of traffic flows. It should
be mentioned that even though the cost per unit of traffic
data for airborne platforms could be lower, as compared to
the traditional ground based methods, the cost of the platform
and the sensors might still be prohibitive. As a great amount
of LiDAR data, as well as imagery, is collected for routine
aerial mapping over transportation corridors and in urban
areas with dense road networks, there is already an
opportunity for obtaining such flow data at practically no
extra effort. Similarly, digital sensor systems can be turned
on to collect data during transit between mapping project
areas. Thus, at almost no cost, a significant amount of data
rich in traffic flow information can be acquired. To move
from a prototype implementation to a turn-key system,
further algorithmic developments are required to achieve a
highly-automated processing plus more tests are needed with
varying vehicle density and dynamics, as well as during
various flight conditions/environment.
ACKNOWLEDGEMENTS
This research was partially supported by the NCRST-F
program. The LiDAR and image data were provided by the
Ohio Department of Transportation and Optech International.
The authors would like to thank Eva Paska and Tamas Lovas,
PhD candidate students at the Department of Civil and
Environmental Engineering and Geodetic Science, OSU and
Department of Photogrammetry and Geoinformatics,
Budapest University of Technology and Economics,
respectively; and Shahram Moafipoor, visiting scholar, OSU
for their help in the data processing.
853
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