Use of GIS Methodology for Online Urban Traffic Monitoring
German Aerospace Center
Institute of Transport Research
M. HetscherS. Lehmann
I. ErnstA. LippokM. Ruhé
DLR - IVFRutherfordstraße 2
D-12489 Berlin
Aimenhancement of accuracy of derivation of traffic flow parameters of an operational
airborne traffic monitoring system and installation of a traffic GIS
Processing Chain
Different Cameras for Image recording with synchronous record of GPS/INS-Data
direct Referencing of Image Data to digital Map (NAVTEQ)
• determination of probable street width from map attributes (NAVTEQ: type, speed category, rough lane number, direction) and road construction regulations, generation of virtual search region with overlapping polygons
• broad variety of appearance due to different conditions led to contour search algorithms in edge images, dependent from actual parameter search for characteristic contours and size for vehicle hypothesis. Pixel values are used as additional information
• for derivation of velocity vectors determination of virtual car position from last image and projection into actual image. Differences to real position in actual image give value and direction of velocities
Image Processing for Traffic Data Extraction
• system calibration, analysis of position- und attitude data
• test of all all street segments of a sufficient area around the recorded region regarding their intersection with the image and determination of observed edge intervals .
Traffic Data Aggregation
Results and Improvement
[(x,y), v, size class]• car list per image and section ID:
[strID,(t0,t1),[rho1,v1],[rho2,v2],[rho3,v3]]• traffic data per image and section ID
Density, average speed determination
[timestamp,edgeID,%seen,[rho1,v1],[rho2,v2],[rho3,v3]]• traffic data per NAVTEQ-edge
weighted average for several images• traffic data per section ID: [strID,%seen,[rho1,v1],[rho2,v2],[rho3,v3]]
weighted average for several section IDs
200 Hz
•Position Lat, Long, Altitude
•Attitude Roll, Pitch, Heading
•Time 1Hz
GPS/INS
Integration
Kalman Filter
GPS/INS
Integration
Kalman Filter
3 x Gyro
3 x Accel
Kinematic
Dif. GPS
Kinematic
Dif. GPS
200 Hz
1Hz
GPS
remote
DGPS
correction
Lat, Long, Altitude
x,y,
z
Vx,y,
z
IMU
• integration of navigation data intoimage header for synchronisation of data streams
06.05.2003 16:24:54 [-0.7800 0.6800 268.0900] [52.51452 13.35138 719.10] 06.05.2003 16:24:54 [-0.7800 0.6800 268.0900] [52.51452 13.35137 719.10] 06.05.2003 16:24:54 [-0.7800 0.6800 268.0900] [52.51452 13.35137 719.10] 06.05.2003 16:24:54 [-0.7800 0.6800 268.0900] [52.51452 13.35136 719.09]
Generation of Traffic Information and Recommandations
• Prognosis and closure of time gaps by simulation
Camera
Camera
GPS/INSGPS/INS
Image Recording
Image Recording
Preprocessing/
Compression
Preprocessing/
Compression
Data Down-
link
Data Down-
link
Ground
Station
Ground
Station
Geo-referencin
gMap Matching
Geo-referencin
gMap Matching
Vehicle Detectio
n
Vehicle Detectio
n
Traffic Data
Extrac-tion
Traffic Data
Extrac-tion
Data Ware Hous
e
Data Ware Hous
e
Service Provide
r
Service Provide
r
System Overview
Tel: (+49)30-67055-646Fax: (+49)30-67055-202
Mail: [email protected]
Parameter IR-Camera Vis Camera
DetectorType MCT, cooled at 77°K IR 18 MK III
CCD
Number of pixels 768 ´ 500 1980 ´ 1079
Field of view 15,28° x 10,20° 50°
Rad. dynamics/ Spectrum 8 Bit / 8 – 14 µm 12 Bit / 450-700 nm
Frame rate 25 Hz 0.2 Hz
GSD flight height 3500ft 0.5 m 0.3 m
Swath width 380 m 594 mAbsolute Accuracy
GPS DGPS
Position Roll,Pitch Heading
4 - 6m0.015deg0.08 deg
0.5 – 2 m0.015deg0.05 deg
• 61 % of vehicles correctly counted • less 20 % false counted cars• improvement to 75 % by exclusion of parking cars
Results
Improvements
•matching accuracy•separation of traffic active area•extension of a-priori knowledge•improvement of digital mask•data and sensor fusion•spatio temporal data analysis
Satellite image projection to digital map
Traffic GIS for offline applications
•database for heterogenous sources•improvement for traffic simulations•traffic scenario validation•spatio temporal data analysis (isochrones, travel times, cachment areas)•visualisation of socioeconomic analysis•mobility research
•temporal analysis for seasonal variation•urban image classification•texture and content analysis •data fusion
to do
Application to online System
Separation of Vegetation
Database Server
Database Server
Simulation/
Prognosis
Simulation/
Prognosis
external information
sources
external information
sources
