Use of GIS Methodology for Online Urban Traffic Monitoring German Aerospace Center Institute of Transport Research M. Hetscher S. Lehmann I. Ernst A. Lippok M. Ruhé DLR - IVF Rutherfordstraße 2 D Berlin Aim enhancement 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  V  x,  y,  z IMU integration of navigation data into image header for synchronisation of data streams :24:54 [ ] [ ] :24:54 [ ] [ ] :24:54 [ ] [ ] Generation of Traffic Information and Recommandations Prognosis and closure of time gaps by simulation Camera GPS/INS Image Recording Preprocessing/ Compression Preprocessing/ Compression Data Down- link Ground Station Geo- referencing Map Matching Vehicle Detection Traffic Data Extrac- tion Data Ware House Service Provider System Overview Tel: (+49) Fax: (+49) Mail: ParameterIR-CameraVis Camera DetectorTypeMCT, cooled at 77°K IR 18 MK IIICCD Number of pixels768 ´ ´ 1079 Field of view15,28° x 10,20°50° Rad. dynamics/ Spectrum8 Bit / 8 – 14 µm12 Bit / nm Frame rate25 Hz0.2 Hz GSD flight height 3500ft0.5 m0.3 m Swath width380 m594 m Absolute Accuracy GPSDGPS Position Roll,Pitch Heading 4 - 6m 0.015deg 0.08 deg 0.5 – 2 m 0.015deg 0.05 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 Simulation/ Prognosis Simulation/ Prognosis external information sources