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: Matthias.Hetscher@dlr.de

Ines.Ernst@dlr.de

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