gi2013 ppt löwner_20130429_geodata_forprivatetransportation_final_print

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Prof. Dr.-Ing. Marc-O. Löwner, 29. April 2013

Grenzüberschreitendes 13. Sächsische GI/GIS/GDI-Forum am 29.-30. April in Dresden

Geo data for private transportation questions

Our daily hazard on the streets

2.361.457 accidents on German

streets in 2011

306.266 accidents causing

personal injury

4.722 fatal accidents

Damages to persons in 2011 [%]

killed badly injured slightly

injured

build up areas out-of-town highway

Statistisches Bundesamt 2012

29.04.2013 | Marc-O. Löwner | Geo data for private transportation questions | Seite 2

Accidents caused by influence of weather (in total)

bad weather

crosswind

dazzling sun

hail or snow storm

fog

[No

. o

f p

ers

on

s]


1.54% of all accidents


Accidents by influence of weather (killed persons)

bad Weather

crosswind

dazzling sun

hail or snow storm

fog

[No

. o

f p

ers

on

s]


2.16% of all accidents


dazzling by low sun conditions is a danger

to private transportation

today, no warning service is available,

either by public authorities or private

navigation system providers

Risk assessment of dazzling is

possible for transport infrastructure

networks !

Small application project supported by the

Verein zur Förderung der Geoinformatik in

Norddeutschlang GiN e.V.

„Massenkarambolage von 259 Autos auf der A2 Bei einer Massenkarambolage auf der Autobahn A2 in Niedersachsen wurden mehr als 60 Menschen verletzt, zehn davon erlitten schwere Verletzungen. Nach heftigen Regenfällen verloren im Bereich Peine mehrere Fahrer die Kontrolle über ihre Autos. Der Sachschaden soll in die Millionenhöhe gehen. […] Ein Grund für die Vielzahl der Beteiligten war nach erster Einschätzung der Polizei auch, dass viele Fahrer bei dem schlechten Wetter zu schnell unterwegs waren und zu wenig Abstand zum Vordermann hielten. Die tiefstehende Sonne behinderte die Sicht erheblich, sagte ein Sprecher der Braunschweiger Autobahnpolizei. […]“

20.7.2009 www.welt.de


Risik assesment of dazzling by low sun conditions

Risik assesment of dazzling by low sun conditions

main target:

Modelling of dazzling effects on the highways of Lower Saxony for one year with high

temporal resolution

model components

street data

position of the sun in a high temporal resolution

vehicle model

result

Movie of street segments under risk

not considered

digital terrain model

surrounding infrastructure

weather effects (reflection from the lane)


Street information from OpenStreetMap

started 2004

Volunteered Geographic Information

user-generated Content

non-professionals may add coordinates,

objects and attributes. They also may

change existing ones.

pPublished under ShareAlike 2.0 Generic

(CC BY-SA 2.0)

users are allowed to use and share data,

even for commercial purposes

Zimmermann & Löwner (2012)


position of the sun

angle and azimuth of the sun (AstAlm 2006;

Meeus 2000) for every 15 minutes

26.200 pairs of angles for one geometry

h

N

Vertikalwinkel

Horizontalwinkel

0103 9:45 143.871086524855 7.5960694032043

0103 10:00 147.105451090724 8.88208963931512

0103 10:15 150.393674576367 10.0626732182494

0103 10:30 153.734117939271 11.1306801587273

0103 10:45 157.124065211231 12.0798261642064

0103 11:00 160.559715910859 12.9045201956266

0103 11:15 164.036211005061 13.5998340957865


vehicle model

three main danger classes

dazzling from forward view

dazzling from forward view in spite of put

on solar screen

dazzling from behind


data infrastructure

OSM Data PostgreSQL osm2pgsql

Ja

va

angle of

the sun

Risk assesment

Ja

va

visualization

Arc

Ob

jects

result pgsql2shape

SQ

L

split

polylines

orientation of

street segments

SQ

L


results

733.600.000 evaluations of single

geometries

403 km (43 %) of highways are

affected in Lower Saxony

Highway interchange Osnabrück

forward view: 171 h/a (75 h/a)

forward view in spite of put on solar

screen: 133 h/a (28 h/a)

from behind: 63 h/a (44 h)


can we go any further?

Advises from ACE Auto Club Europa e.V.

put on solar sreen

approach crossings slowly

use trafic lights not expossed to direct sun

use sun glasses

clean front window

keep savety distance


Architecture of a solar risk service

start

end

PSnPZn

tn

t1

PS1PZ1

route (http)

Solar Risk

Service

split geometry

time stamp City-DB

position of the sun

evaluate geometry

deliver evaluated route

synchronize

CityGML

X3D

KML


Automotive risk service

Start

Ziel

PSnPZn

tn

t1

PS1PZ1

route (http)

Solar Risk

Service

split geometry

time stamp City-DB

position of the sun

evaluate geometry

deliver evaluated route

synchronize

CityGML

X3D

KML

field of vision field of vision

building

Automotive risk

service


Further requirements

semantical enriched 3D-city models: CityGML

geometries for line of sight analysis (CityGML’s LoD 2 Building model would be best)

semantical information on function and usage of buildings (attributes are partly available

in the Building class)

fusion of infrastructure information (geo data) and vehicle sensors used in Advanced

Driver Assistant Systems (ADAS)

precise position

interaction with driver

communication infrastructure


Thank you for your attention !


gi2013 ppt löwner_20130429_geodata_forprivatetransportation_final_print

Technology

lwner geo data

dresdengeo data

risik assesment of dazzling

modelling of dazzling

influence of weather

street information

snow stormfogno

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