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TMCpro
- Presence and Future of Real Time Traffic Information
Dr. Ulrich FastenrathT-Systems –
Systems Integration
DDG Gesellschaft für Verkehrsdaten mbH
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System overview
Administrator
Playout
Center
Sat-Uplink
Leased
lines
Traffic Data
ISDN
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Quality
Raw Data
Content Provider
Service Provider
Terminal Device
User
100%
Traffic Modelling and
Forecasting
The TMCpro
approach to quality
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Producing
Numerical
Data with
SensorsStationary
data
collection
systems improve
the quality of traffic information.
Sensor• measures traffic flow and average speed
• distinguishes cars from trucks
• reports programmable events
> 5.500 loops
DDGGSM
4.000 sensors
Detected network contains >90% of all incidents
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Traffic datamanagement
center
Traffic information (Customer interface)
Product
generation Traffic analysis, Generation of traffic reports,
Calculation of travel times, Historical time series, Disturbance development forecasts,
Short term predictions,automated consistency checks,
Customer specific features
Datacollection
Communication interfaces, Data preprocessor (Plausibility checks, Aggregation, Localization)
Data sourcesLMSt VIZ / VRZ SES FCD
( )
).()()(
1)(1
,
VVVVx
PxVV
tV
xV
t
rmprmp
e
rmp
−⋅+−⋅+∂
∂−=
∂∂
+∂∂
=∂
∂+
∂∂
ρν
ρτρ
ρ
νρρ
From Traffic Data to Traffic Information
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0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0 10 20 30 40 50 60 70 80 90 100
k [Fzg./km]
Gam
ma
gamma_s60 std_gamma_s60 std_gamma_s300 gamma_c std_gamma_c gamma_s300
Extrapolation characteristic, 2 lanes
Traffic does not behave as it is supposed to
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50
60
70
80
90
100
110
120
130
140
150
02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Time
free
vel
ocity
[km
/h]
Calibrating the free Velocity
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Telegram #1
Aggregation Interval
Telegram #2
Time Diagonal
Telegram #3
Data Time
System Time
Coming to terms with the past
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Classification of zero flux(inductive loops)
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18 20 22 24
Time of the day
fraction[%]
SV
FV
KA
Classification of zero flux(infrared detectors)
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16 18 20 22 24
Time of the day
fraction[%]
SV
FV
KA
SV meas
Shown in red is the fraction of all zero flux situations which were due to stationary traffic(data are from 15.05.2002 15:00 - 20.05.2002 06:40)
Go with no flow?
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0
50
100
150
07:00 08:00 09:00 10:00 11:00
time
velo
city
[km
/h]
MQLOKHILGKT
Passage of shock fronts at a virtual detector: the test position is 2284 m away from the upstream detector and 3581 m away from the downstream detector.
Is the traffic still there when nobody looks?
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Detection of disturbed traffic states by DDG infrastructure
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Detection of disturbed traffic states by DDG infrastructure II
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Reference: BMW AG, Dr. Klaus Bogenberger, „Qualität von Verkehrsinformationen“, Straßenverkehrstechnik 10/2003
customers‘ expectation
A Scheme for measuring Product Quality
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Visualisation
of Complex Dynamic Systems
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Road Weather
and Road Conditions
TMC-Code Meaning1002 Danger of aquaplaning1003 Slippery road1019 Slippery road due to frost1009 Freezing rain1008 Black ice1011 Slush1112 Rain1109 Heavy rain1104 Snowfall1101 Heavy snowfall1107 sleet
Road detector system for icy conditions
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Meteorological input data
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Precipitation radar images
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Convert TMC-Codes into weather messages
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Example
for
a TMC Message
„Danger
of Aquaplaning“
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The product feature „DDG road weather“
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Navigation in Space and Time
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Some Varieties of Traffic Forecast
?
?Growth Rate
Pre- Warning
!q
Duration
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Bottlenecks
link
AB
Qarr
(A)
Qarr
(B)
recoveryrecovery
breakdownspillover
active
inactive
blocked
activity of bottlenecks
9576 bottlenecks analysed 2843 bottlenecks
considered relevant for pre-warnings
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Breakdown Frequencies at Bottlenecks
Reference: Brilon, W.; Zurlinden, H.: Kapazität von Straßen als Zufallsgröße, Straßenverkehrstechnik 4/2004, S. 164-172
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Flow rate (q), probability of breakdown (Pbd) and of congestion (Pc) at site Düsseldorf Mörsenbroich located along the highway A52
0%
20%
40%
60%
80%
100%
0 2 4 6 8 10 12 14 16 18 20 22 24time of day 05.07.2004 [h]
prob
abili
ty [%
]
0
300
600
900
1200
flow
rate
[vph
pl]
P_bd(+15 min)P_cq
Breakdown of traffic flow is a stochastic event, whereby probabilities of breakdown are associated with specific flow rates.
Breakdown Probabilities at Bottlenecks
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Störfallmodell
Konkrete Realisierung einer Verkehrsstörung
0
400
800
1200
1600
2000
3 4 5 6 7 8 9 10 11 12 13 14 15 16Tageszeit [H]
Ver
kehr
sstä
rke
[Fz/
h/S
pur]
0
4
8
12
16
20
Ver
wei
ldau
er [m
in]
Q-IN Kapazität Cv Q-OUT Tv
breakdown
recovery
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Delay caused by breakdown of traffic flow at different times Tbd
0
10
20
30
40
50
5 6 7 8 9 10 11entry time [h]
Del
ay [m
in] T_bd=5,75
T_bd=6,5T_bd=7,5T_bd=8,0
Delay Times at Bottlenecks
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Pre-Warnings: Example 1
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Pre-Warnings: Example
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Pre-Warnings: Example 3
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0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
FPR [%]
TPR
[%]
0
50
100
150
200
250
300
VWZ
[min
] ROC
K
L
VWZ
Quality of Pre-Warnings
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23,000 kmmotorways
1,280,000 kmin total
106,000 kmhighways
Motorways are not enough
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Do-iTDo-iT
is
part
of the
research
and development
initiative „Verkehrsmanagement 2010“
sponsored
by
BMWi
Do-iT: The
Project
DDG Gesellschaft für Verkehrsdaten mbH
Partners:
Landeshauptstadt Stuttgart
Innenministerium Baden-Württemberg
Stadt Karlsruhe
====!"§==Mobile=Associated: T-Mobile Deutschland GmbH
Universität Stuttgart, represented byInstitut für Anwendungen der Geodäsie im Bauwesen andLehrstuhl für Verkehrsplanung und Verkehrsleittechnik
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Data provision for public and private
applications
Floating Phone Data: Functional PrincipleDo-iT
FPD-Server
BTSBSC
MSC
BTS
A interface
A-bis interface
MS
Floating Phone Data
Network Probes
Identification of Active Road Users
Mobile Phone Positioning
Map-Matching & Trajectory Generation
BTS
BSC
Reference: IAGB University of Stuttgart
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A linkA-bis link
BTS BSC MSC
BTS
MS
All mobiles:• Localisation Updates (in particular at LA updates)
Active mobiles only:• Handover events• Measurement Reports (~ 2 Hz)
(LAC1) -> (LAC2,CI2)
(CI1) -> (CI2)
CI,TA (=distance)Field strength
Master data needed forinterpretation:
Cell geometry
Topology data(=antenna locations)
Best server plots
Temporary Mobile Subscriber ID
Establishing
the
Data BasisDo-iT
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Network
covered
and Applications
C-NetCity of Karlsruhe
C-NetCity of Stuttgart
A-Netmotorways
B-Netfederal highways
U-Netdiversion routes
AK Walldorf
AK Weinsberg
Applications
Innenministerium BW (A-Net, U-Net):• Dynamic Network Control• Traffic State of U-Net
Cities of Stuttgart and Karlsruhe (C-Net, urban U-Net):• Improvement of knowledge about current traffic situation• Estimation of Travel Times• direct measurement of the impact of network control• improvement of control strategies
DDG (all networks):• Navigate, TMCpro
Do-iT
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Comparison of Location Area Updatesand traffic flow as measured by stationary sensor
0
500
1000
1500
2000
2500
3000
3500
4000
0 3 6 9 12 15 18 21 0
time of day [HH]
Rat
e [e
vent
s/h]
Q-SESLAC-Updates
Flow of mobiles ≠
traffic flowSuperposition of more than one traffic flow
LA boundary
Cell boundary
LA 1LA 2
CI 2
Frequency of transitions LA1 → (LA2,CI2)
Measurement
of Traffic
FlowDo-iT
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Network
covered
at Abis LevelDo-iT
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2211112228
17980
1800215639
32092
5417
9
54178
3413
LAC CI Azimut Time28961 2211128939 12228 120 06:28:3228939 17980 45 06:28:4028939 18002 225* 06:29:4628939 15639 45* 06:30:2428939 32092 240 06:31:1728939 54179 300 06:31:3828939 54178 160 06:32:2428950 3413 06:33:39
* = Tunnel
Example
at A Level: Free TrafficDo-iT
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Do-iTProjektnetz
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Example
for
Incident
DetectionDo-iT
3413(28950)->55508(28682)
0
5
10
15
20
25
30
35
40
45
50
55
60
0 4 8 12 16 20 24
arrival time [h]
trave
l tim
e [m
]
NET-FCDStationary
Sensors
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A Truck Accident
observed
by
Floating Phones
Do-iT
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Waves
of Holiday Traffic, southbound
Do-iT
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It
can
always
get
worse.
Do-iT
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The
Traffic
Jam is
no Respecter
of Persons.
Do-iT
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Early
Example
from
a Trunk Road
Do-iT
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TMCpro: Neue Inhalte für die dynamische Navigation. Baulich getrennte Fahrspuren.
Do-iT
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TMCpro: Neue Inhalte für die dynamische Navigation. Drei-Phasen-Theorie.
Do-iT
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TMCpro: Neue Inhalte für die dynamische Navigation. Synchronisierter Verkehr.
Do-iT
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TMCpro: Neue Inhalte für die dynamische Navigation. Rückreisewellen.
Do-iT
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TMCpro: Neue Inhalte für die dynamische Navigation. Auch das gibt es.
Do-iT
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12.07.2007
0
300
600
900
5 6 7 8 9 10 11 12
Austrittszeit TA [h]
Rei
seze
it [s
]
RohdatenZüge
Travel Time Data for
a Rail
Transit Mode
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0
300
600
900
6,0 6,5 7,0 7,5 8,0 8,5 9,0 9,5 10,0 10,5 11,0 11,5 12,0
Austrittszeit TA [h]
Rei
seze
it [s
]
10.7.11.7.12.7.
ICE 778 ICE 604
ICE 976
ICE 78
ICE 602 ICE 278 ICE 76
ICE 600
ICE 372
S4, S41
Trains
Identified
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A8-OW (stk_id=5)
TMC-LC
SES/VIZ
LUP
LUP + Call
AK Stuttgart
AS AD Leonberg
AS Heimsheim
AS PforzheimO N W
LATT-Messung
Enrichment
of stationary
Infrastructure
by
the Mobile Network
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1/3
2/3
O->W
Probability
Density
of Cells
along
a Motorway
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Example
for
Incident
LocalisationDo-iT
Incident indicationFloating Phones
locationtim
e of
day
Stationary
Sensors
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Sources for Traffic Data in Germany in the Course of Time
1990 20102000 2020
high ways
motor ways
Citiesstationary detection systems
FCD
Net-FCD
stationary
traffic management centers
GATS-FCD
convergence zone
Regional TICs
limited installations
rollout net-FCD
diverse FCD species
SES of DDG
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Sensors, Floating Cars and Floating Phones
Editorial Team
Traffic information
Traffic ModellingTraffic Forecast
Data Collection
Police Loops Sensors Floating Cars
Do-iT
Floating Phones