Präsentation von Dr. Ulrich Fastenrath, Head of Product Development T-Systems Traffic GmbH, zu TMCpro, Floating Phone Data und der Zukunft von Verkehrsinformationsdiensten

1. TMCpro -
Presence and Future of Real Time Traffic Information
Dr. Ulrich Fastenrath
T-Systems – Systems Integration
DDG Gesellschaft für Verkehrsdaten mbH

2. System overview
Sat-Uplink
Leased lines
Playout Center
Traffic Data
ISDN
Administrator
page 1

3. The TMCpro approach to quality
Quality
Traffic
Modelling and
100% Forecasting
Raw Content Service Terminal User
Data Provider Provider Device
page 2

4. Producing Numerical Data with Sensors
Stationary data collection systems improve the quality of traffic information.
GSM
DDG
4.000 sensors
Sensor
> 5.500 loops • measures traffic flow and
average speed
• distinguishes cars from
trucks
Detected network contains • reports programmable events
>90% of all incidents
page 3

5. From Traffic Data to Traffic Information
LMSt VIZ / VRZ SES FCD
Data sources
Data Communication interfaces, Data preprocessor
collection (Plausibility checks, Aggregation, Localization)
Product ∂ρ Traffic )
∂( ρV analysis, Generation of traffic reports,
+ = ν rmp ,
generation ∂t ∂x
Calculation of travel times, Historical time series,
Disturbance development forecasts, ν
∂V ∂V 1 ∂P( ρ ) 1
Traffic data +V =− Short term predictions, rmp ⋅ (Vrmp − V ).
+ ⋅ (Ve − V ) +
management ∂t ρ ∂x τ (ρ)
∂x automated consistency checks, ρ
center Customer specific features
Traffic information
(Customer interface)
page 4

6. Traffic does not behave as it is supposed to
Extrapolation characteristic, 2 lanes
0,8
0,7
0,6
0,5
Gamma
0,4
0,3
0,2
0,1
0
0 10 20 30 40 50 60 70 80 90 100
k [Fzg./km]
gamma_s60 std_gamma_s60 std_gamma_s300 gamma_c std_gamma_c gamma_s300
page 5

7. Calibrating the free Velocity
150
140
130
120
free velocity [km/h]
110
100
90
80
70
60
50
02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Time
page 6

8. Coming to terms with the past
Aggregation Interval Time Diagonal
System Telegram #3
Time Telegram #2
Telegram #1
Data Time
page 7

9. Go with no flow?
Classification of zero flux Classification of zero flux
(inductive loops) (infrared detectors)
100 100
80 80
frac frac SV
60 SV 60
tion tion FV
[%] FV [%]
KA
40 KA 40
SV meas
20 20
0 0
0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24
Time of the day Time of the day
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)
page 8

10. Is the traffic still there when nobody looks?
150
velocity [km/h]
100 MQ
LOK
HIL
GKT
50
0
07:00 08:00 09:00 10:00 11:00
time
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.
page 9

11. Detection of disturbed traffic states by DDG infrastructure
page 10

12. Detection of disturbed traffic states by DDG infrastructure II
page 11

13. A Scheme for measuring Product Quality
Reference: BMW AG, Dr. Klaus Bogenberger,
„Qualität von Verkehrsinformationen“,
Straßenverkehrstechnik 10/2003
customers‘
expectation
page 12

14. Visualisation of Complex Dynamic Systems
page 13

15. Road Weather and Road Conditions
Road detector system for icy conditions
TMC-Code Meaning
1002 Danger of aquaplaning
1003 Slippery road
1019 Slippery road due to frost
1009 Freezing rain
1008 Black ice
1011 Slush
1112 Rain
1109 Heavy rain
1104 Snowfall
1101 Heavy snowfall
1107 sleet
page 14

16. Meteorological input data
page 15

17. Precipitation radar images
page 16

18. Convert TMC-Codes into weather messages
page 17

19. Example for a TMC Message „Danger of Aquaplaning“
page 18

20. The product feature „DDG road weather“
page 19

21. Navigation in Space and Time
page 20

22. Some Varieties of Traffic Forecast
Growth Rate
Duration
?
?
q
! Pre-
Warning
page 21

23. Bottlenecks
A Qarr(A)
B
Qarr(B)
link
active blocked
9576 bottlenecks analysed
spillover 2843 bottlenecks
breakdown considered relevant
for pre-warnings
recovery
recovery
inactive
activity of bottlenecks
page 22

24. Breakdown Frequencies at Bottlenecks
Reference: Brilon, W.; Zurlinden, H.: Kapazität von Straßen als Zufallsgröße,
Straßenverkehrstechnik 4/2004, S. 164-172
page 23

25. Breakdown Probabilities at Bottlenecks
Flow rate (q), probability of breakdown (Pbd) and of congestion (Pc ) at
site Düsseldorf Mörsenbroich located along the highway A52
100% 1200
80%
900
flow rate [vphpl]
probability [%]
60% P_bd(+15 min)
600 P_c
40% q
300
20%
0% 0
0 2 4 6 8 10 12 14 16 18 20 22 24
time of day 05.07.2004 [h]
Breakdown of traffic flow is a stochastic event, whereby probabilities of
breakdown are associated with specific flow rates.
page 24

26. Störfallmodell
Konkrete Realisierung einer Verkehrsstörung breakdown
20
2000 recovery
16
Verweildauer [min]
1600
Verkehrsstärke
[Fz/h/Spur]
12
1200
800 8
400 4
0 0
3 4 5 6 7 8 9 10 11 12 13 14 15 16
Tageszeit [H]
Q-IN Kapazität Cv Q-OUT Tv
page 25

27. Delay Times at Bottlenecks
Delay caused by breakdown of traffic flow
at different times T bd
50
40 T_bd=5,75
Delay [min]
30 T_bd=6,5
20 T_bd=7,5
T_bd=8,0
10
0
5 6 7 8 9 10 11
entry time [h]
page 26

28. Pre-Warnings: Example 1
page 27

29. Pre-Warnings: Example
page 28

30. Pre-Warnings: Example 3
page 29

31. Quality of Pre-Warnings
100% 300
80% 250
200 ROC
VWZ [min]
TPR [% ]
60%
K
150
40% L
100 VWZ
20% 50
0% 0
0% 20% 40% 60% 80% 100%
FPR [%]
page 30

32. Motorways are not enough
1,280,000 km
in total
23,000 km 106,000 km
motorways highways
page 31

33. Do-iT: The Project
Do-iT is part of the research and development
Do-iT initiative „Verkehrsmanagement 2010“ sponsored
by BMWi
Partners:
Innenministerium Baden-Württemberg
Landeshauptstadt Stuttgart
Stadt Karlsruhe
Universität Stuttgart, represented by
Institut für Anwendungen der Geodäsie im Bauwesen and
Lehrstuhl für Verkehrsplanung und Verkehrsleittechnik
Associated: T-Mobile Deutschland GmbH ====!quot;§==Mobile=
DDG Gesellschaft für Verkehrsdaten mbH
page 32

34. Floating Phone Data: Functional Principle
Do-iT
A-bis A
interface interface
BTS BSC
BTS MSC
BSC
MS BTS
Network Probes
Mobile Phone Positioning
Identification of Active Road Users
Data provision for
public and private Floating
Phone Data
Map-Matching &
applications Trajectory Generation
FPD-Server
Reference:33
page IAGB University of Stuttgart

35. Establishing the Data Basis
Do-iT
BTS
BTS BSC MSC
MS
All mobiles: A-bis link A link
• Localisation Updates
(in particular at LA updates) (LAC1) -> (LAC2,CI2)
Active mobiles only: (CI1) -> (CI2)
• Handover events
• Measurement Reports (~ 2 Hz) CI,TA (=distance)
Field strength
Temporary Mobile Subscriber ID
Master data needed for Cell geometry
interpretation:
Topology data
(=antenna locations)
Best server plots page 34

36. Network covered and Applications
Do-iT
Applications
A-Net
U-Net motorways
Innenministerium BW
(A-Net, U-Net): diversion routes
AK Walldorf
• Dynamic Network Control
• Traffic State of U-Net
AK Weinsberg
Cities of Stuttgart and
Karlsruhe B-Net
(C-Net, urban U-Net): federal highways
• Improvement of knowledge
about current traffic situation
• Estimation of Travel Times C-Net
• direct measurement of the City of Karlsruhe
impact of network control
• improvement of control
strategies
C-Net
DDG (all networks): City of Stuttgart
• Navigate, TMCpro
page 35

37. Measurement of Traffic Flow
Do-iT
Comparison of Location Area Updates
and traffic flow as measured by stationary sensor
4000
3500
3000
Rate [events/h]
2500
Q-SES
2000
LA boundary LAC-Updates
1500
LA 2 1000
LA 1
500
CI 2
0
0 3 6 9 12 15 18 21 0
time of day [HH]
Cell boundary
Flow of mobiles ≠ traffic flow
Frequency of transitions Superposition of more than one traffic flow
LA1 → (LA2,CI2)
page 36

38. Network covered at Abis Level
Do-iT
page 37

39. Example at A Level: Free Traffic
Do-iT
LAC CI Azimut Time
22111 28961 22111
12228
28939 12228 120 06:28:32
17980 28939 17980 45 06:28:40
28939 18002 225* 06:29:46
28939 15639 45* 06:30:24
18002 28939 32092 240 06:31:17
15639 28939 54179 300 06:31:38
28939 54178 160 06:32:24
28950 3413 06:33:39
32092 * = Tunnel
9
17
54
54178
3413
page 38

40. Projektnetz Do-iT
page 39

41. Do-iT Example for Incident Detection
Stationary Sensors
NET-FCD
3413(28950)->55508(28682)
60
55
50
45
40
travel time [m]
35
30
25
20
15
10
5
0
0 4 8 12 16 20 24
arrival time [h]
page 40

42. A Truck Accident observed by Floating Phones
Do-iT
page 41

43. Waves of Holiday Traffic, southbound
Do-iT
page 42

44. It can always get worse.
Do-iT
page 43

45. The Traffic Jam is no Respecter of Persons.
Do-iT
page 44

46. Early Example from a Trunk Road
Do-iT
page 45

47. TMCpro: Neue Inhalte für die dynamische Navigation.
Baulich getrennte Fahrspuren.
Do-iT
page 46

48. TMCpro: Neue Inhalte für die dynamische Navigation.
Drei-Phasen-Theorie.
Do-iT
page 47

49. TMCpro: Neue Inhalte für die dynamische Navigation.
Synchronisierter Verkehr.
Do-iT
page 48

50. TMCpro: Neue Inhalte für die dynamische Navigation.
Rückreisewellen.
Do-iT
page 49

51. TMCpro: Neue Inhalte für die dynamische Navigation.
Auch das gibt es.
Do-iT
page 50

52. Travel Time Data for a Rail Transit Mode
12.07.2007
900
600
Reisezeit [s]
Rohdaten
Züge
300
0
5 6 7 8 9 10 11 12
Austrittszeit TA [h]
page 51

53. Trains Identified
900
600
Reisezeit [s]
S4, S41 10.7.
11.7.
12.7.
300 ICE 78
ICE 604
ICE 778 ICE 976 ICE 602 ICE 372
ICE 278 ICE 76
ICE 600
0
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]
page 52

54. Enrichment of stationary Infrastructure by the
Mobile Network
A8-OW (stk_id=5)
TMC-LC
LUP +
Call
LATT-
LUP
Messung
SES/VIZ
AK AS AD AS AS
Stuttgart Leonberg Heimsheim Pforzheim
O page 53
N W

55. Probability Density of Cells along a Motorway
O->W
2/3
1/3
page 54

56. Do-iT Example for Incident Localisation
Floating Phones Stationary Sensors
Incident indication
time of day
location
page 55

57. Sources for Traffic Data in Germany in the Course of Time
stationary
detection
Cities traffic management
systems
centers
convergence FCD
diverse FCD species zone
motor Net-FCD
ways
limited installations
rollout net-FCD
high SES of DDG
ways
Regional TICs GATS-FCD
1990 2000 2010 2020
page 56

58. Sensors, Floating Cars and Floating Phones
Do-iT
Floating Phones
Police Loops Sensors Floating Cars
Data Collection
Traffic Modelling
Editorial Traffic Forecast
Team
Traffic information
page 57